Add Codex research skills and setup docs
Include Codex setup docs, installer updates, and switch the OpenCode web research agent default model to gpt-5.4.
This commit is contained in:
jilinchen
@@ -1,4 +1,4 @@
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# Deep Research Skill for Claude Code / OpenCode
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# Deep Research Skill for Claude Code / OpenCode / Codex
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[English](README.md) | [中文](README.zh.md)
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@@ -6,7 +6,7 @@
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> Inspired by [RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context](https://arxiv.org/abs/2511.18743)
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A structured research workflow skill for Claude Code, supporting two-phase research: outline generation (extensible) and deep investigation. Human-in-the-loop design ensures precise control at every stage.
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A structured research workflow skill for Claude Code, OpenCode, and Codex, supporting two-phase research: outline generation (extensible) and deep investigation. Human-in-the-loop design ensures precise control at every stage.
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@@ -19,6 +19,11 @@ A structured research workflow skill for Claude Code, supporting two-phase resea
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## Installation
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```bash
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git clone https://github.com/Weizhena/deep-research-skills.git
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cd deep-research-skills
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```
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### Claude Code
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```bash
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# English version
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@@ -35,7 +40,7 @@ cp -r agents/web-search-modules ~/.claude/agents/
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pip install pyyaml
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```
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### OpenCode (default: gpt-5.2)
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### OpenCode (default: gpt-5.4)
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```bash
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# Skills (same as Claude Code)
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cp -r skills/research-en/* ~/.claude/skills/ # or research-zh for Chinese
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@@ -48,11 +53,56 @@ cp -r agents/web-search-modules ~/.config/opencode/agents/
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pip install pyyaml
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```
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> **Important**: When OpenCode uses a Codex model, it currently does **not** have native `web search`; only `web fetch` is available. This matters for the deep research phase and should be called out explicitly.
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### Codex
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```bash
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# English version
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mkdir -p ~/.codex/skills ~/.codex/agents
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cp -r skills/research-codex-en/* ~/.codex/skills/
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# Chinese version
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mkdir -p ~/.codex/skills ~/.codex/agents
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cp -r skills/research-codex-zh/* ~/.codex/skills/
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# Required: Install web researcher agent and modules
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cp agents-codex/web-researcher.toml ~/.codex/agents/
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cp -r agents-codex/web-search-modules ~/.codex/agents/
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# Required: Install Python dependency
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pip install pyyaml
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```
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Add or update `~/.codex/config.toml` using either method below:
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**Option A: Automatic script**
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```bash
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cd deep-research-skills
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bash scripts/install-codex.sh
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```
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**Option B: Manual edit**
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```toml
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suppress_unstable_features_warning = true
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[features]
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multi_agent = true
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default_mode_request_user_input = true
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[agents.web_researcher]
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description = "Use this agent when you need to research information on the internet, particularly for debugging issues, finding solutions to technical problems, or gathering comprehensive information from multiple sources. This agent excels at finding relevant discussions. Use when you need creative search strategies, thorough investigation, or compilation of findings from multiple sources."
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config_file = "agents/web-researcher.toml"
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```
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## Commands
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> **Claude Code 2.1.0+**: Direct `/skill-name` trigger is now supported!
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>
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> **Older versions**: Use `run /skill-name` format instead.
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>
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> **Codex**: You can trigger these skills from `/skills` -> `List Skills`, or ask naturally, for example `Use the research skill to build an outline for AI Agent Demo 2025`.
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| Command (2.1.0+) | Description |
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|------------------|-------------|
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@@ -99,7 +149,7 @@ pip install pyyaml
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## Need Help?
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If you have questions, ask Claude Code to explain this project:
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If you have questions, ask Claude Code, OpenCode, or Codex to explain this project:
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```
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Help me understand this project: https://github.com/Weizhena/deep-research-skills
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```
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+59
-4
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# Deep Research Skill for Claude Code / OpenCode
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# Deep Research Skill for Claude Code / OpenCode / Codex
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[English](README.md) | [中文](README.zh.md)
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@@ -6,7 +6,7 @@
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> 灵感来源：[RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context](https://arxiv.org/abs/2511.18743)
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Claude Code 的结构化调研工作流技能，支持两阶段调研：outline生成（可扩展）和深度调查。人在回路设计确保每个阶段的精确控制。
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适用于 Claude Code、OpenCode 和 Codex 的结构化调研工作流技能，支持两阶段调研：outline生成（可扩展）和深度调查。人在回路设计确保每个阶段的精确控制。
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@@ -19,6 +19,11 @@ Claude Code 的结构化调研工作流技能，支持两阶段调研：outline
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## 安装
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```bash
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git clone https://github.com/Weizhena/deep-research-skills.git
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cd deep-research-skills
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```
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### Claude Code
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```bash
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# 中文版
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@@ -35,7 +40,7 @@ cp -r agents/web-search-modules ~/.claude/agents/
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pip install pyyaml
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```
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### OpenCode (默认: gpt-5.2)
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### OpenCode (默认: gpt-5.4)
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```bash
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# Skills (同 Claude Code)
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cp -r skills/research-zh/* ~/.claude/skills/ # 或 research-en 英文版
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@@ -48,11 +53,56 @@ cp -r agents/web-search-modules ~/.config/opencode/agents/
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pip install pyyaml
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```
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> **重要**：当 OpenCode 使用 Codex 模型时，目前**没有**原生 `web search`，只有 `web fetch`。这会直接影响深度调研阶段，README 里需要重点标注。
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### Codex
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```bash
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# 英文版
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mkdir -p ~/.codex/skills ~/.codex/agents
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cp -r skills/research-codex-en/* ~/.codex/skills/
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# 中文版
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mkdir -p ~/.codex/skills ~/.codex/agents
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cp -r skills/research-codex-zh/* ~/.codex/skills/
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# 必需：安装 web researcher agent 和模块
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cp agents-codex/web-researcher.toml ~/.codex/agents/
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cp -r agents-codex/web-search-modules ~/.codex/agents/
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# 必需：安装 Python 依赖
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pip install pyyaml
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```
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使用以下两种方式之一更新 `~/.codex/config.toml`：
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**方式 A：自动脚本**
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```bash
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cd deep-research-skills
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bash scripts/install-codex.sh
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```
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**方式 B：手动编辑**
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```toml
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suppress_unstable_features_warning = true
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[features]
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multi_agent = true
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default_mode_request_user_input = true
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[agents.web_researcher]
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description = "Use this agent when you need to research information on the internet, particularly for debugging issues, finding solutions to technical problems, or gathering comprehensive information from multiple sources. This agent excels at finding relevant discussions. Use when you need creative search strategies, thorough investigation, or compilation of findings from multiple sources."
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config_file = "agents/web-researcher.toml"
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```
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## 命令
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> **Claude Code 2.1.0+**：现已支持直接 `/skill-name` 触发！
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>
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> **旧版本**：请使用 `run /skill-name` 格式。
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>
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> **Codex**：可以通过 `/skills` -> `List Skills` 选择这些 skills，也可以用自然语言触发，例如 `Use the research skill to build an outline for AI Agent Demo 2025`。
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| 命令 (2.1.0+) | 描述 |
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|---------------|------|
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@@ -72,6 +122,11 @@ pip install pyyaml
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```
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💡 **会发生什么**：告诉它你要研究什么 → 它帮你列出调研清单
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对于 Codex，可以这样说：
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```
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Use the research skill to build an outline for AI Agent Demo 2025
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```
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**你会得到**：17个待调研的AI Agent清单（ChatGPT Agent、Claude Computer Use、Cursor等）+ 每个要收集哪些信息
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### （可选）不满意？继续添加
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## 遇到问题？
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如果过程中有什么不懂的，可以让Claude Code帮你理解这个项目：
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如果过程中有什么不懂的，可以让 Claude Code、OpenCode 或 Codex 帮你理解这个项目：
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```
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帮我理解这个项目: https://github.com/Weizhena/deep-research-skills
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```
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model = "gpt-5.4"
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model_reasoning_effort = "high"
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sandbox_mode = "read-only"
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web_search = "live"
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developer_instructions = """
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DONT USE ANY SKILLS.
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You are an elite internet researcher specializing in finding relevant information across diverse online sources. Your expertise lies in creative search strategies, thorough investigation, and comprehensive compilation of findings.
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**Core Capabilities:**
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- You excel at crafting multiple search query variations to uncover hidden gems of information
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- You systematically explore GitHub Issues, Reddit, Stack Overflow, Stack Exchange, technical forums, official documentation, blog posts, Dev.to, Medium, Hacker News, Discord, X/Twitter, Google Scholar, arXiv, Hugging Face Papers, bioRxiv, ResearchGate, Semantic Scholar, ACM Digital Library, IEEE Xplore, CSDN, Juejin, SegmentFault, Zhihu, Cnblogs, OSChina, V2EX, Tencent Cloud and Alibaba Cloud developer communities
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- You never settle for surface-level results - you dig deep to find the most relevant and helpful information
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- You are particularly skilled at debugging assistance, finding others who've encountered similar issues
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- You understand context and can identify patterns across disparate sources
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**Research Methodology:**
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0. **Get Current Date**: Run `date +%Y-%m-%d` to get today's date for time-sensitive searches.
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1. **Query Generation Phase**: When given a topic or problem, you will:
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- Generate 5-10 different search query variations to maximize coverage
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- Include technical terms, error messages, library names, and common misspellings
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- Think of how different people might describe the same issue (novice vs. expert terminology)
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- Consider searching for both the problem AND potential solutions
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- Use exact phrases in quotes for error messages
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- Include version numbers and environment details when relevant
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**Scenario-Specific Query Strategies (MANDATORY Module Loading)**:
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Before using live web search, you MUST inspect the relevant strategy module file(s) under `~/.codex/agents/web-search-modules/`. Based on the research type, read the corresponding file(s):
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- **Debugging/GitHub Issues** → Read `github-debug.md`
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Sources: GitHub Issues (open/closed)
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- **Best Practices/Comparative Research** → Read `general-web.md`
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Sources: Reddit, Official Docs, Blogs, Hacker News, Dev.to, Medium, Discord, X/Twitter
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- **Academic Paper Search** → Read `academic-papers.md`
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Sources: Google Scholar, arXiv, HuggingFace Papers, bioRxiv, ResearchGate, Semantic Scholar, ACM DL, IEEE Xplore
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- **Chinese Tech Community** → Read `chinese-tech.md`
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Sources: CSDN, Juejin, SegmentFault, Zhihu, Cnblogs, OSChina, V2EX, Tencent/Alibaba Cloud
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- **Technical Q&A** → Read `stackoverflow.md`
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Sources: Stack Overflow, Stack Exchange, technical forums
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DO NOT skip this step. Do not start web research before reading at least one relevant module.
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**Module Routing**: Each search may be routed to one or multiple modules:
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- **Single module**: When the task clearly belongs to one domain, load only that module
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- e.g. "search vllm memory leak issue" → Read `github-debug` only
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- **Multi-module**: When complex tasks require cross-domain coverage, load multiple modules
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- e.g. "transformers OOM problem" → Read `github-debug` + `stackoverflow` + `chinese-tech`
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- e.g. "attention mechanism papers and open-source implementations" → Read `academic-papers` + `github-debug`
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- The agent recommends modules based on task content; users can also specify explicitly
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2. **Source Prioritization**: Systematically search across sources defined in the routed modules above. Each module specifies its own prioritized source list. When multiple modules are routed, merge their source lists and deduplicate.
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3. **Information Gathering Standards**: You will:
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- Read beyond the first few results - valuable information is often buried
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- Look for patterns in solutions across different sources
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- Pay attention to dates to ensure relevance (note if solutions are outdated)
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- Note different approaches to the same problem and their trade-offs
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- Identify authoritative sources and experienced contributors
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- Check for updated solutions or superseded approaches
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- Verify if issues have been resolved in newer versions
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4. **Compilation Standards**: When presenting findings, you will:
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- **Caller's requested format takes priority** - satisfy their requirements first
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- Start with key findings summary (2-3 sentences)
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- Organize information by relevance and reliability
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- Provide direct links to all sources
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- Include relevant code snippets or configuration examples
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- Note any conflicting information and explain the differences
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- Highlight the most promising solutions or approaches
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- Include timestamps, version numbers, and environment details when relevant
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- Clearly mark experimental or unverified solutions
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**Quality Assurance:**
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- Verify information across multiple sources when possible
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- Clearly indicate when information is speculative or unverified
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- Date-stamp findings to indicate currency
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- Distinguish between official solutions and community workarounds
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- Note the credibility of sources (official docs vs. random blog post vs. maintainer comment)
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- Flag deprecated or outdated information
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- Highlight security implications if relevant
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- **Self-check before presenting**: Have I explored diverse sources? Any gaps? Is info current? Actionable next steps?
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- **If insufficient info found**: State what was searched, explain limitations, suggest alternatives or communities to ask
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**Standard Output Format**:
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```
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=== IF caller specified format ===
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[Caller's requested format/content]
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## Sources and References ← ALWAYS REQUIRED
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1. [Link with description]
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2. [Link with description]
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=== ELSE use standard format ===
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## Executive Summary
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[Key findings in 2-3 sentences - what you found and the recommended path forward]
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## Detailed Findings
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[Organized by relevance/approach, with clear headings]
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### [Approach/Solution 1]
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- Description
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- Source links
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- Code examples if applicable
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- Pros/Cons
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- Version/environment requirements
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### [Approach/Solution 2]
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[Same structure]
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## Sources and References ← ALWAYS REQUIRED
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1. [Link with description]
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2. [Link with description]
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## Recommendations
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[If applicable - your analysis of the best approach based on findings]
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## Additional Notes
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[Caveats, warnings, areas needing more research, or conflicting information]
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```
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Remember: You are not just a search engine - you are a research specialist who understands context, can identify patterns, and knows how to find information that others might miss. Your goal is to provide comprehensive, actionable intelligence that saves time and provides clarity. Every research task should leave the user better informed and with clear next steps.
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"""
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# Academic Papers Module
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> 从 web-search-agent.md 提取的学术论文搜索专用策略
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**触发场景**: 论文查找、学术研究、算法原理
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## 搜索源 (Academic Sources)
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- **Google Scholar** (scholar.google.com) - comprehensive academic search engine
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- **arXiv** (arxiv.org) - preprints in physics, math, CS, and related fields
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- **Hugging Face Papers** (huggingface.co/papers) - daily/monthly trending ML/AI papers with community upvotes
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- **bioRxiv** (biorxiv.org) - preprints in biology and life sciences
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- **ResearchGate** (researchgate.net) - academic social network with papers and author profiles
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- **Semantic Scholar** (semanticscholar.org) - AI-powered academic search
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- **ACM Digital Library** and **IEEE Xplore** - CS and engineering papers
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## 查询策略 (1.3 Academic Paper Search)
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- Use Google Scholar as primary source with advanced search operators
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- Search by author names, paper titles, DOI numbers, institutions, and publication years
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- Use quotation marks for exact titles and author name combinations
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- Include year ranges to find seminal works and recent publications
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- Look for related papers and citation patterns to identify seminal works
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- Search for preprints on arXiv, bioRxiv, and institutional repositories
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- Check author profiles and ResearchGate for publications and PDFs
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- Identify open-access versions and legal paper download sources
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- Track citation networks to understand research evolution
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- Note impact factors, h-index, and citation counts for relevance assessment
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- Search for conference proceedings, journals, and workshop papers
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- Identify funding agencies and research grants for context
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# Chinese Tech Module
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> 从 web-search-agent.md 提取的中文技术社区专用策略
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**触发场景**: 中文技术问题、国内框架、中文社区解决方案
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## 搜索源 (Chinese Technical Sites)
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- **CSDN** (csdn.net) - China's largest IT community with extensive technical articles and solutions
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- **Juejin** (juejin.cn) - high-quality Chinese developer community with modern tech focus
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- **SegmentFault** (segmentfault.com) - Chinese Q&A platform similar to Stack Overflow
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- **Zhihu** (zhihu.com) - Chinese knowledge-sharing platform with technical discussions
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- **Cnblogs** (cnblogs.com) - Chinese blogging platform with deep technical content
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- **OSChina** (oschina.net) - Chinese open source community and technical news
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- **V2EX** (v2ex.com) - Chinese developer community with active discussions
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- **Tencent Cloud** and **Alibaba Cloud** developer communities - enterprise-level solutions
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## 查询策略 (Bilingual Research)
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- **For bilingual research**: Generate queries in both English and Chinese (中文)
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- Use Chinese technical terms and common translations (e.g., "报错" for errors, "解决方案" for solutions)
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- Search Chinese sites with Chinese keywords for better results from Chinese developer communities
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@@ -0,0 +1,27 @@
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# General Web Module
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> 从 web-search-agent.md 提取的通用网页搜索策略
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**触发场景**: 通用信息、新闻、产品对比、最佳实践
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## 搜索源
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- **Reddit** (r/programming, r/webdev, r/javascript, and topic-specific subreddits) - real-world experiences
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- **Official documentation** and changelogs - authoritative information
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- **Blog posts** and tutorials - detailed explanations
|
||||
- **Hacker News** discussions - high-quality technical discourse
|
||||
- **Dev.to** (dev.to) - developer community with high-quality technical articles
|
||||
- **Medium** (medium.com) - technical blog platform with in-depth articles
|
||||
- **Discord** - official discussion channels for many open source projects
|
||||
- **X/Twitter** - technical announcements and discussions from developers and maintainers
|
||||
|
||||
## 查询策略 (1.2 Best Practices & Comparative Research)
|
||||
- Look for official recommendations first
|
||||
- Cross-reference with community consensus
|
||||
- Find examples from production codebases
|
||||
- Identify anti-patterns and common pitfalls
|
||||
- Note evolving best practices and deprecated approaches
|
||||
- Create structured comparisons with clear criteria
|
||||
- Find real-world usage examples and case studies
|
||||
- Look for performance benchmarks and user experiences
|
||||
- Identify trade-offs and decision factors
|
||||
- Consider scalability, maintenance, and learning curve
|
||||
@@ -0,0 +1,18 @@
|
||||
# GitHub Debug Module
|
||||
|
||||
> 从 web-search-agent.md 提取的 GitHub/Debug 专用策略
|
||||
|
||||
**触发场景**: 项目bug、error调试、issue查找、版本特定问题
|
||||
|
||||
## 搜索源
|
||||
- **GitHub Issues** (both open and closed) - excellent for known bugs and workarounds
|
||||
|
||||
## 查询策略 (1.1 Debugging Assistance)
|
||||
- Search for exact error messages in quotes
|
||||
- Look for issue templates that match the problem pattern
|
||||
- Find workarounds, not just explanations
|
||||
- Check if it's a known bug with existing patches or PRs
|
||||
- Look for similar issues even if not exact matches
|
||||
- Identify if the issue is version-specific
|
||||
- Search for both the library name + error and more general descriptions
|
||||
- Check closed issues for resolution patterns
|
||||
@@ -0,0 +1,9 @@
|
||||
# Stack Overflow Module
|
||||
|
||||
> 从 web-search-agent.md 提取的技术问答专用策略
|
||||
|
||||
**触发场景**: 编程问答、代码实现、API用法
|
||||
|
||||
## 搜索源
|
||||
- **Stack Overflow** and other Stack Exchange sites - technical Q&A
|
||||
- **Technical forums** and discussion boards - community wisdom
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
description: Use this agent when you need to research information on the internet, particularly for debugging issues, finding solutions to technical problems, or gathering comprehensive information from multiple sources. This agent excels at finding relevant discussions. Use when you need creative search strategies, thorough investigation of a topic, or compilation of findings from diverse sources.
|
||||
mode: subagent
|
||||
model: openai/gpt-5.2
|
||||
model: openai/gpt-5.4
|
||||
temperature: 0.4
|
||||
tools:
|
||||
read: true
|
||||
|
||||
Executable
+42
@@ -0,0 +1,42 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
|
||||
CONFIG_PATH="$CODEX_HOME/config.toml"
|
||||
|
||||
mkdir -p "$CODEX_HOME"
|
||||
|
||||
python3 - "$CONFIG_PATH" <<'PY'
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
config_path = Path(sys.argv[1])
|
||||
text = config_path.read_text(encoding="utf-8") if config_path.exists() else ""
|
||||
|
||||
blocks = {
|
||||
"features": """[features]
|
||||
multi_agent = true
|
||||
default_mode_request_user_input = true
|
||||
""",
|
||||
"agents.web_researcher": """[agents.web_researcher]
|
||||
description = "Use this agent when you need to research information on the internet, particularly for debugging issues, finding solutions to technical problems, or gathering comprehensive information from multiple sources. This agent excels at finding relevant discussions. Use when you need creative search strategies, thorough investigation, or compilation of findings from multiple sources."
|
||||
config_file = "agents/web-researcher.toml"
|
||||
""",
|
||||
}
|
||||
|
||||
for section, block in blocks.items():
|
||||
pattern = rf"(?ms)^\[{re.escape(section)}\]\n.*?(?=^\[|\Z)"
|
||||
if re.search(pattern, text):
|
||||
text = re.sub(pattern, block, text)
|
||||
else:
|
||||
text = text.rstrip() + ("\n\n" if text.strip() else "") + block
|
||||
|
||||
text = re.sub(r"(?m)^suppress_unstable_features_warning\s*=.*\n?", "", text).strip()
|
||||
text = 'suppress_unstable_features_warning = true\n\n' + text
|
||||
|
||||
config_path.write_text(text.rstrip() + "\n", encoding="utf-8")
|
||||
PY
|
||||
|
||||
echo "Updated Codex config at $CONFIG_PATH"
|
||||
@@ -0,0 +1,30 @@
|
||||
---
|
||||
name: research-add-fields
|
||||
description: Add field definitions to existing research outline.
|
||||
---
|
||||
|
||||
# Research Add Fields - Supplement Research Fields
|
||||
|
||||
## Trigger
|
||||
`/research-add-fields`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Auto-locate Fields File
|
||||
Find `*/fields.yaml` file in current working directory, auto-read existing fields definitions.
|
||||
|
||||
### Step 2: Get Supplement Source
|
||||
Ask user to choose:
|
||||
- **A. User direct input**: User provides field names and descriptions
|
||||
- **B. Web Search**: Launch agent to search common fields in this domain
|
||||
|
||||
### Step 3: Display and Confirm
|
||||
- Display suggested new fields list
|
||||
- User confirms which fields to add
|
||||
- User specifies field category and detail_level
|
||||
|
||||
### Step 4: Save Update
|
||||
Append confirmed fields to fields.yaml, save file.
|
||||
|
||||
## Output
|
||||
Updated `{topic}/fields.yaml` file (in-place modification, requires user confirmation)
|
||||
@@ -0,0 +1,28 @@
|
||||
---
|
||||
name: research-add-items
|
||||
description: Add items (research objects) to existing research outline.
|
||||
---
|
||||
|
||||
# Research Add Items - Supplement Research Objects
|
||||
|
||||
## Trigger
|
||||
`/research-add-items`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Auto-locate Outline
|
||||
Find `*/outline.yaml` file in current working directory, auto-read.
|
||||
|
||||
### Step 2: Get Supplement Sources in Parallel
|
||||
Simultaneously:
|
||||
- **A. Ask user**: What items to supplement? Any specific names?
|
||||
- **B. Ask if Web Search needed**: Launch agent to search for more items?
|
||||
|
||||
### Step 3: Merge and Update
|
||||
- Append new items to outline.yaml
|
||||
- Display to user for confirmation
|
||||
- Avoid duplicates
|
||||
- Save updated outline
|
||||
|
||||
## Output
|
||||
Updated `{topic}/outline.yaml` file (in-place modification)
|
||||
@@ -0,0 +1,98 @@
|
||||
---
|
||||
name: research-deep
|
||||
description: Read research outline, launch independent agent for each item for deep research. Disable task output.
|
||||
---
|
||||
|
||||
# Research Deep - Deep Research
|
||||
|
||||
## Trigger
|
||||
`/research-deep`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Auto-locate Outline
|
||||
Find `*/outline.yaml` file in current working directory, read items list, execution config (including items_per_agent).
|
||||
|
||||
### Step 2: Resume Check
|
||||
- Check completed JSON files in output_dir
|
||||
- Skip completed items
|
||||
|
||||
### Step 3: Batch Execution
|
||||
- Batch by batch_size (need user approval before next batch)
|
||||
- Each agent handles items_per_agent items
|
||||
- Launch web-search-agent (background parallel, disable task output)
|
||||
|
||||
**Parameter Retrieval**:
|
||||
- `{topic}`: topic field from outline.yaml
|
||||
- `{item_name}`: item's name field
|
||||
- `{item_related_info}`: item's complete yaml content (name + category + description etc.)
|
||||
- `{output_dir}`: execution.output_dir from outline.yaml (default: ./results)
|
||||
- `{fields_path}`: absolute path to {topic}/fields.yaml
|
||||
- `{output_path}`: absolute path to {output_dir}/{item_name_slug}.json (slugify item_name: replace spaces with _, remove special chars)
|
||||
|
||||
**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
|
||||
|
||||
**Prompt Template**:
|
||||
```python
|
||||
prompt = f"""## Task
|
||||
Research {item_related_info}, output structured JSON to {output_path}
|
||||
|
||||
## Field Definitions
|
||||
Read {fields_path} to get all field definitions
|
||||
|
||||
## Output Requirements
|
||||
1. Output JSON according to fields defined in fields.yaml
|
||||
2. Mark uncertain field values with [uncertain]
|
||||
3. Add uncertain array at the end of JSON, listing all uncertain field names
|
||||
4. All field values must be in English
|
||||
|
||||
## Output Path
|
||||
{output_path}
|
||||
|
||||
## Validation
|
||||
After completing JSON output, run validation script to ensure complete field coverage:
|
||||
python /home/weizhena/.codex/skills/research/validate_json.py -f {fields_path} -j {output_path}
|
||||
Task is complete only after validation passes.
|
||||
"""
|
||||
```
|
||||
|
||||
**One-shot Example** (assuming researching GitHub Copilot):
|
||||
```
|
||||
## Task
|
||||
Research name: GitHub Copilot
|
||||
category: International Product
|
||||
description: Developed by Microsoft/GitHub, first mainstream AI coding assistant, ~40% market share, output structured JSON to /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
|
||||
|
||||
## Field Definitions
|
||||
Read /home/weizhena/AIcoding/aicoding-history/fields.yaml to get all field definitions
|
||||
|
||||
## Output Requirements
|
||||
1. Output JSON according to fields defined in fields.yaml
|
||||
2. Mark uncertain field values with [uncertain]
|
||||
3. Add uncertain array at the end of JSON, listing all uncertain field names
|
||||
4. All field values must be in English
|
||||
|
||||
## Output Path
|
||||
/home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
|
||||
|
||||
## Validation
|
||||
After completing JSON output, run validation script to ensure complete field coverage:
|
||||
python /home/weizhena/.codex/skills/research/validate_json.py -f /home/weizhena/AIcoding/aicoding-history/fields.yaml -j /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
|
||||
Task is complete only after validation passes.
|
||||
```
|
||||
|
||||
### Step 4: Wait and Monitor
|
||||
- Wait for current batch to complete
|
||||
- Launch next batch
|
||||
- Display progress
|
||||
|
||||
### Step 5: Summary Report
|
||||
After all complete, output:
|
||||
- Completion count
|
||||
- Failed/uncertain marked items
|
||||
- Output directory
|
||||
|
||||
## Agent Config
|
||||
- Background execution: Yes
|
||||
- Task Output: Disabled (agent has explicit output file when complete)
|
||||
- Resume support: Yes
|
||||
@@ -0,0 +1,91 @@
|
||||
---
|
||||
name: research-report
|
||||
description: Summarize deep research results into markdown report, cover all fields, skip uncertain values.
|
||||
---
|
||||
|
||||
# Research Report - Summary Report
|
||||
|
||||
## Trigger
|
||||
`/research-report`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Locate Results Directory
|
||||
Find `*/outline.yaml` in current working directory, read topic and output_dir config.
|
||||
|
||||
### Step 2: Scan Optional Summary Fields
|
||||
Read all JSON results, extract fields suitable for TOC display (numeric, short metrics), e.g.:
|
||||
- github_stars
|
||||
- google_scholar_cites
|
||||
- swe_bench_score
|
||||
- user_scale
|
||||
- valuation
|
||||
- release_date
|
||||
|
||||
Use request_user_input to ask user:
|
||||
- Which fields to display in TOC besides item name?
|
||||
- Provide dynamic options list (based on actual fields in JSON)
|
||||
|
||||
### Step 3: Generate Python Conversion Script
|
||||
Generate `generate_report.py` in `{topic}/` directory, script requirements:
|
||||
- Read all JSON from output_dir
|
||||
- Read fields.yaml to get field structure
|
||||
- Cover all field values from each JSON
|
||||
- Skip fields with values containing [uncertain]
|
||||
- Skip fields listed in uncertain array
|
||||
- Generate markdown report format: Table of contents (with anchor links + user-selected summary fields) + Detailed content (by field category)
|
||||
- Save to `{topic}/report.md`
|
||||
|
||||
**TOC Format Requirements**:
|
||||
- Must include every item
|
||||
- Each item displays: number, name (anchor link), user-selected summary fields
|
||||
- Example: `1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%`
|
||||
|
||||
#### Script Technical Requirements (Must Follow)
|
||||
|
||||
**1. JSON Structure Compatibility**
|
||||
Support two JSON structures:
|
||||
- Flat structure: Fields directly at top level `{"name": "xxx", "release_date": "xxx"}`
|
||||
- Nested structure: Fields in category sub-dict `{"basic_info": {"name": "xxx"}, "technical_features": {...}}`
|
||||
|
||||
Field lookup order: Top level -> category mapping key -> Traverse all nested dicts
|
||||
|
||||
**2. Category Multi-language Mapping**
|
||||
fields.yaml category names and JSON keys can be any combination (CN-CN, CN-EN, EN-CN, EN-EN). Must establish bidirectional mapping:
|
||||
```python
|
||||
CATEGORY_MAPPING = {
|
||||
"Basic Info": ["basic_info", "Basic Info"],
|
||||
"Technical Features": ["technical_features", "technical_characteristics", "Technical Features"],
|
||||
"Performance Metrics": ["performance_metrics", "performance", "Performance Metrics"],
|
||||
"Milestone Significance": ["milestone_significance", "milestones", "Milestone Significance"],
|
||||
"Business Info": ["business_info", "commercial_info", "Business Info"],
|
||||
"Competition & Ecosystem": ["competition_ecosystem", "competition", "Competition & Ecosystem"],
|
||||
"History": ["history", "History"],
|
||||
"Market Positioning": ["market_positioning", "market", "Market Positioning"],
|
||||
}
|
||||
```
|
||||
|
||||
**3. Complex Value Formatting**
|
||||
- list of dicts (e.g., key_events, funding_history): Format each dict as one line, separate kv with ` | `
|
||||
- Normal list: Short lists joined with comma, long lists displayed with line breaks
|
||||
- Nested dict: Recursive formatting, display with semicolon or line breaks
|
||||
- Long text strings (over 100 chars): Add line breaks `<br>` or use blockquote format for readability
|
||||
|
||||
**4. Extra Fields Collection**
|
||||
Collect fields that exist in JSON but not defined in fields.yaml, put in "Other Info" category. Note to filter:
|
||||
- Internal fields: `_source_file`, `uncertain`
|
||||
- Nested structure top-level keys: `basic_info`, `technical_features` etc.
|
||||
- `uncertain` array: Display each field name on separate line, don't compress into one line
|
||||
|
||||
**5. Uncertain Value Skipping**
|
||||
Skip conditions:
|
||||
- Field value contains `[uncertain]` string
|
||||
- Field name is in `uncertain` array
|
||||
- Field value is None or empty string
|
||||
|
||||
### Step 4: Execute Script
|
||||
Run `python {topic}/generate_report.py`
|
||||
|
||||
## Output
|
||||
- `{topic}/generate_report.py` - Conversion script
|
||||
- `{topic}/report.md` - Summary report
|
||||
@@ -0,0 +1,143 @@
|
||||
---
|
||||
name: research
|
||||
description: Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.
|
||||
---
|
||||
|
||||
# Research Skill - Preliminary Research
|
||||
|
||||
## Trigger
|
||||
`/research <topic>`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Generate Initial Framework from Model Knowledge
|
||||
Based on topic, use model's existing knowledge to generate:
|
||||
- Main research objects/items list in this domain
|
||||
- Suggested research field framework
|
||||
|
||||
Output {step1_output}, use request_user_input to confirm:
|
||||
- Need to add/remove items?
|
||||
- Does field framework meet requirements?
|
||||
|
||||
### Step 2: Web Search Supplement
|
||||
Use request_user_input to ask for time range (e.g., last 6 months, since 2024, unlimited).
|
||||
|
||||
**Parameter Retrieval**:
|
||||
- `{topic}`: User input research topic
|
||||
- `{YYYY-MM-DD}`: Current date
|
||||
- `{step1_output}`: Complete output from Step 1
|
||||
- `{time_range}`: User specified time range
|
||||
|
||||
**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
|
||||
|
||||
Launch 1 web-search-agent (background), **Prompt Template**:
|
||||
```python
|
||||
prompt = f"""## Task
|
||||
Research topic: {topic}
|
||||
Current date: {YYYY-MM-DD}
|
||||
|
||||
Based on the following initial framework, supplement latest items and recommended research fields.
|
||||
|
||||
## Existing Framework
|
||||
{step1_output}
|
||||
|
||||
## Goals
|
||||
1. Verify if existing items are missing important objects
|
||||
2. Supplement items based on missing objects
|
||||
3. Continue searching for {topic} related items within {time_range} and supplement
|
||||
4. Supplement new fields
|
||||
|
||||
## Output Requirements
|
||||
Return structured results directly (do not write files):
|
||||
|
||||
### Supplementary Items
|
||||
- item_name: Brief explanation (why it should be added)
|
||||
...
|
||||
|
||||
### Recommended Supplementary Fields
|
||||
- field_name: Field description (why this dimension is needed)
|
||||
...
|
||||
|
||||
### Sources
|
||||
- [Source1](url1)
|
||||
- [Source2](url2)
|
||||
"""
|
||||
```
|
||||
|
||||
**One-shot Example** (assuming researching AI Coding History):
|
||||
```
|
||||
## Task
|
||||
Research topic: AI Coding History
|
||||
Current date: 2025-12-30
|
||||
|
||||
Based on the following initial framework, supplement latest items and recommended research fields.
|
||||
|
||||
## Existing Framework
|
||||
### Items List
|
||||
1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
|
||||
2. Cursor: AI-first IDE, based on VSCode
|
||||
...
|
||||
|
||||
### Field Framework
|
||||
- Basic Info: name, release_date, company
|
||||
- Technical Features: underlying_model, context_window
|
||||
...
|
||||
|
||||
## Goals
|
||||
1. Verify if existing items are missing important objects
|
||||
2. Supplement items based on missing objects
|
||||
3. Continue searching for AI Coding History related items within since 2024 and supplement
|
||||
4. Supplement new fields
|
||||
|
||||
## Output Requirements
|
||||
Return structured results directly (do not write files):
|
||||
|
||||
### Supplementary Items
|
||||
- item_name: Brief explanation (why it should be added)
|
||||
...
|
||||
|
||||
### Recommended Supplementary Fields
|
||||
- field_name: Field description (why this dimension is needed)
|
||||
...
|
||||
|
||||
### Sources
|
||||
- [Source1](url1)
|
||||
- [Source2](url2)
|
||||
```
|
||||
|
||||
### Step 3: Ask User for Existing Fields
|
||||
Use request_user_input to ask if user has existing field definition file, if so read and merge.
|
||||
|
||||
### Step 4: Generate Outline (Separate Files)
|
||||
Merge {step1_output}, {step2_output} and user's existing fields, generate two files:
|
||||
|
||||
**outline.yaml** (items + config):
|
||||
- topic: Research topic
|
||||
- items: Research objects list
|
||||
- execution:
|
||||
- batch_size: Number of parallel agents (confirm with request_user_input)
|
||||
- items_per_agent: Items per agent (confirm with request_user_input)
|
||||
- output_dir: Results output directory (default: ./results)
|
||||
|
||||
**fields.yaml** (field definitions):
|
||||
- Field categories and definitions
|
||||
- Each field's name, description, detail_level
|
||||
- detail_level hierarchy: brief -> moderate -> detailed
|
||||
- uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)
|
||||
|
||||
### Step 5: Output and Confirm
|
||||
- Create directory: `./{topic_slug}/`
|
||||
- Save: `outline.yaml` and `fields.yaml`
|
||||
- Show to user for confirmation
|
||||
|
||||
## Output Path
|
||||
```
|
||||
{current_working_directory}/{topic_slug}/
|
||||
├── outline.yaml # items list + execution config
|
||||
└── fields.yaml # field definitions
|
||||
```
|
||||
|
||||
## Follow-up Commands
|
||||
- `/research-add-items` - Supplement items
|
||||
- `/research-add-fields` - Supplement fields
|
||||
- `/research-deep` - Start deep research
|
||||
@@ -0,0 +1,160 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import json
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
|
||||
CATEGORY_MAPPING = {
|
||||
"basic_info": ["basic_info", "Basic Info"],
|
||||
"technical_features": ["technical_features", "technical_characteristics", "Technical Features"],
|
||||
"performance_metrics": ["performance_metrics", "performance", "Performance Metrics"],
|
||||
"milestone_significance": ["milestone_significance", "milestones", "Milestone Significance"],
|
||||
"business_info": ["business_info", "commercial_info", "Business Info"],
|
||||
"competition_ecosystem": ["competition_ecosystem", "competition", "Competition Ecosystem"],
|
||||
"history": ["history", "History"],
|
||||
"market_positioning": ["market_positioning", "market", "Market Positioning"],
|
||||
}
|
||||
|
||||
_SKIP_KEYS = {"_source_file", "uncertain"}
|
||||
|
||||
|
||||
def load_fields_yaml(fields_path):
|
||||
with fields_path.open(encoding="utf-8") as f:
|
||||
data = yaml.safe_load(f)
|
||||
items = [
|
||||
(field["name"], category["category"], field.get("required", False))
|
||||
for category in data.get("field_categories", [])
|
||||
for field in category.get("fields", [])
|
||||
]
|
||||
all_fields = {name for name, _, _ in items}
|
||||
required_fields = {name for name, _, required in items if required}
|
||||
field_categories = {name: category for name, category, _ in items}
|
||||
return all_fields, required_fields, field_categories
|
||||
|
||||
|
||||
def extract_json_fields(data, category_mapping=None):
|
||||
category_mapping = CATEGORY_MAPPING if category_mapping is None else category_mapping
|
||||
nested_keys = {k for keys in category_mapping.values() for k in keys}
|
||||
fields = set()
|
||||
stack = [(data, True)]
|
||||
while stack:
|
||||
obj, is_category_level = stack.pop()
|
||||
if isinstance(obj, dict):
|
||||
for k, v in obj.items():
|
||||
if k in _SKIP_KEYS:
|
||||
continue
|
||||
if is_category_level and k in nested_keys:
|
||||
if isinstance(v, dict):
|
||||
stack.append((v, True))
|
||||
continue
|
||||
fields.add(k)
|
||||
elif isinstance(obj, list):
|
||||
stack.extend((item, is_category_level) for item in obj if isinstance(item, dict))
|
||||
return fields
|
||||
|
||||
|
||||
def validate_json(json_path, all_fields, required_fields, field_categories):
|
||||
with json_path.open(encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
json_fields = extract_json_fields(data)
|
||||
covered = all_fields & json_fields
|
||||
missing = all_fields - json_fields
|
||||
extra = json_fields - all_fields
|
||||
missing_required = missing & required_fields
|
||||
missing_by_category = defaultdict(list)
|
||||
for field in missing:
|
||||
missing_by_category[field_categories.get(field, "Unknown")].append(field)
|
||||
return {
|
||||
"file": json_path.name,
|
||||
"total_defined": len(all_fields),
|
||||
"covered": len(covered),
|
||||
"missing": len(missing),
|
||||
"extra": len(extra),
|
||||
"coverage_rate": len(covered) / len(all_fields) * 100 if all_fields else 100,
|
||||
"missing_required": sorted(missing_required),
|
||||
"missing_optional": sorted(missing - required_fields),
|
||||
"missing_by_category": {k: sorted(v) for k, v in missing_by_category.items()},
|
||||
"extra_fields": sorted(extra),
|
||||
"valid": len(missing_required) == 0,
|
||||
}
|
||||
|
||||
|
||||
def print_result(result, verbose=True):
|
||||
status = "PASS" if result["valid"] else "FAIL"
|
||||
line = "=" * 60
|
||||
print(f"\n{line}")
|
||||
print(f"[{status}] {result['file']}")
|
||||
print(line)
|
||||
print(f"Coverage: {result['coverage_rate']:.1f}% ({result['covered']}/{result['total_defined']})")
|
||||
if result["missing_required"]:
|
||||
print(f"\n[ERROR] Missing required fields ({len(result['missing_required'])}):")
|
||||
print("\n".join(f" - {f}" for f in result["missing_required"]))
|
||||
if verbose and result["missing_optional"]:
|
||||
missing_required = set(result["missing_required"])
|
||||
print(f"\n[WARN] Missing optional fields ({len(result['missing_optional'])}):")
|
||||
for cat in sorted(result["missing_by_category"]):
|
||||
optional = [f for f in result["missing_by_category"][cat] if f not in missing_required]
|
||||
if optional:
|
||||
print(f" [{cat}]: {', '.join(optional)}")
|
||||
if verbose and result["extra_fields"]:
|
||||
extra = result["extra_fields"]
|
||||
print(f"\n[INFO] Extra fields ({len(extra)}):")
|
||||
print(f" {', '.join(extra[:10])}")
|
||||
if len(extra) > 10:
|
||||
print(f" ... and {len(extra) - 10} more")
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description="Validate whether JSON files cover all fields defined in fields.yaml")
|
||||
parser.add_argument("--fields", "-f", type=str, help="Path to fields.yaml", default="fields.yaml")
|
||||
parser.add_argument("--json", "-j", type=str, nargs="*", help="JSON file paths to validate")
|
||||
parser.add_argument("--dir", "-d", type=str, help="Directory containing JSON files", default="results")
|
||||
parser.add_argument("--quiet", "-q", action="store_true", help="Show summary only")
|
||||
args = parser.parse_args()
|
||||
fields_path = Path(args.fields)
|
||||
if not fields_path.exists():
|
||||
for p in (Path.cwd() / "fields.yaml", Path.cwd().parent / "fields.yaml"):
|
||||
if p.exists():
|
||||
fields_path = p
|
||||
break
|
||||
if not fields_path.exists():
|
||||
print(f"[ERROR] fields.yaml not found: {fields_path}")
|
||||
sys.exit(1)
|
||||
print(f"Field definition file: {fields_path}")
|
||||
all_fields, required_fields, field_categories = load_fields_yaml(fields_path)
|
||||
print(f"Total fields: {len(all_fields)} (required: {len(required_fields)}, optional: {len(all_fields) - len(required_fields)})")
|
||||
json_files = (
|
||||
[Path(p) for p in args.json]
|
||||
if args.json
|
||||
else sorted(Path(args.dir).glob("*.json")) if Path(args.dir).exists() else []
|
||||
)
|
||||
if not json_files:
|
||||
print("[WARN] No JSON files found")
|
||||
sys.exit(0)
|
||||
results = []
|
||||
for json_path in json_files:
|
||||
if not json_path.exists():
|
||||
print(f"[WARN] File not found: {json_path}")
|
||||
continue
|
||||
result = validate_json(json_path, all_fields, required_fields, field_categories)
|
||||
results.append(result)
|
||||
print_result(result, verbose=not args.quiet)
|
||||
line = "=" * 60
|
||||
print(f"\n{line}")
|
||||
print("Summary")
|
||||
print(line)
|
||||
passed = sum(1 for r in results if r["valid"])
|
||||
avg_coverage = sum(r["coverage_rate"] for r in results) / len(results) if results else 0
|
||||
print(f"Validation passed: {passed}/{len(results)}")
|
||||
print(f"Average coverage: {avg_coverage:.1f}%")
|
||||
if passed < len(results):
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,30 @@
|
||||
---
|
||||
name: research-add-fields
|
||||
description: Add field definitions to existing research outline.
|
||||
---
|
||||
|
||||
# Research Add Fields - Supplement Research Fields
|
||||
|
||||
## Trigger
|
||||
`/research-add-fields`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Auto-locate Fields File
|
||||
Find `*/fields.yaml` file in current working directory, auto-read existing fields definitions.
|
||||
|
||||
### Step 2: Get Supplement Source
|
||||
Ask user to choose:
|
||||
- **A. User direct input**: User provides field names and descriptions
|
||||
- **B. Web Search**: Launch agent to search common fields in this domain
|
||||
|
||||
### Step 3: Display and Confirm
|
||||
- Display suggested new fields list
|
||||
- User confirms which fields to add
|
||||
- User specifies field category and detail_level
|
||||
|
||||
### Step 4: Save Update
|
||||
Append confirmed fields to fields.yaml, save file.
|
||||
|
||||
## Output
|
||||
Updated `{topic}/fields.yaml` file (in-place modification, requires user confirmation)
|
||||
@@ -0,0 +1,28 @@
|
||||
---
|
||||
name: research-add-items
|
||||
description: Add items (research objects) to existing research outline.
|
||||
---
|
||||
|
||||
# Research Add Items - Supplement Research Objects
|
||||
|
||||
## Trigger
|
||||
`/research-add-items`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Auto-locate Outline
|
||||
Find `*/outline.yaml` file in current working directory, auto-read.
|
||||
|
||||
### Step 2: Get Supplement Sources in Parallel
|
||||
Simultaneously:
|
||||
- **A. Ask user**: What items to supplement? Any specific names?
|
||||
- **B. Ask if Web Search needed**: Launch agent to search for more items?
|
||||
|
||||
### Step 3: Merge and Update
|
||||
- Append new items to outline.yaml
|
||||
- Display to user for confirmation
|
||||
- Avoid duplicates
|
||||
- Save updated outline
|
||||
|
||||
## Output
|
||||
Updated `{topic}/outline.yaml` file (in-place modification)
|
||||
@@ -0,0 +1,98 @@
|
||||
---
|
||||
name: research-deep
|
||||
description: Read research outline, launch independent agent for each item for deep research. Disable task output.
|
||||
---
|
||||
|
||||
# Research Deep - Deep Research
|
||||
|
||||
## Trigger
|
||||
`/research-deep`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Auto-locate Outline
|
||||
Find `*/outline.yaml` file in current working directory, read items list, execution config (including items_per_agent).
|
||||
|
||||
### Step 2: Resume Check
|
||||
- Check completed JSON files in output_dir
|
||||
- Skip completed items
|
||||
|
||||
### Step 3: Batch Execution
|
||||
- Batch by batch_size (need user approval before next batch)
|
||||
- Each agent handles items_per_agent items
|
||||
- Launch web-search-agent (background parallel, disable task output)
|
||||
|
||||
**Parameter Retrieval**:
|
||||
- `{topic}`: topic field from outline.yaml
|
||||
- `{item_name}`: item's name field
|
||||
- `{item_related_info}`: item's complete yaml content (name + category + description etc.)
|
||||
- `{output_dir}`: execution.output_dir from outline.yaml (default: ./results)
|
||||
- `{fields_path}`: absolute path to {topic}/fields.yaml
|
||||
- `{output_path}`: absolute path to {output_dir}/{item_name_slug}.json (slugify item_name: replace spaces with _, remove special chars)
|
||||
|
||||
**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
|
||||
|
||||
**Prompt Template**:
|
||||
```python
|
||||
prompt = f"""## Task
|
||||
Research {item_related_info}, output structured JSON to {output_path}
|
||||
|
||||
## Field Definitions
|
||||
Read {fields_path} to get all field definitions
|
||||
|
||||
## Output Requirements
|
||||
1. Output JSON according to fields defined in fields.yaml
|
||||
2. Mark uncertain field values with [uncertain]
|
||||
3. Add uncertain array at the end of JSON, listing all uncertain field names
|
||||
4. All field values must be in English
|
||||
|
||||
## Output Path
|
||||
{output_path}
|
||||
|
||||
## Validation
|
||||
After completing JSON output, run validation script to ensure complete field coverage:
|
||||
python /home/weizhena/.codex/skills/research/validate_json.py -f {fields_path} -j {output_path}
|
||||
Task is complete only after validation passes.
|
||||
"""
|
||||
```
|
||||
|
||||
**One-shot Example** (assuming researching GitHub Copilot):
|
||||
```
|
||||
## Task
|
||||
Research name: GitHub Copilot
|
||||
category: International Product
|
||||
description: Developed by Microsoft/GitHub, first mainstream AI coding assistant, ~40% market share, output structured JSON to /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
|
||||
|
||||
## Field Definitions
|
||||
Read /home/weizhena/AIcoding/aicoding-history/fields.yaml to get all field definitions
|
||||
|
||||
## Output Requirements
|
||||
1. Output JSON according to fields defined in fields.yaml
|
||||
2. Mark uncertain field values with [uncertain]
|
||||
3. Add uncertain array at the end of JSON, listing all uncertain field names
|
||||
4. All field values must be in English
|
||||
|
||||
## Output Path
|
||||
/home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
|
||||
|
||||
## Validation
|
||||
After completing JSON output, run validation script to ensure complete field coverage:
|
||||
python /home/weizhena/.codex/skills/research/validate_json.py -f /home/weizhena/AIcoding/aicoding-history/fields.yaml -j /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
|
||||
Task is complete only after validation passes.
|
||||
```
|
||||
|
||||
### Step 4: Wait and Monitor
|
||||
- Wait for current batch to complete
|
||||
- Launch next batch
|
||||
- Display progress
|
||||
|
||||
### Step 5: Summary Report
|
||||
After all complete, output:
|
||||
- Completion count
|
||||
- Failed/uncertain marked items
|
||||
- Output directory
|
||||
|
||||
## Agent Config
|
||||
- Background execution: Yes
|
||||
- Task Output: Disabled (agent has explicit output file when complete)
|
||||
- Resume support: Yes
|
||||
@@ -0,0 +1,91 @@
|
||||
---
|
||||
name: research-report
|
||||
description: Summarize deep research results into markdown report, cover all fields, skip uncertain values.
|
||||
---
|
||||
|
||||
# Research Report - Summary Report
|
||||
|
||||
## Trigger
|
||||
`/research-report`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Locate Results Directory
|
||||
Find `*/outline.yaml` in current working directory, read topic and output_dir config.
|
||||
|
||||
### Step 2: Scan Optional Summary Fields
|
||||
Read all JSON results, extract fields suitable for TOC display (numeric, short metrics), e.g.:
|
||||
- github_stars
|
||||
- google_scholar_cites
|
||||
- swe_bench_score
|
||||
- user_scale
|
||||
- valuation
|
||||
- release_date
|
||||
|
||||
Use request_user_input to ask user:
|
||||
- Which fields to display in TOC besides item name?
|
||||
- Provide dynamic options list (based on actual fields in JSON)
|
||||
|
||||
### Step 3: Generate Python Conversion Script
|
||||
Generate `generate_report.py` in `{topic}/` directory, script requirements:
|
||||
- Read all JSON from output_dir
|
||||
- Read fields.yaml to get field structure
|
||||
- Cover all field values from each JSON
|
||||
- Skip fields with values containing [uncertain]
|
||||
- Skip fields listed in uncertain array
|
||||
- Generate markdown report format: Table of contents (with anchor links + user-selected summary fields) + Detailed content (by field category)
|
||||
- Save to `{topic}/report.md`
|
||||
|
||||
**TOC Format Requirements**:
|
||||
- Must include every item
|
||||
- Each item displays: number, name (anchor link), user-selected summary fields
|
||||
- Example: `1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%`
|
||||
|
||||
#### Script Technical Requirements (Must Follow)
|
||||
|
||||
**1. JSON Structure Compatibility**
|
||||
Support two JSON structures:
|
||||
- Flat structure: Fields directly at top level `{"name": "xxx", "release_date": "xxx"}`
|
||||
- Nested structure: Fields in category sub-dict `{"basic_info": {"name": "xxx"}, "technical_features": {...}}`
|
||||
|
||||
Field lookup order: Top level -> category mapping key -> Traverse all nested dicts
|
||||
|
||||
**2. Category Multi-language Mapping**
|
||||
fields.yaml category names and JSON keys can be any combination (CN-CN, CN-EN, EN-CN, EN-EN). Must establish bidirectional mapping:
|
||||
```python
|
||||
CATEGORY_MAPPING = {
|
||||
"Basic Info": ["basic_info", "Basic Info"],
|
||||
"Technical Features": ["technical_features", "technical_characteristics", "Technical Features"],
|
||||
"Performance Metrics": ["performance_metrics", "performance", "Performance Metrics"],
|
||||
"Milestone Significance": ["milestone_significance", "milestones", "Milestone Significance"],
|
||||
"Business Info": ["business_info", "commercial_info", "Business Info"],
|
||||
"Competition & Ecosystem": ["competition_ecosystem", "competition", "Competition & Ecosystem"],
|
||||
"History": ["history", "History"],
|
||||
"Market Positioning": ["market_positioning", "market", "Market Positioning"],
|
||||
}
|
||||
```
|
||||
|
||||
**3. Complex Value Formatting**
|
||||
- list of dicts (e.g., key_events, funding_history): Format each dict as one line, separate kv with ` | `
|
||||
- Normal list: Short lists joined with comma, long lists displayed with line breaks
|
||||
- Nested dict: Recursive formatting, display with semicolon or line breaks
|
||||
- Long text strings (over 100 chars): Add line breaks `<br>` or use blockquote format for readability
|
||||
|
||||
**4. Extra Fields Collection**
|
||||
Collect fields that exist in JSON but not defined in fields.yaml, put in "Other Info" category. Note to filter:
|
||||
- Internal fields: `_source_file`, `uncertain`
|
||||
- Nested structure top-level keys: `basic_info`, `technical_features` etc.
|
||||
- `uncertain` array: Display each field name on separate line, don't compress into one line
|
||||
|
||||
**5. Uncertain Value Skipping**
|
||||
Skip conditions:
|
||||
- Field value contains `[uncertain]` string
|
||||
- Field name is in `uncertain` array
|
||||
- Field value is None or empty string
|
||||
|
||||
### Step 4: Execute Script
|
||||
Run `python {topic}/generate_report.py`
|
||||
|
||||
## Output
|
||||
- `{topic}/generate_report.py` - Conversion script
|
||||
- `{topic}/report.md` - Summary report
|
||||
@@ -0,0 +1,143 @@
|
||||
---
|
||||
name: research
|
||||
description: Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.
|
||||
---
|
||||
|
||||
# Research Skill - Preliminary Research
|
||||
|
||||
## Trigger
|
||||
`/research <topic>`
|
||||
|
||||
## Workflow
|
||||
|
||||
### Step 1: Generate Initial Framework from Model Knowledge
|
||||
Based on topic, use model's existing knowledge to generate:
|
||||
- Main research objects/items list in this domain
|
||||
- Suggested research field framework
|
||||
|
||||
Output {step1_output}, use request_user_input to confirm:
|
||||
- Need to add/remove items?
|
||||
- Does field framework meet requirements?
|
||||
|
||||
### Step 2: Web Search Supplement
|
||||
Use request_user_input to ask for time range (e.g., last 6 months, since 2024, unlimited).
|
||||
|
||||
**Parameter Retrieval**:
|
||||
- `{topic}`: User input research topic
|
||||
- `{YYYY-MM-DD}`: Current date
|
||||
- `{step1_output}`: Complete output from Step 1
|
||||
- `{time_range}`: User specified time range
|
||||
|
||||
**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
|
||||
|
||||
Launch 1 web-search-agent (background), **Prompt Template**:
|
||||
```python
|
||||
prompt = f"""## Task
|
||||
Research topic: {topic}
|
||||
Current date: {YYYY-MM-DD}
|
||||
|
||||
Based on the following initial framework, supplement latest items and recommended research fields.
|
||||
|
||||
## Existing Framework
|
||||
{step1_output}
|
||||
|
||||
## Goals
|
||||
1. Verify if existing items are missing important objects
|
||||
2. Supplement items based on missing objects
|
||||
3. Continue searching for {topic} related items within {time_range} and supplement
|
||||
4. Supplement new fields
|
||||
|
||||
## Output Requirements
|
||||
Return structured results directly (do not write files):
|
||||
|
||||
### Supplementary Items
|
||||
- item_name: Brief explanation (why it should be added)
|
||||
...
|
||||
|
||||
### Recommended Supplementary Fields
|
||||
- field_name: Field description (why this dimension is needed)
|
||||
...
|
||||
|
||||
### Sources
|
||||
- [Source1](url1)
|
||||
- [Source2](url2)
|
||||
"""
|
||||
```
|
||||
|
||||
**One-shot Example** (assuming researching AI Coding History):
|
||||
```
|
||||
## Task
|
||||
Research topic: AI Coding History
|
||||
Current date: 2025-12-30
|
||||
|
||||
Based on the following initial framework, supplement latest items and recommended research fields.
|
||||
|
||||
## Existing Framework
|
||||
### Items List
|
||||
1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
|
||||
2. Cursor: AI-first IDE, based on VSCode
|
||||
...
|
||||
|
||||
### Field Framework
|
||||
- Basic Info: name, release_date, company
|
||||
- Technical Features: underlying_model, context_window
|
||||
...
|
||||
|
||||
## Goals
|
||||
1. Verify if existing items are missing important objects
|
||||
2. Supplement items based on missing objects
|
||||
3. Continue searching for AI Coding History related items within since 2024 and supplement
|
||||
4. Supplement new fields
|
||||
|
||||
## Output Requirements
|
||||
Return structured results directly (do not write files):
|
||||
|
||||
### Supplementary Items
|
||||
- item_name: Brief explanation (why it should be added)
|
||||
...
|
||||
|
||||
### Recommended Supplementary Fields
|
||||
- field_name: Field description (why this dimension is needed)
|
||||
...
|
||||
|
||||
### Sources
|
||||
- [Source1](url1)
|
||||
- [Source2](url2)
|
||||
```
|
||||
|
||||
### Step 3: Ask User for Existing Fields
|
||||
Use request_user_input to ask if user has existing field definition file, if so read and merge.
|
||||
|
||||
### Step 4: Generate Outline (Separate Files)
|
||||
Merge {step1_output}, {step2_output} and user's existing fields, generate two files:
|
||||
|
||||
**outline.yaml** (items + config):
|
||||
- topic: Research topic
|
||||
- items: Research objects list
|
||||
- execution:
|
||||
- batch_size: Number of parallel agents (confirm with request_user_input)
|
||||
- items_per_agent: Items per agent (confirm with request_user_input)
|
||||
- output_dir: Results output directory (default: ./results)
|
||||
|
||||
**fields.yaml** (field definitions):
|
||||
- Field categories and definitions
|
||||
- Each field's name, description, detail_level
|
||||
- detail_level hierarchy: brief -> moderate -> detailed
|
||||
- uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)
|
||||
|
||||
### Step 5: Output and Confirm
|
||||
- Create directory: `./{topic_slug}/`
|
||||
- Save: `outline.yaml` and `fields.yaml`
|
||||
- Show to user for confirmation
|
||||
|
||||
## Output Path
|
||||
```
|
||||
{current_working_directory}/{topic_slug}/
|
||||
├── outline.yaml # items list + execution config
|
||||
└── fields.yaml # field definitions
|
||||
```
|
||||
|
||||
## Follow-up Commands
|
||||
- `/research-add-items` - Supplement items
|
||||
- `/research-add-fields` - Supplement fields
|
||||
- `/research-deep` - Start deep research
|
||||
@@ -0,0 +1,160 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import json
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
|
||||
CATEGORY_MAPPING = {
|
||||
"basic_info": ["basic_info", "Basic Info"],
|
||||
"technical_features": ["technical_features", "technical_characteristics", "Technical Features"],
|
||||
"performance_metrics": ["performance_metrics", "performance", "Performance Metrics"],
|
||||
"milestone_significance": ["milestone_significance", "milestones", "Milestone Significance"],
|
||||
"business_info": ["business_info", "commercial_info", "Business Info"],
|
||||
"competition_ecosystem": ["competition_ecosystem", "competition", "Competition Ecosystem"],
|
||||
"history": ["history", "History"],
|
||||
"market_positioning": ["market_positioning", "market", "Market Positioning"],
|
||||
}
|
||||
|
||||
_SKIP_KEYS = {"_source_file", "uncertain"}
|
||||
|
||||
|
||||
def load_fields_yaml(fields_path):
|
||||
with fields_path.open(encoding="utf-8") as f:
|
||||
data = yaml.safe_load(f)
|
||||
items = [
|
||||
(field["name"], category["category"], field.get("required", False))
|
||||
for category in data.get("field_categories", [])
|
||||
for field in category.get("fields", [])
|
||||
]
|
||||
all_fields = {name for name, _, _ in items}
|
||||
required_fields = {name for name, _, required in items if required}
|
||||
field_categories = {name: category for name, category, _ in items}
|
||||
return all_fields, required_fields, field_categories
|
||||
|
||||
|
||||
def extract_json_fields(data, category_mapping=None):
|
||||
category_mapping = CATEGORY_MAPPING if category_mapping is None else category_mapping
|
||||
nested_keys = {k for keys in category_mapping.values() for k in keys}
|
||||
fields = set()
|
||||
stack = [(data, True)]
|
||||
while stack:
|
||||
obj, is_category_level = stack.pop()
|
||||
if isinstance(obj, dict):
|
||||
for k, v in obj.items():
|
||||
if k in _SKIP_KEYS:
|
||||
continue
|
||||
if is_category_level and k in nested_keys:
|
||||
if isinstance(v, dict):
|
||||
stack.append((v, True))
|
||||
continue
|
||||
fields.add(k)
|
||||
elif isinstance(obj, list):
|
||||
stack.extend((item, is_category_level) for item in obj if isinstance(item, dict))
|
||||
return fields
|
||||
|
||||
|
||||
def validate_json(json_path, all_fields, required_fields, field_categories):
|
||||
with json_path.open(encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
json_fields = extract_json_fields(data)
|
||||
covered = all_fields & json_fields
|
||||
missing = all_fields - json_fields
|
||||
extra = json_fields - all_fields
|
||||
missing_required = missing & required_fields
|
||||
missing_by_category = defaultdict(list)
|
||||
for field in missing:
|
||||
missing_by_category[field_categories.get(field, "Unknown")].append(field)
|
||||
return {
|
||||
"file": json_path.name,
|
||||
"total_defined": len(all_fields),
|
||||
"covered": len(covered),
|
||||
"missing": len(missing),
|
||||
"extra": len(extra),
|
||||
"coverage_rate": len(covered) / len(all_fields) * 100 if all_fields else 100,
|
||||
"missing_required": sorted(missing_required),
|
||||
"missing_optional": sorted(missing - required_fields),
|
||||
"missing_by_category": {k: sorted(v) for k, v in missing_by_category.items()},
|
||||
"extra_fields": sorted(extra),
|
||||
"valid": len(missing_required) == 0,
|
||||
}
|
||||
|
||||
|
||||
def print_result(result, verbose=True):
|
||||
status = "PASS" if result["valid"] else "FAIL"
|
||||
line = "=" * 60
|
||||
print(f"\n{line}")
|
||||
print(f"[{status}] {result['file']}")
|
||||
print(line)
|
||||
print(f"Coverage: {result['coverage_rate']:.1f}% ({result['covered']}/{result['total_defined']})")
|
||||
if result["missing_required"]:
|
||||
print(f"\n[ERROR] Missing required fields ({len(result['missing_required'])}):")
|
||||
print("\n".join(f" - {f}" for f in result["missing_required"]))
|
||||
if verbose and result["missing_optional"]:
|
||||
missing_required = set(result["missing_required"])
|
||||
print(f"\n[WARN] Missing optional fields ({len(result['missing_optional'])}):")
|
||||
for cat in sorted(result["missing_by_category"]):
|
||||
optional = [f for f in result["missing_by_category"][cat] if f not in missing_required]
|
||||
if optional:
|
||||
print(f" [{cat}]: {', '.join(optional)}")
|
||||
if verbose and result["extra_fields"]:
|
||||
extra = result["extra_fields"]
|
||||
print(f"\n[INFO] Extra fields ({len(extra)}):")
|
||||
print(f" {', '.join(extra[:10])}")
|
||||
if len(extra) > 10:
|
||||
print(f" ... and {len(extra) - 10} more")
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description="Validate whether JSON files cover all fields defined in fields.yaml")
|
||||
parser.add_argument("--fields", "-f", type=str, help="Path to fields.yaml", default="fields.yaml")
|
||||
parser.add_argument("--json", "-j", type=str, nargs="*", help="JSON file paths to validate")
|
||||
parser.add_argument("--dir", "-d", type=str, help="Directory containing JSON files", default="results")
|
||||
parser.add_argument("--quiet", "-q", action="store_true", help="Show summary only")
|
||||
args = parser.parse_args()
|
||||
fields_path = Path(args.fields)
|
||||
if not fields_path.exists():
|
||||
for p in (Path.cwd() / "fields.yaml", Path.cwd().parent / "fields.yaml"):
|
||||
if p.exists():
|
||||
fields_path = p
|
||||
break
|
||||
if not fields_path.exists():
|
||||
print(f"[ERROR] fields.yaml not found: {fields_path}")
|
||||
sys.exit(1)
|
||||
print(f"Field definition file: {fields_path}")
|
||||
all_fields, required_fields, field_categories = load_fields_yaml(fields_path)
|
||||
print(f"Total fields: {len(all_fields)} (required: {len(required_fields)}, optional: {len(all_fields) - len(required_fields)})")
|
||||
json_files = (
|
||||
[Path(p) for p in args.json]
|
||||
if args.json
|
||||
else sorted(Path(args.dir).glob("*.json")) if Path(args.dir).exists() else []
|
||||
)
|
||||
if not json_files:
|
||||
print("[WARN] No JSON files found")
|
||||
sys.exit(0)
|
||||
results = []
|
||||
for json_path in json_files:
|
||||
if not json_path.exists():
|
||||
print(f"[WARN] File not found: {json_path}")
|
||||
continue
|
||||
result = validate_json(json_path, all_fields, required_fields, field_categories)
|
||||
results.append(result)
|
||||
print_result(result, verbose=not args.quiet)
|
||||
line = "=" * 60
|
||||
print(f"\n{line}")
|
||||
print("Summary")
|
||||
print(line)
|
||||
passed = sum(1 for r in results if r["valid"])
|
||||
avg_coverage = sum(r["coverage_rate"] for r in results) / len(results) if results else 0
|
||||
print(f"Validation passed: {passed}/{len(results)}")
|
||||
print(f"Average coverage: {avg_coverage:.1f}%")
|
||||
if passed < len(results):
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Executable
+61
@@ -0,0 +1,61 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
|
||||
TMP_HOME="$(mktemp -d)"
|
||||
trap 'rm -rf "$TMP_HOME"' EXIT
|
||||
|
||||
assert_contains() {
|
||||
local path="$1"
|
||||
local expected="$2"
|
||||
if ! rg -F "$expected" "$path" >/dev/null 2>&1; then
|
||||
echo "expected '$expected' in $path" >&2
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
HOME="$TMP_HOME" bash "$ROOT_DIR/scripts/install-codex.sh"
|
||||
HOME="$TMP_HOME" bash "$ROOT_DIR/scripts/install-codex.sh"
|
||||
|
||||
if [[ ! -f "$TMP_HOME/.codex/config.toml" ]]; then
|
||||
echo "expected config.toml to exist" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [[ -d "$TMP_HOME/.codex/skills" ]]; then
|
||||
echo "did not expect skills directory to be created" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [[ -d "$TMP_HOME/.codex/agents" ]]; then
|
||||
echo "did not expect agents directory to be created" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
assert_contains "$TMP_HOME/.codex/config.toml" "multi_agent = true"
|
||||
assert_contains "$TMP_HOME/.codex/config.toml" "default_mode_request_user_input = true"
|
||||
assert_contains "$TMP_HOME/.codex/config.toml" "suppress_unstable_features_warning = true"
|
||||
assert_contains "$TMP_HOME/.codex/config.toml" "[agents.web_researcher]"
|
||||
assert_contains "$TMP_HOME/.codex/config.toml" 'config_file = "agents/web-researcher.toml"'
|
||||
|
||||
if [[ "$(rg -c '^\[features\]$' "$TMP_HOME/.codex/config.toml")" != "1" ]]; then
|
||||
echo "expected exactly one [features] section" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [[ "$(rg -c '^\[agents\.web_researcher\]$' "$TMP_HOME/.codex/config.toml")" != "1" ]]; then
|
||||
echo "expected exactly one [agents.web_researcher] section" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if awk '
|
||||
/^\[features\]$/ { in_features=1; next }
|
||||
/^\[/ { in_features=0 }
|
||||
in_features && /suppress_unstable_features_warning/
|
||||
' "$TMP_HOME/.codex/config.toml" | grep -q .; then
|
||||
echo "did not expect suppress_unstable_features_warning inside [features]" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "install-codex test passed"
|
||||
Reference in New Issue
Block a user