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
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---
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name: research-add-fields
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description: Add field definitions to existing research outline.
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---
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# Research Add Fields - Supplement Research Fields
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## Trigger
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`/research-add-fields`
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## Workflow
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### Step 1: Auto-locate Fields File
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Find `*/fields.yaml` file in current working directory, auto-read existing fields definitions.
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### Step 2: Get Supplement Source
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Ask user to choose:
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- **A. User direct input**: User provides field names and descriptions
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- **B. Web Search**: Launch agent to search common fields in this domain
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### Step 3: Display and Confirm
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- Display suggested new fields list
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- User confirms which fields to add
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- User specifies field category and detail_level
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### Step 4: Save Update
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Append confirmed fields to fields.yaml, save file.
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## Output
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Updated `{topic}/fields.yaml` file (in-place modification, requires user confirmation)
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---
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name: research-add-items
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description: Add items (research objects) to existing research outline.
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---
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# Research Add Items - Supplement Research Objects
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## Trigger
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`/research-add-items`
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## Workflow
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### Step 1: Auto-locate Outline
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Find `*/outline.yaml` file in current working directory, auto-read.
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### Step 2: Get Supplement Sources in Parallel
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Simultaneously:
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- **A. Ask user**: What items to supplement? Any specific names?
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- **B. Ask if Web Search needed**: Launch agent to search for more items?
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### Step 3: Merge and Update
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- Append new items to outline.yaml
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- Display to user for confirmation
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- Avoid duplicates
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- Save updated outline
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## Output
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Updated `{topic}/outline.yaml` file (in-place modification)
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---
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name: research-deep
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description: Read research outline, launch independent agent for each item for deep research. Disable task output.
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---
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# Research Deep - Deep Research
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## Trigger
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`/research-deep`
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## Workflow
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### Step 1: Auto-locate Outline
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Find `*/outline.yaml` file in current working directory, read items list, execution config (including items_per_agent).
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### Step 2: Resume Check
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- Check completed JSON files in output_dir
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- Skip completed items
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### Step 3: Batch Execution
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- Batch by batch_size (need user approval before next batch)
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- Each agent handles items_per_agent items
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- Launch web-search-agent (background parallel, disable task output)
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**Parameter Retrieval**:
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- `{topic}`: topic field from outline.yaml
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- `{item_name}`: item's name field
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- `{item_related_info}`: item's complete yaml content (name + category + description etc.)
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- `{output_dir}`: execution.output_dir from outline.yaml (default: ./results)
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- `{fields_path}`: absolute path to {topic}/fields.yaml
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- `{output_path}`: absolute path to {output_dir}/{item_name_slug}.json (slugify item_name: replace spaces with _, remove special chars)
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**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
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**Prompt Template**:
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```python
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prompt = f"""## Task
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Research {item_related_info}, output structured JSON to {output_path}
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## Field Definitions
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Read {fields_path} to get all field definitions
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## Output Requirements
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1. Output JSON according to fields defined in fields.yaml
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2. Mark uncertain field values with [uncertain]
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3. Add uncertain array at the end of JSON, listing all uncertain field names
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4. All field values must be in English
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## Output Path
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{output_path}
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## Validation
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After completing JSON output, run validation script to ensure complete field coverage:
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python /home/weizhena/.codex/skills/research/validate_json.py -f {fields_path} -j {output_path}
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Task is complete only after validation passes.
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"""
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```
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**One-shot Example** (assuming researching GitHub Copilot):
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```
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## Task
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Research name: GitHub Copilot
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category: International Product
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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
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## Field Definitions
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Read /home/weizhena/AIcoding/aicoding-history/fields.yaml to get all field definitions
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## Output Requirements
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1. Output JSON according to fields defined in fields.yaml
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2. Mark uncertain field values with [uncertain]
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3. Add uncertain array at the end of JSON, listing all uncertain field names
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4. All field values must be in English
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## Output Path
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/home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
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## Validation
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After completing JSON output, run validation script to ensure complete field coverage:
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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
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Task is complete only after validation passes.
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```
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### Step 4: Wait and Monitor
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- Wait for current batch to complete
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- Launch next batch
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- Display progress
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### Step 5: Summary Report
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After all complete, output:
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- Completion count
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- Failed/uncertain marked items
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- Output directory
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## Agent Config
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- Background execution: Yes
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- Task Output: Disabled (agent has explicit output file when complete)
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- Resume support: Yes
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---
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name: research-report
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description: Summarize deep research results into markdown report, cover all fields, skip uncertain values.
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---
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# Research Report - Summary Report
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## Trigger
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`/research-report`
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## Workflow
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### Step 1: Locate Results Directory
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Find `*/outline.yaml` in current working directory, read topic and output_dir config.
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### Step 2: Scan Optional Summary Fields
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Read all JSON results, extract fields suitable for TOC display (numeric, short metrics), e.g.:
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- github_stars
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- google_scholar_cites
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- swe_bench_score
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- user_scale
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- valuation
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- release_date
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Use request_user_input to ask user:
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- Which fields to display in TOC besides item name?
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- Provide dynamic options list (based on actual fields in JSON)
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### Step 3: Generate Python Conversion Script
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Generate `generate_report.py` in `{topic}/` directory, script requirements:
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- Read all JSON from output_dir
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- Read fields.yaml to get field structure
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- Cover all field values from each JSON
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- Skip fields with values containing [uncertain]
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- Skip fields listed in uncertain array
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- Generate markdown report format: Table of contents (with anchor links + user-selected summary fields) + Detailed content (by field category)
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- Save to `{topic}/report.md`
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**TOC Format Requirements**:
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- Must include every item
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- Each item displays: number, name (anchor link), user-selected summary fields
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- Example: `1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%`
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#### Script Technical Requirements (Must Follow)
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**1. JSON Structure Compatibility**
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Support two JSON structures:
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- Flat structure: Fields directly at top level `{"name": "xxx", "release_date": "xxx"}`
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- Nested structure: Fields in category sub-dict `{"basic_info": {"name": "xxx"}, "technical_features": {...}}`
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Field lookup order: Top level -> category mapping key -> Traverse all nested dicts
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**2. Category Multi-language Mapping**
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fields.yaml category names and JSON keys can be any combination (CN-CN, CN-EN, EN-CN, EN-EN). Must establish bidirectional mapping:
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```python
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CATEGORY_MAPPING = {
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"Basic Info": ["basic_info", "Basic Info"],
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"Technical Features": ["technical_features", "technical_characteristics", "Technical Features"],
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"Performance Metrics": ["performance_metrics", "performance", "Performance Metrics"],
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"Milestone Significance": ["milestone_significance", "milestones", "Milestone Significance"],
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"Business Info": ["business_info", "commercial_info", "Business Info"],
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"Competition & Ecosystem": ["competition_ecosystem", "competition", "Competition & Ecosystem"],
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"History": ["history", "History"],
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"Market Positioning": ["market_positioning", "market", "Market Positioning"],
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}
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```
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**3. Complex Value Formatting**
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- list of dicts (e.g., key_events, funding_history): Format each dict as one line, separate kv with ` | `
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- Normal list: Short lists joined with comma, long lists displayed with line breaks
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- Nested dict: Recursive formatting, display with semicolon or line breaks
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- Long text strings (over 100 chars): Add line breaks `<br>` or use blockquote format for readability
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**4. Extra Fields Collection**
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Collect fields that exist in JSON but not defined in fields.yaml, put in "Other Info" category. Note to filter:
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- Internal fields: `_source_file`, `uncertain`
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- Nested structure top-level keys: `basic_info`, `technical_features` etc.
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- `uncertain` array: Display each field name on separate line, don't compress into one line
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**5. Uncertain Value Skipping**
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Skip conditions:
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- Field value contains `[uncertain]` string
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- Field name is in `uncertain` array
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- Field value is None or empty string
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### Step 4: Execute Script
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Run `python {topic}/generate_report.py`
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## Output
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- `{topic}/generate_report.py` - Conversion script
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- `{topic}/report.md` - Summary report
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---
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name: research
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description: Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.
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---
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# Research Skill - Preliminary Research
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## Trigger
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`/research <topic>`
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## Workflow
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### Step 1: Generate Initial Framework from Model Knowledge
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Based on topic, use model's existing knowledge to generate:
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- Main research objects/items list in this domain
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- Suggested research field framework
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Output {step1_output}, use request_user_input to confirm:
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- Need to add/remove items?
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- Does field framework meet requirements?
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### Step 2: Web Search Supplement
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Use request_user_input to ask for time range (e.g., last 6 months, since 2024, unlimited).
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**Parameter Retrieval**:
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- `{topic}`: User input research topic
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- `{YYYY-MM-DD}`: Current date
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- `{step1_output}`: Complete output from Step 1
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- `{time_range}`: User specified time range
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**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
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Launch 1 web-search-agent (background), **Prompt Template**:
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```python
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prompt = f"""## Task
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Research topic: {topic}
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Current date: {YYYY-MM-DD}
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Based on the following initial framework, supplement latest items and recommended research fields.
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## Existing Framework
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{step1_output}
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## Goals
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1. Verify if existing items are missing important objects
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2. Supplement items based on missing objects
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3. Continue searching for {topic} related items within {time_range} and supplement
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4. Supplement new fields
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## Output Requirements
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Return structured results directly (do not write files):
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### Supplementary Items
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- item_name: Brief explanation (why it should be added)
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...
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### Recommended Supplementary Fields
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- field_name: Field description (why this dimension is needed)
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...
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### Sources
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- [Source1](url1)
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- [Source2](url2)
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"""
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```
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**One-shot Example** (assuming researching AI Coding History):
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```
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## Task
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Research topic: AI Coding History
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Current date: 2025-12-30
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Based on the following initial framework, supplement latest items and recommended research fields.
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## Existing Framework
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### Items List
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1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
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2. Cursor: AI-first IDE, based on VSCode
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...
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### Field Framework
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- Basic Info: name, release_date, company
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- Technical Features: underlying_model, context_window
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...
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## Goals
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1. Verify if existing items are missing important objects
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2. Supplement items based on missing objects
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3. Continue searching for AI Coding History related items within since 2024 and supplement
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4. Supplement new fields
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## Output Requirements
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Return structured results directly (do not write files):
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### Supplementary Items
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- item_name: Brief explanation (why it should be added)
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...
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### Recommended Supplementary Fields
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- field_name: Field description (why this dimension is needed)
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...
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### Sources
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- [Source1](url1)
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- [Source2](url2)
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```
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### Step 3: Ask User for Existing Fields
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Use request_user_input to ask if user has existing field definition file, if so read and merge.
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### Step 4: Generate Outline (Separate Files)
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Merge {step1_output}, {step2_output} and user's existing fields, generate two files:
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**outline.yaml** (items + config):
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- topic: Research topic
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- items: Research objects list
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- execution:
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- batch_size: Number of parallel agents (confirm with request_user_input)
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- items_per_agent: Items per agent (confirm with request_user_input)
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- output_dir: Results output directory (default: ./results)
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**fields.yaml** (field definitions):
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- Field categories and definitions
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- Each field's name, description, detail_level
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- detail_level hierarchy: brief -> moderate -> detailed
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- uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)
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### Step 5: Output and Confirm
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- Create directory: `./{topic_slug}/`
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- Save: `outline.yaml` and `fields.yaml`
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- Show to user for confirmation
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## Output Path
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```
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{current_working_directory}/{topic_slug}/
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├── outline.yaml # items list + execution config
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└── fields.yaml # field definitions
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```
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## Follow-up Commands
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- `/research-add-items` - Supplement items
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- `/research-add-fields` - Supplement fields
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- `/research-deep` - Start deep research
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import json
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import sys
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from collections import defaultdict
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from pathlib import Path
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import yaml
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CATEGORY_MAPPING = {
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"basic_info": ["basic_info", "Basic Info"],
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"technical_features": ["technical_features", "technical_characteristics", "Technical Features"],
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"performance_metrics": ["performance_metrics", "performance", "Performance Metrics"],
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"milestone_significance": ["milestone_significance", "milestones", "Milestone Significance"],
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"business_info": ["business_info", "commercial_info", "Business Info"],
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"competition_ecosystem": ["competition_ecosystem", "competition", "Competition Ecosystem"],
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"history": ["history", "History"],
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"market_positioning": ["market_positioning", "market", "Market Positioning"],
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}
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_SKIP_KEYS = {"_source_file", "uncertain"}
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def load_fields_yaml(fields_path):
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with fields_path.open(encoding="utf-8") as f:
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data = yaml.safe_load(f)
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items = [
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(field["name"], category["category"], field.get("required", False))
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for category in data.get("field_categories", [])
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for field in category.get("fields", [])
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]
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all_fields = {name for name, _, _ in items}
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required_fields = {name for name, _, required in items if required}
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field_categories = {name: category for name, category, _ in items}
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return all_fields, required_fields, field_categories
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def extract_json_fields(data, category_mapping=None):
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category_mapping = CATEGORY_MAPPING if category_mapping is None else category_mapping
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nested_keys = {k for keys in category_mapping.values() for k in keys}
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fields = set()
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stack = [(data, True)]
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while stack:
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obj, is_category_level = stack.pop()
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if isinstance(obj, dict):
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for k, v in obj.items():
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if k in _SKIP_KEYS:
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continue
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if is_category_level and k in nested_keys:
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if isinstance(v, dict):
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stack.append((v, True))
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continue
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fields.add(k)
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elif isinstance(obj, list):
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stack.extend((item, is_category_level) for item in obj if isinstance(item, dict))
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return fields
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def validate_json(json_path, all_fields, required_fields, field_categories):
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with json_path.open(encoding="utf-8") as f:
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data = json.load(f)
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json_fields = extract_json_fields(data)
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covered = all_fields & json_fields
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missing = all_fields - json_fields
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extra = json_fields - all_fields
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missing_required = missing & required_fields
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missing_by_category = defaultdict(list)
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for field in missing:
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missing_by_category[field_categories.get(field, "Unknown")].append(field)
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return {
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"file": json_path.name,
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"total_defined": len(all_fields),
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"covered": len(covered),
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"missing": len(missing),
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"extra": len(extra),
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"coverage_rate": len(covered) / len(all_fields) * 100 if all_fields else 100,
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"missing_required": sorted(missing_required),
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"missing_optional": sorted(missing - required_fields),
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"missing_by_category": {k: sorted(v) for k, v in missing_by_category.items()},
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"extra_fields": sorted(extra),
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"valid": len(missing_required) == 0,
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}
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def print_result(result, verbose=True):
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status = "PASS" if result["valid"] else "FAIL"
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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()
|
||||
Reference in New Issue
Block a user