Szpitale-graph/skills/research/deep/SKILL.md

description, allowed-tools

description	allowed-tools
Read research outline, launch independent agent for each item for deep research. Disable task output.	Bash, Read, Write, Glob, WebSearch, Task

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}.json

Hard Constraint: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.

Prompt Template:

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 Chinese (research can be in English, but final JSON values in Chinese)

## Output Path
{output_path}

## Validation
After completing JSON output, run validation script to ensure complete field coverage:
python ~/.claude/commands/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 Chinese (research can be in English, but final JSON values in Chinese)

## 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 ~/.claude/commands/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