Process Research Integration Opportunities

Process Research Integration Opportunities

Batch process all research markdown files to discover and document integration opportunities with existing repository capabilities.

What This Does

1. Finds all markdown files in research/ (excluding README.md files)
2. For each research file, spawns the research-context-agent
3. Agent reads the file, searches for connections, appends Integration Opportunities section
4. Reports summary of all processed files

Usage

/process-research-integration

Options:

# Process a specific category
/process-research-integration --category developer-tools

# Process a single file
/process-research-integration --file research/developer-tools/loguru.md

# Force reprocess (replace existing Integration Opportunities)
/process-research-integration --force

# Dry run (show what would be processed without making changes)
/process-research-integration --dry-run

Implementation

This command uses the batch processing script at ./scripts/process-research-integration.py.

Script Features:

• Cross-platform Python script using uv with inline script metadata
• CLI built with Typer for command-line options
• Rich console output for progress tracking and results visualization
• Supports category filtering, single file processing, force reprocessing, and dry-run mode

For manual invocation:

# Using uv (recommended - handles dependencies automatically)
uv run ./scripts/process-research-integration.py --help

# Or make executable and run directly
chmod +x ./scripts/process-research-integration.py
./scripts/process-research-integration.py --help

Processing workflow:

1. Find research files matching criteria (category/file/all)
2. For each file, spawn research-context-agent via Task tool
3. Agent reads, analyzes, validates with WebSearch, appends Integration Opportunities
4. Track and display results in formatted tables

Agent orchestration (to be implemented):

• Use Task tool to spawn research-context-agent instances
• Pass file path as parameter
• Sequential processing (one file at a time)
• Collect results and generate summary

Expected Output

🔍 Processing Research Integration Opportunities

Found 58 research files across 24 categories

Processing: research/developer-tools/loguru.md
✅ Completed (2 enhancements, 0 skills, 1 cross-ref)

Processing: research/developer-tools/traycer.md
✅ Completed (1 enhancement, 0 skills, 0 cross-refs)

...

📊 Summary
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Files processed:        58
Enhancements found:     87
New skill candidates:   12
New MCP candidates:     8
Cross-references:       23
Failures:               0
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✅ All research files processed

Notes

• Processing time: With 58 files, expect ~30-60 minutes for full batch
• Incremental updates: Can re-run safely; existing sections will be replaced
• Review required: Auto-generated opportunities should be reviewed before implementation
• Cross-reference sync: When updating one file, related files may need updates too

Quality Assurance

The research-context-agent applies these quality gates:

✅ Concrete, actionable descriptions (not vague suggestions) ✅ Specific connection between research and target ✅ Empty sections omitted (not filled with "None") ✅ Existing content preserved (append-only) ✅ Idempotent (can re-run without duplication)

Related

• Agent: .claude/agents/research-context-agent.md — The agent that does the actual processing
• Script: ./scripts/process-research-integration.py — Python batch processing implementation
• Skill: .claude/skills/research-curator/ — Related research management workflow
• Directory: research/ — All research entries