Autonomous Gap Detector Protocol

This protocol defines how an AI agent should autonomously manage skill gaps within a repository.

🤖 Agent Instructions (CRITICAL)

As an AI Agent, you are responsible for maintaining the "Documentation Health" of the project. You MUST trigger the Gap Detection process in the following scenarios:

•Task Initialization: When starting a new feature, scan for existing skills to see if you have the necessary knowledge.
•Dependency Changes: Immediately after adding or updating any library (e.g., in package.json, requirements.txt).
•Task Completion (DoD Check): Before marking any task as "Done", you must verify that no new skill gaps were introduced.

Execution Protocol

When triggered, you must perform these steps:

•

Run Scanner: Execute the diagnostic script:

cmd

python d:\Cerebra\cerebraSkills\scripts\skill_watcher.py --target .

•
Analyze GAP_REPORT.md:
- •
  If RED GAPS are found:
  - •Analyze if the library is critical for the project's long-term maintenance.
  - •If it represents a new logic pattern, use the Skill Architect (72-metacognitive-skill-architect) to draft a new skill.
  - •Ask the user if they want you to finalize the new skill now or later.
- •If ALL GREEN: Proceed with the task.
•
Cross-Reference: Check if the library is already in LIB_MAPPING in scripts/skill_watcher.py. If not, update the mapping to ensure future scans are accurate.

Reporting Logic

Any gaps identified should be reported to the user in this format:

"🚨 Skill Gap Detected: You are using {library}, but we don't have a standardized Skill for it yet. This may lead to maintenance issues. Should I draft a Skill for this now?"

Technical Details

Scanner Script

•Location: d:\Cerebra\cerebraSkills\scripts\skill_watcher.py
•Config: Edit LIB_MAPPING to add new technology associations.

Logic Flow

•Parsing project dependency files.
•Filtering against IGNORE_FILES and IGNORE_DIRS.
•Matching against SKILL_INDEX.md entries.
•Outputting markdown report to GAP_REPORT.md.