Technical Skill Finder

Technical Skill Finder

Purpose

Find recurring pain points from local agent logs and convert them into actionable skill candidates, reuse opportunities, or existing skill updates.

When to use

• You want to discover missing technical skills from historical agent activity.
• You want reproducible criteria before creating a new skill.
• You want to validate whether an existing skill already covers the pattern.
• You want to include optional personal-signal sources (when authorized).

Inputs

• SCOPE (required): repository paths, workspace, or tool domains to inspect.
• SOURCES (required): ordered source list to mine.
• TIMEFRAME (optional): default all unless constrained by user.
• PRIVACY_POLICY (required): explicit user direction for personal logs.
• TOP_N (optional): number of highest-priority candidates to return.

Workflow

1. Initialize source set
   • ~/.codex/history.jsonl
   • ~/.codex/archived_sessions/*.jsonl
   • ~/.codex/sessions/*.jsonl and ~/.codex/log/* if present
   • Repository-specific telemetry in AGENTS.md/local docs when available
   • Cursor / Codex agent logs detected under known dotfiles directories
2. Normalize extraction signals
   • Parse stack traces and classify failure type (auth, type-check, llm-error, git/ci, runtime, refactor-merge, test)
   • Parse recurring command phrases (rg, mypy, pytest, gh, git, package-manager failures)
   • Record frequency, recency, and affected project context
3. Cluster signals
   • Group by: domain (python/js/rust/docs/tooling), command lineage, and error signature.
   • Deprioritize one-off sessions with low recurrence.
4. Map to existing skills
   • Compare candidate clusters with available skills by name and description.
   • If overlap is high, propose skill update path.
   • If no overlap, propose new skill.
5. Emit ranking output
   • Provide impact, frequency, confidence, skill-fit, and first-apply command set.
6. Produce minimal first-iteration output for high-priority candidates
   • Candidate title + scope
   • Trigger phrase examples
   • Required inputs
   • Suggested workflow summary
   • Evidence snippets (line/file-level)
   • Suggested dependencies/tools (e.g., jq, rg, shell utilities, MCP resources)
   • Return this through chat/stdout by default. Create a persistent artifact root only when the user selects one or another required workflow declares it, with file/byte budgets and source/input identity.
7. Optional extension to personal-signal sources
   • Only after explicit approval to read personal channels.
   • If MCP is available and user has granted access, run MCP resource discovery and include message-signal-derived patterns.
   • Keep this opt-in and isolated from coding-signal output unless user requests a merged plan.

Guardrails

• Never infer or emit private content from message logs unless explicitly permitted.
• Skip binary/corrupt files and summarize only parseable text sources.
• Prefer deterministic commands and small scripts over ad-hoc manual parsing.
• Always avoid proposing skills with unresolved operational context (credentials, environment, private URLs).
• If evidence is ambiguous, return confidence: low and request one more session sample.
• Reuse one canonical identity-matched inventory instead of materializing duplicate large extracts. Apply the $operations-worktree task artifact contract when retention or resumable phase state is required.

Outputs

• skill_candidates.md-style report in chat:
  • reuse candidates (existing skill can be extended)
  • new skill candidates (not yet covered)
  • top source anchors with references
  • recommended next action (create/update)

Read references/sources.md for source precedence. Read references/scorecard.md for prioritization rules.