technical-skill-finder（技术技能查找器）

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

1. 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

1. Cluster signals

- Group by: domain (python/js/rust/docs/tooling), command lineage, and error signature. - Deprioritize one-off sessions with low recurrence.

1. 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.

1. Emit ranking output

- Provide impact, frequency, confidence, skill-fit, and first-apply command set.

1. Produce minimal first-iteration artifacts 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)

1. 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.

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.