technical-skill-finder
Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations.
What this skill does
# 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 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) 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. ## 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.
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