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vincentkoc / 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.

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SKILL.md
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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.

Flow

stateDiagram-v2
    [*] --> SelectAuthorizedSources
    SelectAuthorizedSources --> NormalizeAndCluster
    NormalizeAndCluster --> CompareExistingSkills
    CompareExistingSkills --> ProposeUpdate: substantial overlap
    CompareExistingSkills --> ProposeNewSkill: no existing coverage
    CompareExistingSkills --> ReportUncertainty: insufficient evidence
    ProposeUpdate --> RankAndReport
    ProposeNewSkill --> RankAndReport
    RankAndReport --> [*]
    ReportUncertainty --> [*]
    note right of SelectAuthorizedSources
        Personal channels require explicit approval.
        Return findings inline unless retention is selected.
    end note