AgentSkillsCN

technical-skill-finder

挖掘编码代理的日志（如 Codex/Cursor 会话历史记录及类似遥测数据），以发现高价值的候选技能，随后拟定结构化的技能创建与复用建议。

SKILL.md
--- frontmatter
name: technical-skill-finder
description: Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations.
license: AGPL-3.0-only
metadata:
  source: "https://github.com/vincentkoc/dotskills"

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.