vincentkoc/dotskills/technical-skill-finder

Claude Code and agent skill by vincentkoc/dotskills

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

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

Install

npx vskill@latest install vincentkoc/dotskills/technical-skill-finder

SKILL.md

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
  • Recursively discover *.jsonl under ~/.codex/sessions/, including dated YYYY/MM/DD/rollout-*.jsonl paths
  • ~/.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 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.

  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.
  • 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

mermaid
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

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
28
Repository forks
4
7-day trend score
17

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 2 Apr 2026, 7 months ago.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

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