Dotskills 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.
install
source · Clone the upstream repo
git clone https://github.com/vincentkoc/dotskills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/vincentkoc/dotskills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/technical-skill-finder" ~/.claude/skills/vincentkoc-dotskills-technical-skill-finder && rm -rf "$T"
manifest:
skills/technical-skill-finder/SKILL.mdsource content
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
(required): repository paths, workspace, or tool domains to inspect.SCOPE
(required): ordered source list to mine.SOURCES
(optional): defaultTIMEFRAME
unless constrained by user.all
(required): explicit user direction for personal logs.PRIVACY_POLICY
(optional): number of highest-priority candidates to return.TOP_N
Workflow
- Initialize source set
~/.codex/history.jsonl~/.codex/archived_sessions/*.jsonl
and~/.codex/sessions/*.jsonl
if present~/.codex/log/*- Repository-specific telemetry in
/local docs when availableAGENTS.md
/Cursor
agent logs detected under known dotfiles directoriesCodex
- 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
, package-manager failures)git - Record frequency, recency, and affected project context
- Parse stack traces and classify failure type (
- Cluster signals
- Group by: domain (python/js/rust/docs/tooling), command lineage, and error signature.
- Deprioritize one-off sessions with low recurrence.
- Map to existing skills
- Compare candidate clusters with available skills by
andname
.description - If overlap is high, propose skill update path.
- If no overlap, propose new skill.
- Compare candidate clusters with available skills by
- Emit ranking output
- Provide
,impact
,frequency
,confidence
, and first-apply command set.skill-fit
- Provide
- 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
, shell utilities, MCP resources)rg
- 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
and request one more session sample.confidence: low
Outputs
-style report in chat:skill_candidates.md
candidates (existing skill can be extended)reuse
skill candidates (not yet covered)new- top source anchors with references
- recommended next action (create/update)
Read
references/sources.md for source precedence.
Read references/scorecard.md for prioritization rules.