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

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
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
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
```
name
```
  and
```
description
```
  .
- If overlap is high, propose skill update path.
- If no overlap, propose new skill.
Emit ranking output
- Provide
```
impact
```
  ,
```
frequency
```
  ,
```
confidence
```
  ,
```
skill-fit
```
  , and first-apply command set.
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)
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.

technical-skill-finder

NPX Install

Tags

SKILL.md Content