| name | mine-logs-for-skills |
| description | Analyze your own AI coding-assistant session history (Claude Code AND OpenAI Codex) to discover recurring workflows and propose new skill candidates (global or project-scoped), plus surface usage insights. Use when the user wants to mine/analyze their logs/transcripts/history for skill ideas, find repeated tasks worth automating, or audit how they use their AI tools. |
Mining AI assistant logs for new skill candidates
Your transcripts are a record of every task you've actually done. This skill turns
that history into proposed skills and usage insights — using a cheap
deterministic distill step plus your own agentic reasoning (semantic
understanding, not regex clustering).
It reads both assistants' transcript stores:
- Claude Code —
~/.claude/projects/<dashed-cwd>/<uuid>.jsonl (message-stream)
- OpenAI Codex —
~/.codex/sessions/YYYY/MM/DD/rollout-*.jsonl (event-stream)
Where does it look? Both stores live at fixed home-relative paths, so the
skill does NOT need to detect which assistant is running it — it reads whichever
stores exist and extracts from both by default (--source both). A missing
store is silently skipped. cwd is the join key, so the same repo worked in
either tool groups together in the corpus. Narrow with --source claude|codex.
Pipeline
Step 1 — Distill (deterministic, cheap)
Run the distiller (path is relative to this skill's directory — the runtime tells
you the skill's base directory):
python3 scripts/distill.py
python3 scripts/distill.py --source codex
Outputs (default ~/.cache/mine-logs-for-skills/):
corpus.md — size-bounded digest: per-source counts, a global tool-frequency
table (the single best at-a-glance signal of what you automate), and prompts
grouped by project (each session tagged [claude]/[codex]). Read this first.distilled.jsonl — one complete record per session (source, project, prompts,
tool_counts, tool_sequence, timestamps). Use Grep/Read for drill-down.
Step 1b — Dashboard (optional, visual)
Render a self-contained HTML dashboard (KPI cards, top-tools chart, busiest
projects, per-source split, monthly activity) — aggregates only, no raw prompt
text, safe to share:
python3 scripts/dashboard.py
Open the printed dashboard.html in a browser. No external dependencies (works
offline; embeds no remote scripts).
Step 2 — Analyze (agentic: semantic reasoning, this is the point)
Read corpus.md, then reason over it the way only an LLM can. Do NOT just count
strings — recognize intent even when wording differs.
- Cluster by intent, not exact text. Collapse near-duplicates (e.g. the many
auto-generated
/security-review "Review this change…" prompts are ONE recurring
workflow already covered by a command — note it, don't propose it).
- Route global vs project: count how many distinct project paths a cluster
appears in. Many projects → user-level skill; one repo → project-level.
- Cross-tool signal: a workflow that shows up in BOTH Claude and Codex logs is
a strong, tool-agnostic skill candidate.
- For scale, fan out subagents (Explore / general-purpose), one per project
group or corpus slice, returning structured findings; then synthesize.
- Cross-reference existing skills so you don't propose a duplicate.
Step 3 — Propose
Per candidate: name, one-line description (trigger sentence), scope
(global vs project + distinct-project count), evidence (2-4 real prompts),
frequency, sketch (SKILL.md + helper), and the friction it removes.
Also surface non-skill insights: heavily-used tools/MCPs, repeated failures,
multi-turn workflows that could be shortened.
Step 4 — Author (on approval only)
For each approved candidate, use a skill-authoring flow to generate a properly
structured SKILL.md (+ helper) in the correct location. Validate any shipped
code before writing.
Bonus: secret hygiene
scripts/scan_secrets.py scans both stores for plaintext tokens and reports
redacted locations (high-confidence prefixed tokens vs. low-confidence 40-hex that
also matches git SHAs). Useful before sharing/retaining logs.
Guardrails
- Logs are personal and may contain secrets. Keep all analysis local; never send
corpus contents to an external service.
- Don't auto-create skills — always show candidates and get explicit approval.
- Re-runnable: as history grows, re-run Step 1 with
--since to analyze only new
sessions.
