mine-logs-for-skills

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                 # both sources (default)
python3 scripts/distill.py --source codex  # or claude / both
# options: --since YYYY-MM-DD, --max-prompt-chars N, --claude-dir, --codex-dir, --out

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    # reads distilled.jsonl → dashboard.html

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.