review-chats

name	review-chats
description	Use when the user wants to analyze their Claude Code chat history for recurring patterns, forgotten threads, or skill-abstraction candidates ("review my chats", "abstract skills from chat logs", "what am I repeating", "find patterns in my Claude sessions", "retro my chat history").

review-chats

Analyze Mike's Claude Code chat history (~133 project corpuses at ~/.claude/projects/) to surface recurring workflows that could become skills. Self-bootstrapping — uses parallel Explore subagents to avoid blowing up the main context.

PAIRED WITH: When a candidate is selected, chain into new-skill to scaffold it.

REQUIRED BACKGROUND: Uses superpowers:dispatching-parallel-agents pattern — up to 3 Explore agents in parallel, each with a disjoint slice of the corpus.

When to Use

Use when the user asks:

• "review my chats for patterns"
• "abstract skills from my chat logs"
• "what am I repeating across projects"
• "find skill candidates" / "what should I skill-ify"
• "retro my chat history"

Do NOT use when:

• The user wants to find a specific past conversation (use grep / search directly)
• The user wants today's chat only (too small a corpus)
• The user wants metrics (token counts, durations) — out of scope here

Corpus Structure

Each chat log is at ~/.claude/projects/<slug>/<session-uuid>.jsonl. Lines are JSON objects; user messages have "type":"user". The first user message of each session is the session-intent signal — most informative per byte.

Total scope (as of 2026-04): ~133 project dirs, mostly small. A handful are large (SignLab-Dev 331MB, superpowers 11MB, etc.) — weight sampling by corpus size.

Workflow

1. Scope the corpus. Default: all of ~/.claude/projects/. If the user specifies a project filter ("in roxas") or date range ("last month"), narrow accordingly.

2. Partition the corpus into 3 clusters for parallel analysis:

   • Cluster A: most recently active (last 2 weeks by mtime)
   • Cluster B: largest by bytes (top 5 dirs by du -sh)
   • Cluster C: the rest (sample evenly)

3. Dispatch 3 Explore subagents in parallel — one per cluster. Each agent's prompt:

   • Lists the specific project dirs it owns
   • Asks it to extract first-user-messages and look for phrasing patterns
   • Asks it to report in under 400 words: pattern name, frequency, 2-3 verbatim examples, proposed skill scope
   • Explicitly lists the CURRENTLY INSTALLED skills (pass them in) so the agent can filter out already-covered patterns

4. Merge & dedupe the three reports. Patterns surfaced by 2+ agents are high-confidence.

5. Cross-check against installed skills. Run ls ~/.claude/skills/ && ls /Users/mikelady/dev/mike-skills/ and compare trigger phrases in candidate descriptions. Drop any candidate whose triggers heavily overlap with an existing skill.

6. Present candidates via AskUserQuestion (multi-select). Each option shows: pattern name, frequency, one example. Include an "add your own" by letting Mike reply with "Other".

7. For each selected candidate: chain into the new-skill skill to scaffold it.

Signal Extraction Heuristics

Pass these hints to the subagents:

Signal	What it means
Same opening phrase across ≥3 sessions (e.g., "install https://...")	Strong skill candidate
Repeated tool-use sequences (Bash → Grep → Bash)	Workflow worth abstracting
First user message is long and highly structured	Often a template the user re-types
Terms like "again", "like last time", "the usual"	User is doing repeat work
Sessions ending mid-stream (assistant tool calls, no user response)	Forgotten threads, not patterns

Getting the Installed-Skills List

Pass this into every subagent prompt so they can filter:

( ls ~/.claude/skills/ ; ls /Users/mikelady/dev/mike-skills/ ) | sort -u

Output Format

After all subagents report:

Found N candidate patterns (M after filtering against installed skills):

1. <pattern-name> — <frequency>
   Example: "<verbatim user message>"
   Proposed scope: <one sentence>

2. ...

Then use AskUserQuestion to let Mike select which to build.

Common Mistakes

• Running serially instead of in parallel. The corpus is large; 3 parallel Explore agents cut wall time to ~1/3.
• Reading full chat logs. Only the first-user-message of each session is worth the tokens. Grep and sample.
• Forgetting to filter against installed skills. Without the filter, the output is 80% things Mike already has.
• Proposing too many candidates. Cap at 5-7. More than that paralyzes selection.
• Missing framework-prefixed skills in the installed-list. dialed:*, peon-ping-*, superpowers:* count — include them in the filter.
• Not distinguishing "recurring" from "just happened recently". A pattern needs to span multiple sessions to be a real candidate.

Don't Re-Derive

If Mike has already run review-chats recently (check /Users/mikelady/dev/mike-skills/review-chats/last-run.md if present), start from that baseline rather than re-scanning the entire corpus. Ask Mike if he wants a delta from last run or a fresh full scan.