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From great_cto
Distils repeating patterns from session logs and lessons into draft skill files. Run after 10+ sessions to extract durable knowledge.
npx claudepluginhub avelikiy/great_ctoHow this skill is triggered — by the user, by Claude, or both
Slash command
/great_cto:crystallizeWhen to use
Apply when: - CTO says /crystallize, "crystallize", or "extract knowledge" - Session count in .great_cto/logs/ reaches a multiple of 10 (auto-suggest) - User asks "what have we learned?" or "turn lessons into skills"
.great_cto/logs/**.great_cto/lessons.md~/.great_cto/decisions.mdskills/**This skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Invoke when the CTO says `/crystallize`, "crystallize", "extract knowledge", or
Detects repeated workflow patterns from AI session transcripts and suggests reusable skills to automate them. Use when optimizing workflows.
Mines coding-agent session history, transcripts, and memories to discover recurring workflows and draft new Agent Skills from real usage evidence.
Extracts reusable patterns from Claude Code sessions and saves them as learned skills for future use. Useful after long sessions with complex problem-solving.
Share bugs, ideas, or general feedback.
Invoke when the CTO says /crystallize, "crystallize", "extract knowledge", or
"what have we learned?". Also auto-suggested when session count is a multiple
of 10 (the session-end hook checks .great_cto/.last-crystallize).
The knowledge-extractor agent (Opus) does the heavy lifting. This skill
orchestrates the workflow and emits the final report.
Session-end hint integration: The session-end hook checks
.great_cto/.last-crystallize and suggests running /crystallize when the
session count exceeds last_sessions + 10. Run this skill after ≥10 sessions
to keep extracted skills current.
# Count sessions
SESSION_COUNT=$(ls .great_cto/logs/session-*-end.md 2>/dev/null | wc -l | tr -d ' ')
echo "Sessions: $SESSION_COUNT"
# Read lessons
cat .great_cto/lessons.md 2>/dev/null || echo "(no lessons yet)"
# Read cross-project decisions
cat ~/.great_cto/decisions.md 2>/dev/null | head -200 || echo "(none)"
# Find patterns that appear in ≥3 sessions
grep -h "^## pattern:" .great_cto/logs/session-*-end.md 2>/dev/null | sort | uniq -c | sort -rn | head -20
# Recent git log for context
git log --oneline --since="30 days ago" | head -30
If SESSION_COUNT is 0, tell the CTO: "No session logs found in
.great_cto/logs/. Run at least 10 sessions before crystallizing." Exit.
If SESSION_COUNT < 10, tell the CTO: "Only {N} sessions found. Patterns
are more reliable after ≥10 sessions. Proceed anyway? [yes/no]" Wait for
confirmation before continuing.
Spawn the knowledge-extractor agent with the gathered data as context:
Agent: knowledge-extractor
Task: |
Read .great_cto/lessons.md and all files in .great_cto/logs/.
Cluster lesson entries by pattern slug.
For each cluster with ≥3 occurrences, write a draft skill file to
skills/{domain}/SKILL.md (status: draft in frontmatter).
If a skill for that domain already exists, append a new ## section instead
of replacing the file.
Infer domain from the pattern slug and its archetype tags.
Return a structured summary: clusters found, drafts written, already-covered.
Wait for the agent to complete before proceeding to Step 3.
After the agent completes, print:
CRYSTALLIZE REPORT
════════════════════════════════════════
Sessions analysed: {SESSION_COUNT}
Lessons found: {LESSON_COUNT}
Clusters: {CLUSTER_COUNT}
Draft skills: {DRAFT_COUNT} (in skills/{domain}/SKILL.md)
Already covered: {COVERED_COUNT} (pattern already in existing skill)
════════════════════════════════════════
Draft files:
{list of paths and brief description per draft}
Next: review drafts, remove `status: draft` when satisfied.
Run /crystallize again after 10 more sessions.
════════════════════════════════════════
After emitting the report, write the marker file:
SESSION_COUNT=$(ls .great_cto/logs/session-*-end.md 2>/dev/null | wc -l | tr -d ' ')
DRAFT_COUNT={P} # from agent output
mkdir -p .great_cto
node -e "
const fs = require('fs');
fs.writeFileSync('.great_cto/.last-crystallize', JSON.stringify({
ts: new Date().toISOString(),
sessions: parseInt('$SESSION_COUNT') || 0,
drafts: parseInt('$DRAFT_COUNT') || 0
}) + '\n');
"
If SESSION_COUNT is a multiple of 10 (and > 0), append to the report:
Auto-suggestion: you've completed {SESSION_COUNT} sessions. Consider running
`/crystallize` every 10 sessions to keep skills current.