aesthetic-instrument
great_cto's own committed aesthetic — the instrument panel. Dark five-step surface ladder, exactly one accent, two faces divided by MEANING (Geist speaks,…
Distils repeating patterns from session logs and lessons.md into draft skill files. Run after ≥10 sessions to extract durable knowledge. Output: draft skills/ files + promotion report.
$ npx -y skills add avelikiy/great_cto --skill crystallize --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/crystallizeContext preview
The summary Claude sees to decide when to auto-load this skill.
Distils repeating patterns from session logs and lessons.md into draft skill files. Run after ≥10 sessions to extract durable knowledge. Output: draft skills/ files + promotion report.
name: crystallize description: "Distils repeating patterns from session logs and lessons.md into draft skill files. Run after ≥10 sessions to extract durable knowledge. Output: draft skills/ files + promotion report." when_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" effort: high allowed-tools: Read, Write, Glob, Grep, Bash, Agent paths: - ".great_cto/logs/**" - ".great_cto/lessons.md" - "~/.great_cto/decisions.md" - "skills/**"
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.You already have the agent. This is everything around it. great_cto runs Claude Code as a pipeline of 70 specialist agents — an independent model checks each stage before the next builds on it, spending caps refuse rather than warn, and three decisions stay yours: what gets built, how, and whether it ships.
Repo: avelikiy/great_cto
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