knowledge-flywheel

Imported from astroville/sprout (.claude/skills/knowledge-flywheel/SKILL.md). Install upstream with npx skills add astroville/sprout --skill knowledge-flywheel. Copyright stays with the author.

What I do

Analyze the knowledge base (.hero/knowledge/) for patterns that should be promoted to conventions, decisions, or warnings. This turns raw captured notes into structured team intelligence.

When to use me

• At the end of a sprint or after 5+ knowledge entries have been captured
• When /prime detects a growing knowledge base (>10 entries)
• When the user asks "what have we learned?" or "any patterns?"

How to use me

1. Read all entries in .hero/knowledge/notes/
2. Group by topic (file paths mentioned, error types, technologies)
3. Identify patterns:
   • Repeated decisions: same choice made 3+ times → suggest a convention
   • Recurring issues: same debugging finding 2+ times → suggest a rule
   • Converging practices: similar approaches across specs → suggest codifying
4. For each pattern found, suggest an action:
   • /convention to codify a repeated practice
   • Add to an existing convention if one covers the topic
   • Flag as a potential architectural concern if it's a structural pattern

Output format

Knowledge flywheel — N entries analyzed

Patterns detected:

  1. Convention candidate: "Error handling in API routes"
     Seen in: 3 notes (auth-debugging, payment-retry, export-errors)
     Suggestion: /convention codify API error response retry pattern

  2. Recurring issue: "Database connection pool exhaustion"
     Seen in: 2 notes (monday-outage, load-test-findings)
     Suggestion: Add connection pool monitoring to operational runbook

  3. No new patterns (N entries too unique to cluster)

What not to do

• Don't fabricate patterns — only surface what's genuinely repeated
• Don't suggest conventions for one-off decisions
• Don't modify existing knowledge entries
• Don't require an LLM — pattern detection is keyword/topic clustering