| name | knowledge-flywheel |
| description | Analyze captured knowledge for patterns — detect repeated decisions, suggest conventions, surface "you keep running into X" insights. |
| compatibility | opencode |
| metadata | {"audience":"any-agent","purpose":"knowledge-amplification"} |
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
- Read all entries in
.hero/knowledge/notes/
- Group by topic (file paths mentioned, error types, technologies)
- 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
- 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
