auto-knowledge-capture

What I do

Teach agents how to silently evaluate whether a work session produced knowledge worth capturing, and if so, persist it without prompting the user. This is the "smart auto-capture" skill — it only fires when there's actually something to capture.

When to use me

This skill is embedded in the final step of /deliver, /diagnose, /retro, and /design. You don't load it manually — it activates automatically at the end of these workflows when knowledge.auto_capture is enabled in hero.json (default: on).

The decision: capture or skip?

Before writing anything, evaluate whether the session produced novel, reusable knowledge. Ask yourself:

Capture if ANY of these are true:

• A technical constraint was discovered that isn't documented (e.g., "FTS5 can't handle hyphens in MATCH queries")
• A design decision was made with trade-offs discussed (e.g., "chose SSE over WebSockets for MCP transport")
• A pattern was established that future work should follow (e.g., "all CLI flags use package-level vars and resetFlags()")
• A workaround was needed for a tool/library limitation (e.g., "Cobra flag state persists across test runs")
• An integration or API behavior was learned empirically (e.g., "Figma API can't generate designs, only read them")
• A architectural boundary or constraint was established (e.g., "no new Go dependencies")

Skip if ALL of these are true:

• The work was straightforward implementation of an existing spec
• No surprises or unexpected behavior was encountered
• No decisions were made — just execution
• The knowledge already exists in .hero/knowledge/

When in doubt, skip. Noise in the knowledge base is worse than gaps.

How to capture silently

When you determine something is worth capturing:

1. Don't prompt the user. Don't ask "should I save this?" Just do it.

2. Classify the learning into the right knowledge type:

   • Pattern/style → convention (.hero/knowledge/conventions/<slug>/spec.md)
   • Technical choice → decision (.hero/knowledge/decisions/<slug>/spec.md)
   • Hard constraint → rule (.hero/knowledge/rules/<slug>/spec.md)
   • Background info → context (.hero/knowledge/context/<slug>/spec.md)
   • Rough insight → note (hero note <slug>)

3. Write concisely. Knowledge entries should be scannable:

   • Title: clear, specific (not "Database Notes" — instead "SQLite FTS5 Hyphen Handling")
   • Body: 3-10 sentences. What, why, and how to apply it.
   • Tags: 2-4 relevant tags for discoverability

4. Mention it briefly at the end of your response. One line, not a sales pitch:

   • "Captured convention: sqlite-fts5-hyphen-handling"
   • "Captured decision: sse-over-websockets-mcp-transport"
   • Or if nothing: say nothing. Don't announce "no knowledge captured."

5. Run hero index after writing to make it searchable.

Knowledge entry templates

Convention

---
title: <Pattern Name>
type: convention
status: active
tags: [<tag1>, <tag2>]
created: <YYYY-MM-DD>
---

## Convention

<What the pattern is. 1-2 sentences.>

## Rationale

<Why this pattern exists. What goes wrong without it.>

## Examples

<Good and bad examples if helpful.>

Decision

---
title: <Decision Title>
type: decision
status: accepted
tags: [<tag1>, <tag2>]
created: <YYYY-MM-DD>
---

## Decision

<What was decided. 1 sentence.>

## Context

<What prompted this decision.>

## Alternatives Considered

<What else was evaluated and why it was rejected.>

Rule

---
title: <Rule Name>
type: rule
status: active
tags: [<tag1>, <tag2>]
created: <YYYY-MM-DD>
---

## Rule

<The constraint. 1 sentence.>

## Rationale

<Why this rule exists.>

Deduplication

Before writing, mentally check against existing knowledge. If you recall that a similar convention or decision already exists from earlier in the conversation or from reading the knowledge base, don't create a duplicate. If the existing entry needs updating, update it instead of creating a new one.

Volume control

A typical session should produce 0-3 knowledge entries. If you find yourself wanting to write more than 3, you're probably being too granular. Consolidate related learnings into a single entry.