Kw:compound

Compound

Close the loop. Extract what you learned and save it where future work will find it.

When to Use

• After completing a plan, campaign, analysis, or strategy session

• "Compound this session", "Save what we learned", "What should we remember?"

• After a data correction, process fix, or strategic insight

• At the end of any meaningful work session

Process

Step 1: Identify learnings

Scan the current session for compoundable insights. Look for:

Extract 1-3 learnings max. Quality over quantity. If nothing is worth saving, say so:

"Nothing from this session seems worth saving as a standalone learning. The work is captured in the plan/deliverables."

For each learning, draft:

**Learning:** [One sentence — what we now know]
**Type:** [insight | playbook | correction | pattern]
**Why it matters:** [One sentence — how this changes future work]

Step 2: Get user approval

Present the drafted learnings and ask:

"Found [N] learnings worth saving. Review and approve?"

Show each learning with its classification. User can:

• Approve as-is

• Edit the wording

• Skip individual learnings

• Add learnings you missed

Do not save anything without approval.

Step 3: Check for duplicates

For each approved learning, search existing knowledge:

Grep: [key phrases] in docs/knowledge/
Grep: [key phrases] in docs/solutions/

If a similar learning already exists:

• Show the existing entry

• Ask: "Update existing or save as new?"

• If updating, edit the existing file

Step 3.5: Check for stale knowledge

After identifying what to save, launch the stale knowledge checker:

Launch Task agent: compound-knowledge:research:stale-knowledge-checker

• Pass: the new learning(s) being saved
• Returns: existing entries that may be contradicted or superseded

If stale entries are found, present them to the user:

"This new learning may conflict with existing knowledge:

• [existing file] says [X], but the new learning says [Y]
• Recommendation: [Update / Remove / Keep both]

Want me to update the old entry?"

Step 4: Save locally

Write each learning to docs/knowledge/:

Filename: docs/knowledge/{descriptive-slug}.md

Create the directory if it doesn't exist: mkdir -p docs/knowledge/

File format:

---
type: [insight | playbook | correction | pattern]
tags: [relevant keywords for future search]
confidence: [high | medium | low]
created: [today's date]
source: [brief description of what triggered this]
---

# [Learning Title]

[2-4 sentences explaining the learning. Be specific enough that someone reading this in 3 months understands what happened and why it matters.]

## Context

[What you were doing when you discovered this.]

## Implication

[How this should change future work. Be concrete: "When doing X, always check Y first."]

Step 5: Confirm and offer next steps

## Compounded

**Saved:**
- docs/knowledge/{filename}.md

**This learning will be surfaced by /kw:plan** when future work touches:
- [list the tags that would trigger retrieval]

Use AskUserQuestion:

Question: "Learnings saved. What next?"

Options:

1. Run /kw:plan — Start a new planning cycle (the learnings will be found)
2. Push to Proof — Share the learnings for team review
3. Done — Session complete

Important Rules

• 1-3 learnings max per session. If you're saving 5 things, you're not filtering enough.

• Approval required. Never auto-save. The user decides what's worth remembering.

• Be specific. "Use the right data source" is useless. "Revenue metrics come from [specific dashboard], not [other source] which overcounts by ~$X" is useful.

• Duplicates are waste. Always check before creating. Update existing entries when possible.

• Confidence matters. Mark low if based on one data point. Mark high if verified across multiple sessions or with data.

• Tags are for retrieval. Choose tags that /kw:plan's grep search would match on. Think: "What future question would this answer?"

Pipeline Mode

When invoked with disable-model-invocation context (e.g., from an orchestrator or automation):

• Skip all AskUserQuestion prompts
• Use sensible defaults for all choices
• Write output files without waiting for confirmation
• Proceed to the next suggested skill automatically
• Output structured results that the calling context can parse