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:

Type	Signals
Insight	"We discovered...", surprising finding, counter-intuitive result
Playbook	Repeatable process that worked, step-by-step that others could follow
Correction	Wrong assumption fixed, data source clarified, definition updated
Pattern	Something that keeps recurring, systemic observation

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Returns: existing entries that may be contradicted or superseded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?"

<critical_requirement> Agents return TEXT only. They must NOT write or delete files. Only the orchestrating compound skill writes files — both new learnings and updates to stale entries. </critical_requirement>

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:

Run /kw:plan — Start a new planning cycle (the learnings will be found)
Push to Proof — Share the learnings for team review
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

kw:compound