recipe-reflect

Context: Drive the feedback loop by reflecting on outcomes, updating target artifacts, and distilling learnings across the knowledge pyramid (see product-principles skill for Tier definitions).

Orchestrator Definition

Execution Protocol:

1. Delegate distillation to knowledge-distiller (via Agent tool, subagent_type: "discover:knowledge-distiller") for unbiased pattern extraction
2. Follow the reflection flow defined below
3. Stop at every [STOP — BLOCKING] marker — present findings and CANNOT proceed until user explicitly confirms

Workflow Overview

Input (reflection trigger — hypothesis concluded, Opportunity review, or periodic)
    ↓
1. Scope Assessment → Determine reflection level (hypothesis / Opportunity / Vision)
    ↓
2. Result Recording → Update target artifacts with outcomes
    ↓
3. Knowledge Distillation → knowledge-distiller extracts patterns [Stop: Distillation review]
    ↓
4. Knowledge Promotion → Tier 2 → Tier 1 if criteria met
    ↓
5. Index Update → Update INDEX.md
    ↓
Output: Updated artifacts + learnings + INDEX.md

Execution Decision Flow

1. Scope Assessment

Input: $ARGUMENTS

Determine the reflection level (see references/reflection-guide.md):

2. Result Recording

Level 1: Hypothesis Reflection

1. Verify the hypothesis file has been updated with results (validation results, confidence scores, evidence)
2. Document learnings: What did we learn regardless of outcome?
3. Check if this result changes understanding of the parent Opportunity

Level 2: Opportunity Reflection

1. Gather all hypotheses under the target Opportunity
2. Prepare context for knowledge-distiller (hypothesis summaries, results, confidence changes)

Level 3: Vision Reflection

1. Gather cross-Opportunity data
2. Review Product Outcomes — are targets still correct?
3. Review NSM — still the right connecting metric?
4. Prepare context for knowledge-distiller

3. Knowledge Distillation

Invoke knowledge-distiller using Agent tool (subagent_type: "discover:knowledge-distiller") for pattern extraction:

• knowledge-distiller operates in a separate context to avoid individual hypothesis bias
• It analyzes multiple hypotheses to find patterns, contradictions, and trends
• It proposes Tier 2 learnings (for Opportunity) or Tier 1 promotions (for Vision)
• It enforces distillation quality criteria (per product-principles skill)

[STOP — BLOCKING] Present distillation results to user for review:

• Extracted patterns and trends
• Proposed learnings (Tier 2 or Tier 1)
• Contradictions found (these become priority Discovery targets)
• Tier promotion proposals with supporting evidence

CANNOT write learnings or promote Tiers until user explicitly confirms.

4. Knowledge Promotion

After user approval:

Tier 3 → Tier 2

• Write learnings to the Opportunity file's "Tier 2 Learnings" section
• Include hypothesis references that support each learning

Tier 2 → Tier 1

• Write to docs/product/learnings.md
• Include freshness tag (last-validated: YYYY-MM-DD)
• Include supporting hypothesis references (3+ required)
• Include cross-segment evidence

5. Index Update

Update docs/discovery/INDEX.md with:

• Hypothesis status summary (counts by status)
• Opportunity-to-hypothesis mapping
• Recent validation results
• Tier 1 learning changes (if any)
• Last updated timestamp

Sub-agent Usage

Scope Boundaries

Included: Result recording, knowledge distillation, Tier promotion, INDEX.md maintenance Not included: Hypothesis validation, new hypothesis generation

Completion Criteria

• Reflection level determined
• Target artifacts updated with results
• knowledge-distiller invoked for pattern extraction (Level 2+)
• User reviewed distillation proposals
• Tier promotions applied (if approved)
• docs/discovery/INDEX.md updated
• Freshness tags current on modified learnings