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. Required specialist execution: Invoking this recipe is the user's explicit instruction and authorization to execute every named specialist whose condition applies. Execute each applicable Agent call with its declared subagent_type when its prerequisites are met and continue from its returned result; equivalent orchestrator work does not complete that step
2. Exact specialist handoff: The complete Agent prompt consists of all and only the applicable canonical field: value entries declared by the specialist's Input Contract. Copy each value unchanged from its authoritative source; serialize path fields as path strings so the specialist reads referenced artifacts directly
3. Follow the reflection flow defined below
4. Approval gate: At [STOP — BLOCKING], present the distillation decision and resume after explicit user confirmation

Workflow

Determine the reflection level → record results → independently distill applicable evidence → confirm promotions → update artifacts and index.

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

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?

3. Knowledge Distillation

Invoke knowledge-distiller using Agent tool (subagent_type: "discover:knowledge-distiller") with exact scope_type, opportunity_ids, and hypothesis_paths fields for pattern extraction:

• knowledge-distiller operates in a separate context so orchestrator synthesis does not replace direct source inspection
• 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

Write learnings and apply Tier promotions after explicit user confirmation.

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 independent supporting evidence strong enough for a product-level rule
• State the exact segments or conditions covered by that 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

Scope Boundaries

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

Completion Criteria

• Result evidence is recorded at the applicable reflection level
• Level 2/3 patterns and promotions come from knowledge-distiller and user confirmation
• Modified learnings have current freshness tags and docs/discovery/INDEX.md reflects the result