generate-hypotheses

name	generate-hypotheses
description	Use when evaluation data exists and optimization proposals are needed. Analyzes scores, user journey drop-off points, and prior attempts to produce ranked hypotheses batched 2-3 per cycle.
argument-hint	[count] - Optional: number of hypotheses to generate (default: 5, max: 10)

Generate Hypotheses

Analyze evaluation data and produce ranked optimization proposals. Batches the top 2-3 hypotheses per evolution cycle for implementation.

Instructions

CRITICAL — NON-INTERACTIVE: Execute all steps without pausing. Never ask questions, request confirmation, or wait for input. Read state, analyze, generate, write output, update state, stop.

---

Step 0: Initialize

1. Read factory/evolution-state.json:

   • Verify status is "hypothesizing" or "rejected" (if rejected, we're retrying with next batch)
   • Set CYCLE to current_cycle
   • If status is neither, display error and stop

2. Parse $ARGS for optional count (default: 5, max: 10)

3. Read the latest evaluation:

   • Find the most recent file in factory/evaluations/eval-*.json
   • Load its scores, top_issues, and user_journeys

4. Read hypothesis history:

   • Read all files in factory/hypotheses/hyp-*.json
   • Read rejected_hypotheses from evolution state
   • Build a set of previously attempted and rejected approaches

---

Step 1: Analyze Evaluation Data

For each issue in top_issues, map it to a concrete improvement:

1. Identify the root cause — what in the codebase is causing this score deficit?
2. Define the fix — what specific change would address it?
3. Estimate impact — how many composite score points could this recover?
4. Assess confidence — how certain are we this fix will work? (0.0-1.0)
5. Estimate effort — "small" (1-2 tasks), "medium" (3-5 tasks), "large" (6+ tasks)

Additionally, analyze user journey drop-off points:

• Each drop-off point maps to at least one hypothesis
• Drop-off points get +2 to expected_impact

---

Step 2: Score and Rank

For each hypothesis, calculate priority:

effort_multiplier = { "small": 1, "medium": 2, "large": 3 }

priority_score = (expected_impact * confidence) / effort_multiplier

Apply modifiers:

• Journey drop-off: expected_impact += 2 (already applied in Step 1)
• Multi-journey issue (appears in 2+ journeys): confidence *= 1.2 (cap at 1.0)
• Similar to rejected hypothesis: confidence *= 0.5
• Previously attempted (same approach, different cycle): skip entirely

Sort by priority_score descending.

---

Step 3: Batch Top Hypotheses

Select the top 2-3 hypotheses for this cycle:

1. Take the highest-priority hypothesis
2. Add the next highest that does NOT conflict (same files, overlapping changes)
3. If a third doesn't conflict with the first two, include it
4. If all remaining conflict, batch only 2

A hypothesis conflicts if it modifies the same primary files as an already-batched hypothesis in a way that could cause merge issues.

---

Step 4: Generate Implementation Sketches

For each batched hypothesis, produce an implementation sketch:

{
  "pages_affected": ["src/app/page.tsx"],
  "new_components": ["src/components/SocialProof.tsx"],
  "modifications": ["src/app/layout.tsx"],
  "estimated_tasks": 3,
  "key_changes": [
    "Add testimonial data to homepage",
    "Create SocialProof component with carousel",
    "Add trust badges below hero section"
  ]
}

Read the actual source files referenced to ensure the sketch is grounded in the real codebase.

---

Step 5: Write Output Files

Generate a timestamp: TIMESTAMP = current ISO-8601 datetime formatted as YYYY-MM-DD-HHmmss

5A: JSON Output

Write factory/hypotheses/hyp-{CYCLE}-{TIMESTAMP}.json:

{
  "version": 1,
  "timestamp": "[ISO-8601]",
  "cycle": 1,
  "evaluation_ref": "factory/evaluations/eval-[latest].json",
  "total_generated": 5,
  "batched_count": 3,
  "hypotheses": [
    {
      "id": "hyp-1-001",
      "title": "Add social proof section below hero",
      "category": "conversion",
      "description": "Homepage lacks trust signals. Adding testimonials, logos, and stats below the hero would increase trust and reduce bounce rate.",
      "rationale": "Conversion audit scored trust_credibility at 2/5. User journey 'New visitor' drops off before CTA due to lack of social validation.",
      "expected_impact": 8,
       
       
       
       
       
        
       
       
         
         
      
       
         
         
         
         
         
          
          
          
        
      
    
  
   
    
       
       
       
       
    
  

5B: Markdown Report

Write factory/hypotheses/hyp-{CYCLE}-{TIMESTAMP}.md:

# Optimization Hypotheses — Cycle [N]

**Project:** [name]
**Date:** [date]
**Current Score:** [X]/100
**Evaluation:** [ref]

---

## Batched for This Cycle (2-3)

### 1. [Title] ⭐ Priority: [score]
- **Category:** [dimension]
- **Impact:** [expected_impact]/10 | **Confidence:** [confidence] | **Effort:** [effort]
- **Addresses:** [issue IDs], [journey names]
- **Predicted Score Change:** [composite delta]
- **Rationale:** [why this will work]
- **Key Changes:**
  - [change 1]
  - [change 2]

### 2. [Title] ...

---

## Other Candidates (Not Batched)

| Rank | Title | Priority | Reason Not Batched |
|------|-------|----------|-------------------|
| 4 | [title] | [score] | Conflicts with #1 |
| 5 | [title] | [score] | Lower priority |

---

## Skipped

| Title | Reason |
|-------|--------|
| [title] | Similar to rejected hyp-0-002 |

---

## Next: Run /plan-optimization to convert these into a stage plan

---

Step 6: Update Evolution State

1. Read factory/evolution-state.json
2. Update status to "planning"
3. Write back

---

Step 7: Display Summary

================================================================
  Hypotheses Generated
================================================================

  Project:     [name]
  Cycle:       [N]
  Generated:   [total] hypotheses
  Batched:     [count] for this cycle

  Batched Hypotheses:
    1. [title] — priority [score], predicted +[X] score
    2. [title] — priority [score], predicted +[X] score
    3. [title] — priority [score], predicted +[X] score

  Output:
    JSON: factory/hypotheses/hyp-{CYCLE}-{TIMESTAMP}.json
    Report: factory/hypotheses/hyp-{CYCLE}-{TIMESTAMP}.md

  Next: Run /plan-optimization to create the stage plan
================================================================

---

Error Recovery

Scenario	Recovery
No evaluation files	Display error: "Run /evaluate-product first"
All hypotheses conflict	Batch only the top 1
All hypotheses previously rejected	Generate from secondary issues, widen scope
Evaluation has no top_issues	Generate hypotheses from lowest-scoring dimensions
Context exhausted mid-generation	Runner re-invokes; check for partial output, resume