Pivot Engine

Skill: pivot-engine

Purpose

When an idea scores poorly in one or more dimensions, generate concrete pivot options rather than abandoning the idea entirely. A pivot is a deliberate change in one variable to improve the weakest dimension — not a complete restart. The best pivots preserve what's already strong while fixing what's broken, and they're grounded in real market signals, not wishful thinking.

Input

• Idea slug
• memory/ideas/<slug>/scores.json (current scores — required)
• memory/ideas/<slug>/weaknesses.json (weak dimensions with root causes — required in standard mode, optional in micro mode)
• memory/ideas/<slug>/idea.md (current concept, business_model)
• memory/ideas/<slug>/competitors.json (positioning gaps, competitor complaints)
• memory/ideas/<slug>/distribution.json (current channel assessment)
• memory/ideas/<slug>/pricing.json (current pricing model and WTP)
• memory/ideas/<slug>/retention.json (current retention assessment)
• memory/user_profile.md (ICP tier, budget, time, distribution advantages)
• memory/market_insights/<niche>-*-<YYYY>-<MM>.md (trend data — use all available platform files)

Using Market Insights

Market insights are essential for generating pivots grounded in real demand rather than theory. Extract:

Pivot Types

The business_model in idea.md decides which rows apply: the three B2B rows are for prosumer, b2b-smb, and b2b2c ideas; a pivot that changes business_model itself counts as one variable and must be flagged as such.

Micro-pivot Mode

When the user names the variable themselves — "what if I charged per client instead of per seat", "what if I sold to agencies instead of end users", "what if I led with the export feature" — there is nothing to diagnose. Run micro mode:

Everything else is unchanged: the option must still pass the Minimum Viable Pivot Criteria, the scoring simulation, and the indie buildability filter. Micro mode is a shortcut through diagnosis, not through rigour — if the named change fails the criteria, say so plainly and explain what would have to change instead.

Slug Rule

A pivot either updates the idea in place or becomes a new idea. Count what changes:

<pivot-word> is one or two words naming the change (habit-tracker-climbers-b2b, invoice-app-agencies). Keep the combined slug within 40 characters.

Do not copy dimension files into the new directory. A pivot big enough to earn a new slug changes the competitive set, the pricing anchors, and the channels, so those files are researched again; idea.md is the only inheritance. Never delete the original directory — the paused idea is the record of what was tried.

Minimum Viable Pivot Criteria

A pivot must be different enough to change the score but similar enough to preserve existing work and insights. Apply the Same Idea Test:

A valid pivot must meet ALL of these:

1. Changes exactly 1–2 variables from the list above. Changing 3+ variables is a new idea, not a pivot — create a new idea slug instead.
2. Preserves at least one strong dimension (score ≥ 60 in the original). A pivot that abandons all strengths is a restart.
3. Has evidence — the pivot direction is supported by at least one concrete signal (market_insights data, competitor gap, user complaint, trend).
4. Addressable root cause — the weakness it targets has root_cause_type of "addressable" or "situational" in weaknesses.json. Pivots cannot fix structural weaknesses unless the pivot changes the core mechanic (which usually means it's a new idea).

A pivot is NOT valid if:

• It requires the founder to acquire a skill set they don't have and can't learn in < 4 weeks
• It targets a completely different audience AND a different problem (that's a new idea)
• The only evidence is "maybe this would work" with no supporting signal
• It makes the idea harder to build without a proportional improvement in market viability

Process

Step 1 — Load and assess weakness landscape

1. Load weaknesses.json. Identify all weak dimensions and their root causes.
2. Separate weaknesses into:
   • Addressable (root_cause_type = "addressable" or "situational") → these are pivot targets
   • Knowledge gaps (root_cause_type = "knowledge-gap") → these need more research, not a pivot
   • Structural (root_cause_type = "structural") → only fixable by major pivot or new idea
3. If overall_weakness_severity = "fatal" and all weaknesses are structural, recommend dropping rather than pivoting. Note this in the output.

Step 2 — Map weak dimensions to pivot types

Use this mapping to identify which pivot types are most likely to improve each weak dimension:

If multiple dimensions are weak, prioritize the one with the lowest score AND an addressable root cause.

Step 3 — Generate pivot options using market_insights

For each applicable pivot type (from Step 2), generate a concrete option:

1. Pull evidence from market_insights:

   • For audience pivots: look for specific user segments mentioned in Reddit discussions or TikTok content
   • For niche pivots: identify adjacent niches with rising trend_velocity
   • For distribution pivots: check which channels show actual activity for the niche in market_insights
   • For pricing pivots: reference monetization_evidence and competitor pricing from competitors.json

2. Be specific, not generic:

   • Bad: "Narrow the audience"
   • Good: "Target freelance designers who use Figma (150K+ active in r/FigmaDesign) instead of all designers. This segment has higher WTP ($8–12/mo based on competitor pricing), active Reddit communities for organic distribution, and specific pain points around invoice management that existing tools ignore."

3. Generate 2–3 options, ranked by expected impact. Each option must pass the Minimum Viable Pivot Criteria.

Step 4 — Scoring simulation

For each pivot option, simulate the expected score change by projecting how each dimension would shift. This gives the orchestrator enough information to decide whether a full re-score is warranted.

Simulation method

For the dimension(s) the pivot targets, estimate the new score range based on:

• The pivot direction and its evidence strength
• Category benchmarks from the rubrics in idea-scoring
• What the market_insights data suggests about the pivot target

For dimensions the pivot doesn't target, assume they remain unchanged unless there's a clear secondary effect:

• Audience narrowing typically improves Distribution (+5 to +15) but may reduce Demand (-5 to -10) and Market Size
• Feature simplification typically improves Founder-Market Fit (+10 to +20) but may reduce Retention (-5 to -10) if features drove stickiness
• Pricing pivots can affect Distribution (freemium → more installs) and Retention (subscription → commitment)

Projected score calculation

projected_score = sum of (projected_dimension_scores × weights) × projected_floor_penalty

Use the same weights and floor penalty logic from idea-scoring. This is an estimate — the actual re-score (step 4 of the pivot-optimization workflow) will be definitive.

Report as a range: projected_score_range: { low: X, high: Y }.

Step 5 — Effort estimation

Each pivot has an execution cost. Estimate it relative to the founder's tier:

Tier adjustment on effort

A pivot with "high" effort for the founder's tier should be flagged as risky — the time investment may not be justified unless the projected score improvement is substantial (≥ 20 points).

Step 6 — Indie buildability filter

Before finalizing, verify each pivot option passes these constraints:

If a pivot fails any constraint, either modify it to fit or discard it and note why.

Step 7 — Select recommended pivot

Rank remaining options by: (projected_score_improvement / effort_level) — the best pivot is the one with the highest score impact per unit of effort.

Tie-breakers:

1. Stronger evidence from market_insights
2. Lower risk (fewer dimensions negatively affected)
3. Preserves more of the original idea's strengths

Output

Write two files to memory/ideas/<slug>/:

1. pivot_options.json

Machine-readable structured data for downstream skills (idea-scoring, decision-memo):

{
  "idea_slug": "",
  "generated_at": "YYYY-MM-DD",
  "lane": "b2c | b2b",
  "pivot_scope": "micro | standard",
  "slug_decision": "in-place | new-slug",
  "new_slug": null,
  "original_score": 0,
  "original_verdict": "",
  "triggered_by_weaknesses": [
    {
      "dimension": "",
      "score": 0,
      "root_cause_type": "",
      "root_cause_description": ""
    }
  ],
  "structural_weaknesses_unpivotable": [],
  "pivot_options": [
    {
      "pivot_id": "pivot-1",
      "pivot_type": "",
      "description": "",
      "specific_change": "",
      "evidence": "",
      "evidence_source": "",
      "meets_minimum_viable_pivot": true,
      "scoring_simulation": {
        "dimensions_improved": [
          { "dimension": "", "current": 0, "projected_low": 0, "projected_high": 0 }
        ],
        "dimensions_worsened": [
          { "dimension": "", "current": 0, "projected_low": 0, "projected_high": 0 }
        ],
        "dimensions_unchanged": [],
        "projected_score_range": { "low": 0, "high": 0 },
        "projected_verdict_range": ""
      },
      "effort": {
        "level": "low | medium | high",
        "tier_adjusted_level": "low | medium | high",
        "timeline": "",
        "what_changes": "",
        "what_stays": ""
      },
      "indie_buildability": {
        "passes": true,
        "constraints_checked": ["solo_buildable", "budget_feasible", "time_feasible", "skill_feasible", "sales_cycle_guard"],
        "failed_constraints": []
      },
      "trade_offs": [],
      "variables_changed": 0
    }
  ],
  "recommended_pivot": "",
  "recommended_pivot_rationale": "",
  "drop_recommendation": false,
  "drop_rationale": "",
  "market_insights_sources_used": []
}

2. pivot_report.md

Human-readable pivot brief. This is the document the founder actually reads. Write it after pivot_options.json is complete — source all data from the JSON, don't introduce new judgements.

Structure

---
idea_slug: <slug>
original_score: <X>
original_verdict: <verdict>
recommended_pivot: <pivot_id>
created_at: <YYYY-MM-DD>
---

# Pivot Report: <Idea Name>

## Why the Original Idea Scored <X>/100

<2–3 sentences. State the root cause of the low score plainly — not a list of every problem, just the one or two structural reasons the idea can't work as-is. Cite specific scores from scores.json (e.g. "Distribution scored 34/100 because…"). No hedging.>

---

## What Can Be Fixed vs. What Can't

**Addressable weaknesses** (pivot targets):
- <dimension>: <1-sentence root cause and why it's fixable>
- ...

**Structural weaknesses** (cannot be pivoted away):
- <dimension>: <1-sentence root cause and why no pivot can fix it>
- ...

<If all weaknesses are structural, state clearly that a pivot is unlikely to rescue this idea and explain why.>

---

## Pivot Options

### Option 1 — <Pivot Type>: <Short Name> · Projected score: <low>–<high>/100 · Effort: <tier_adjusted_level>

**The change:** <1–2 sentences. Be specific about exactly what changes — audience, feature, channel, pricing model, platform. Name concrete details (specific subreddits, competitor pricing, App Store keywords, etc.).>

**Why this works:** <2–3 sentences grounded in evidence. Cite the market_insights signal, competitor gap, or user complaint that supports this direction. Name the source (e.g. "r/FigmaDesign has 150K active members discussing invoice pain", "BookPal's 1-star reviews consistently mention X", "TikTok hashtag #X has 40M views and rising velocity").>

**What stays the same:** <1 sentence. What existing work and strengths are preserved.>

**Score projection:**
| Dimension | Current | Projected |
|---|---|---|
| <dimension> | <current>/100 | <low>–<high>/100 |
| <dimension (worsened)> | <current>/100 | <low>–<high>/100 |
| ... | ... | unchanged |

**Trade-offs:** <1–2 sentences. What this pivot gives up. Be honest — every pivot has a cost.>

**Effort:** <What specifically needs to change — code, copy, positioning, research. Timeline.>

---

### Option 2 — <Pivot Type>: <Short Name> · Projected score: <low>–<high>/100 · Effort: <tier_adjusted_level>

<Same structure as Option 1.>

---

### Option 3 — <Pivot Type>: <Short Name> · Projected score: <low>–<high>/100 · Effort: <tier_adjusted_level> *(optional)*

<Same structure as Option 1. Include only if a genuinely distinct third option exists.>

---

## Recommendation

**Go with Option <N> — <Short Name>.**

<3–4 sentences. State why this option has the best impact-to-effort ratio. Reference the scoring simulation. Name the one thing that makes this pivot more credible than the alternatives (the market signal, the competitor gap, the distribution advantage). End with a specific first action the founder should take this week.>

**If this pivot also scores below 50:** <1 sentence — what that means and what to do (drop, major rethink, or new idea slug).>

---

## What to Do First

<1–3 concrete steps, ordered. Each step should be doable within a week. No vague advice — name the specific subreddit, pricing change, App Store keyword, or feature to cut. If a RAT experiment makes sense before committing to the pivot, define it here: ≤2 weeks, ≤$100, pass/fail criteria.>

Writing rules for pivot_report.md

1. Every claim cites a source. Name the market_insights file, competitor data point, or dimension score behind every assertion. "Demand is weak" is not a claim — "Demand scored 36/100 because search volume for 'invoice app for freelancers' is 2,400/mo, below the 10K threshold for a viable indie SOM" is.
2. Options must be meaningfully distinct. Don't generate three variations of the same audience pivot. Each option should change a different variable.
3. Score projections must be ranges, not false precision. 62–71/100 is honest. 67/100 implies certainty the model doesn't have.
4. The recommendation must pick one. Not "it depends" — commit to the best option and explain why.
5. Total length: ~500–800 words. Cut anything that doesn't help the founder decide. This is a decision brief, not a research report.

Notes

• If overall_weakness_severity = "fatal" and all weaknesses are structural, set drop_recommendation = true and explain why no pivot can save this idea. Still generate 1 option as a "Hail Mary" if the founder wants to try, but be honest about the odds.
• When generating niche pivots, always check competitors.json for positioning_gaps — an identified gap with evidence is the strongest pivot foundation.
• Pivot options that combine two small changes (e.g., audience narrowing + pricing change) are allowed as a single option if both changes are "low" effort. Call this out as a compound pivot and flag the higher risk.
• The pivot_id field is used by idea-scoring to link re-scores in pivot_scores.json back to the specific option.
• pivot_scope: micro requires exactly one entry in pivot_options with variables_changed: 1; the harness rejects anything else. slug_decision: new-slug requires new_slug to be filled.
• If market_insights files are past their stale_after date, note that pivot evidence may be outdated and recommend refreshing trend analysis before committing to a pivot direction.
• Only addressable_weaknesses from weaknesses.json are pivot targets. A knowledge_gaps entry means a dimension file is missing or thin: send it back to that research skill rather than pivoting around it.