Lp Prioritize

lp-prioritize: Startup Go-Item Ranking

Purpose: Score all candidate go-items (features, experiments, distribution bets, content) and select top 2-3 to pursue.

Invocation

/lp-prioritize --business <BIZ>

Business resolution pre-flight: If --business is absent or the directory docs/business-os/strategy/<BIZ>/ does not exist, apply _shared/business-resolution.md before any other step.

Optional flags:

• --max <N> - select top N items (default 2-3)
• --backlog <PATH> - include existing backlog items

Operating Mode

READ + RANK + RECOMMEND

This skill reads candidate go-items from upstream outputs (lp-readiness, lp-offer, lp-forecast), scores them on 3 dimensions, ranks by combined score, and selects top 2-3 with rationale.

Differs from lp-do-idea-generate

CRITICAL: This is NOT a renamed lp-do-idea-generate. Key differences:

1. Simple rank-and-pick vs 7-stage pipeline: No Cabinet Secretary, no multi-lens expert passes, no clustering, no Munger/Buffett filter. Just: list candidates → score → rank → pick top 2-3.

2. 3 scoring dimensions vs 12+ sub-experts: Scores by effort, impact, and learning-value only. No persona-based expert evaluation (no Product Strategist, no Growth Hacker, no Financial Analyst, etc.).

3. Includes experiment + distribution candidates: lp-do-idea-generate focuses on business ideas. lp-prioritize ranks ALL go-items: product features, experiments, distribution bets, content pieces, operational improvements.

4. Direct output to lp-do-fact-find: Ranked list feeds directly into lp-do-fact-find for the top items. No card creation, no idea persistence, no DGPs.

5. 100-150 lines vs 1200+ lines: Deliberately lightweight for startups with 5-10 candidates, not enterprise backlogs with hundreds of items.

Inputs

Required:

• Business context: current stage, constraints, budget, team size
• Candidate go-items from:
  • lp-readiness output (capability gaps, quick wins)
  • lp-offer output (feature candidates, positioning experiments)
  • lp-forecast output (distribution experiments, content bets)
  • Existing backlog (if provided via --backlog)

Optional (S3B):

• docs/business-os/strategy/<BIZ>/lp-other-products-results.user.md — if present (human-produced after running the S3B prompt in a deep research tool), extract product candidates from the Track 4 top-5 shortlist as additional go-items. Score them through the standard Effort/Impact/Learning-Value rubric alongside feature and distribution bet candidates. Source label: S3B lp-other-products.

Candidate shape: Each item must have title, description, and source (which upstream output it came from).

Scoring Dimensions

Effort (1-5): How much work? Time, complexity, dependencies. Lower = better.

• 1 = <1 week, no dependencies, trivial complexity
• 3 = 2-4 weeks, moderate dependencies, medium complexity
• 5 = >8 weeks, heavy dependencies, high complexity

Impact (1-5): How much business value? Revenue potential, learning value, risk reduction. Higher = better.

• 1 = Minimal value, nice-to-have
• 3 = Moderate value, supports growth
• 5 = Game-changer, unlocks new market/revenue

Learning-Value (1-5): How much do we learn? Hypothesis validation, market signal, capability building. Higher = better.

• 1 = Low learning, known outcome
• 3 = Moderate learning, tests hypothesis
• 5 = High learning, validates core assumption

Combined score: (Impact + Learning-Value) / Effort

Higher combined score = better ROI on time/effort.

Hypothesis Portfolio Bridge (Optional)

When candidate records include explicit hypothesis linkage, /lp-prioritize can inject portfolio-normalized scoring:

• Linkage forms:
  • hypothesis_id: <id>
  • tag hypothesis:<id>
• Linked + portfolio metadata present:
  • score via hypothesis portfolio bridge mapping (normalized to 1-5)
• Linked + blocked hypothesis:
  • surface explicit blocked reason
  • apply neutral/zero injection (per bridge output) instead of silent fallback
• Linked + metadata missing:
  • do not fail run; keep baseline score and mark as metadata_missing
• Unlinked:
  • keep baseline formula unchanged

Reference implementation: scripts/src/hypothesis-portfolio/prioritize-bridge.ts

Workflow

Stage 1: Collect Candidates

• Read all upstream outputs (lp-readiness, lp-offer, lp-forecast)
• Extract candidate go-items (features, experiments, bets)
• Include backlog items if --backlog provided
• Normalize to standard shape (title, description, source)

Stage 2: Score Each Candidate

• For each item, assign:
  • Effort score (1-5)
  • Impact score (1-5)
  • Learning-Value score (1-5)
• Calculate combined score: (Impact + Learning-Value) / Effort
• Write brief rationale for each score

Stage 3: Rank by Combined Score

• Sort candidates by combined score (descending)
• Break ties by: Impact > Learning-Value > lower Effort

Stage 4: Select Top 2-3

• Take top N items (default 2-3, or --max if provided)
• For each selected item:
  • Write acceptance criteria (how we know it's done)
  • Estimate effort (person-weeks)
  • Identify dependencies
• For non-selected top items, write "Why not" notes

Stage 5: Produce Ranked Output

• Output markdown table with all scored items
• Output selected items with detail
• Output "Why not" notes for next-tier items

Output Contract

Ranked Table (all candidates):

| Rank | Item | Source | Effort | Impact | Learning | Score | Rationale |
|------|------|--------|--------|--------|----------|-------|-----------|
| 1    | ... | ...    | 2      | 5      | 4        | 4.5   | ...       |

Selected Items (top 2-3):

For each selected item:

• Title: [item name]
• Source: [which output it came from]
• Why selected: [rationale based on scores]
• Acceptance criteria: [how we know it's done]
• Estimated effort: [person-weeks]
• Dependencies: [what must be true first]

Why Not Selected (next-tier items):

For items ranked 4-6:

• Title: [item name]
• Why not: [reason not selected despite good score]

Quality Checks

Before outputting, verify:

• All candidates scored on 3 dimensions (effort, impact, learning-value)
• Combined scores calculated correctly
• Top 2-3 items have acceptance criteria and effort estimates
• "Why not" notes for next-tier items (if applicable)
• Rationale for each score is brief and concrete
• No ties in ranking (all ties broken by tiebreaker rule)

Red Flags

Invalid output:

• More than 5 items selected (defeats focus)
• No rationale for scores (just numbers)
• No acceptance criteria for selected items
• Effort/Impact/Learning-Value scores outside 1-5 range
• Combined score not calculated as (Impact + Learning-Value) / Effort

Integration

Downstream: Output feeds directly into /lp-do-fact-find for the selected top 2-3 items. Each selected item becomes a fact-find target.

Upstream: Reads from:

• /lp-readiness (capability gaps, quick wins)
• /lp-offer (feature candidates, positioning experiments)
• /lp-forecast (distribution experiments, content bets)

Persistence: No card creation, no idea persistence. This is a one-shot ranking for current loop cycle. Next cycle re-runs from scratch.

Example Invocation

/lp-prioritize --business BRIK --max 3

Expected output:

1. Ranked table of all candidates (8-12 items typical)
2. Top 3 selected items with acceptance criteria and effort
3. "Why not" notes for items 4-6
4. Ready to pipe selected items into /lp-do-fact-find