ln-230-story-prioritizer

仓库: levnikolaevich/claude-code-skills
安装量: 109
排名: #7793

安装

npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-230-story-prioritizer

Evaluate Stories using RICE scoring with market research. Generate consolidated prioritization table for Epic.

Purpose & Scope

  • Prioritize Stories AFTER ln-220 creates them

  • Research market size and competition per Story

  • Calculate RICE score for each Story

  • Generate prioritization table (P0/P1/P2/P3)

  • Output: docs/market/[epic-slug]/prioritization.md

When to Use

Use this skill when:

  • Stories created by ln-220, need business prioritization

  • Planning sprint with limited capacity (which Stories first?)

  • Stakeholder review requires data-driven priorities

  • Evaluating feature ROI before implementation

Do NOT use when:

  • Epic has no Stories yet (run ln-220 first)

  • Stories are purely technical (infrastructure, refactoring)

  • Prioritization already exists in docs/market/

Who calls this skill:

  • User (manual) - after ln-220-story-coordinator

  • Future: ln-200-scope-decomposer (optional Phase)

Input Parameters

| epic | Yes | Epic ID or "Epic N" format | -

| stories | No | Specific Story IDs to prioritize | All in Epic

| depth | No | Research depth (quick/standard/deep) | "standard"

depth options:

  • quick - 2-3 min/Story, 1 WebSearch per type

  • standard - 5-7 min/Story, 2-3 WebSearches per type

  • deep - 8-10 min/Story, comprehensive research

Output Structure

docs/market/[epic-slug]/
└── prioritization.md    # Consolidated table + RICE details + sources

Table columns (from user requirements):

| P0 | User pain point | Story title | Technical approach | Why important | Business impact | $XB | [Link] | Blue 1-3 / Red 4-5

Research Tools

| WebSearch | Market size, competitors | "[domain] market size 2025"

| mcp__Ref | Industry reports | "[domain] market analysis report"

| Linear | Load Stories | list_issues(project=Epic.id)

| Glob | Check existing | "docs/market/[epic]/*"

Workflow

Phase 1: Discovery (2 min)

Objective: Validate input and prepare context.

Process:

  • Parse Epic input:

Accept: Epic ID, "Epic N", or Linear Project URL

  • Query: get_project(query=epic)

  • Extract: Epic ID, title, description

  • Auto-discover configuration:

Read docs/tasks/kanban_board.md for Team ID

  • Slugify Epic title for output path

  • Check existing prioritization:

Glob: docs/market/[epic-slug]/prioritization.md

If exists: Ask "Update existing or create new?"

  • If new: Continue

  • Create output directory:

mkdir -p docs/market/[epic-slug]/

Output: Epic metadata, output path, existing check result

Phase 2: Load Stories Metadata (3 min)

Objective: Build Story queue with metadata only (token efficiency).

Process:

  • Query Stories from Epic:
list_issues(project=Epic.id, label="user-story")
  • Extract metadata only:

Story ID, title, status

  • DO NOT load full descriptions yet

  • Filter Stories:

Exclude: Done, Cancelled, Archived

  • Include: Backlog, Todo, In Progress

  • Build processing queue:

Order by: existing priority (if any), then by ID

  • Count: N Stories to process

Output: Story queue (ID + title), ~50 tokens/Story

Phase 3: Story-by-Story Analysis Loop (5-10 min/Story)

Objective: For EACH Story: load description, research, score RICE.

Critical: Process Stories ONE BY ONE for token efficiency!

Per-Story Steps:

Step 3.1: Load Story Description

get_issue(id=storyId, includeRelations=false)

Extract from Story:

  • Feature: Story title

  • Customer Problem: From "So that [value]" + Context section

  • Solution: From Technical Notes (implementation approach)

  • Rationale: From AC + Success Criteria

Step 3.2: Research Market Size WebSearch queries (based on depth):

"[customer problem domain] market size TAM 2025"
"[feature type] industry market forecast"

mcp__Ref query:

"[domain] market analysis Gartner Statista"

Extract:

  • Market size: $XB (with unit: B=Billion, M=Million)

  • Growth rate: X% CAGR

  • Sources: URL + date

Confidence mapping:

  • Industry report (Gartner, Statista) → Confidence 0.9-1.0

  • News article → Confidence 0.7-0.8

  • Blog/Forum → Confidence 0.5-0.6

Step 3.3: Research Competition WebSearch queries:

"[feature] competitors alternatives 2025"
"[solution approach] market leaders"

Count competitors and classify:

| 0 | 1 | Blue Ocean

| 1-2 | 2 | Emerging

| 3-5 | 3 | Growing

| 6-10 | 4 | Mature

| >10 | 5 | Red Ocean

Step 3.4: Calculate RICE Score

RICE = (Reach x Impact x Confidence) / Effort

Reach (1-10): Users affected per quarter

| 1-2 | <500 | Niche, single persona

| 3-4 | 500-2K | Department-level

| 5-6 | 2K-5K | Organization-wide

| 7-8 | 5K-10K | Multi-org

| 9-10 | >10K | Platform-wide

Impact (0.25-3.0): Business value

| 0.25 | Minimal | Nice-to-have

| 0.5 | Low | QoL improvement

| 1.0 | Medium | Efficiency gain

| 2.0 | High | Revenue driver

| 3.0 | Massive | Strategic differentiator

Confidence (0.5-1.0): Data quality (from Step 3.2)

Effort (1-10): Person-months

| 1-2 | <2 weeks | 3 AC, simple CRUD

| 3-4 | 2-4 weeks | 4 AC, integration

| 5-6 | 1-2 months | 5 AC, complex logic

| 7-8 | 2-3 months | External dependencies

| 9-10 | 3+ months | New infrastructure

Step 3.5: Determine Priority

| P0 (Critical) | >= 30 | OR Competition = 1 (Blue Ocean monopoly)

| P1 (High) | >= 15 | OR Competition <= 2 (Emerging market)

| P2 (Medium) | >= 5 | -

| P3 (Low) | < 5 | Competition = 5 (Red Ocean) forces P3

Step 3.6: Store and Clear

  • Append row to in-memory results table

  • Clear Story description from context

  • Move to next Story in queue

Output per Story: Complete row for prioritization table

Phase 4: Generate Prioritization Table (5 min)

Objective: Create consolidated markdown output.

Process:

  • Sort results:

Primary: Priority (P0 → P3)

  • Secondary: RICE score (descending)

  • Generate markdown:

Use template from references/prioritization_template.md

  • Fill: Priority Summary, Main Table, RICE Details, Sources

  • Save file:

Write: docs/market/[epic-slug]/prioritization.md

Output: Saved prioritization.md

Phase 5: Summary & Next Steps (1 min)

Objective: Display results and recommendations.

Output format:

## Prioritization Complete

**Epic:** [Epic N - Name]
**Stories analyzed:** X
**Time elapsed:** Y minutes

### Priority Distribution:
- P0 (Critical): X Stories - Implement ASAP
- P1 (High): X Stories - Next sprint
- P2 (Medium): X Stories - Backlog
- P3 (Low): X Stories - Consider deferring

### Top 3 Priorities:
1. [Story Title] - RICE: X, Market: $XB, Competition: Blue/Red

### Saved to:
docs/market/[epic-slug]/prioritization.md

### Next Steps:
1. Review table with stakeholders
2. Run ln-300 for P0/P1 Stories first
3. Consider cutting P3 Stories

Time-Box Constraints

| quick | 2-3 min | 20-30 min

| standard | 5-7 min | 50-70 min

| deep | 8-10 min | 80-100 min

Time management rules:

  • If Story exceeds time budget: Skip deep research, use estimates (Confidence 0.5)

  • If total exceeds budget: Switch to "quick" depth for remaining Stories

  • Parallel WebSearch where possible (market + competition)

Token Efficiency

Loading pattern:

  • Phase 2: Metadata only (~50 tokens/Story)

  • Phase 3: Full description ONE BY ONE (~3,000-5,000 tokens/Story)

  • After each Story: Clear description, keep only result row (~100 tokens)

Memory management:

  • Sequential processing (not parallel)

  • Maximum context: 1 Story description at a time

  • Results accumulate as compact table rows

Integration with Ecosystem

Position in workflow:

ln-210 (Scope → Epics)
     ↓
ln-220 (Epic → Stories)
     ↓
ln-230 (RICE per Story → prioritization table) ← THIS SKILL
     ↓
ln-300 (Story → Tasks)

Dependencies:

  • WebSearch, mcp__Ref (market research)

  • Linear MCP (load Epic, Stories)

  • Glob, Write, Bash (file operations)

Downstream usage:

  • Sprint planning uses P0/P1 to select Stories

  • ln-300 processes Stories in priority order

  • Stakeholders review before implementation

Critical Rules

  • Source all data - Every Market number needs source + date

  • Prefer recent data - 2024-2025, warn if older

  • Cross-reference - 2+ sources for Market size (reduce error)

  • Time-box strictly - Skip depth for speed if needed

  • Confidence levels - Mark High/Medium/Low for estimates

  • No speculation - Only sourced claims, note "[No data]" gaps

  • One Story at a time - Token efficiency critical

  • Preserve language - If user asks in Russian, respond in Russian

Definition of Done

Epic validated in Linear All Stories loaded (metadata, then descriptions per-Story) Market research completed (2+ sources per Story) RICE score calculated for each Story Competition index assigned (1-5) Priority assigned (P0/P1/P2/P3) Table sorted by Priority + RICE File saved to docs/market/[epic-slug]/prioritization.md Summary with top priorities and next steps Total time within budget

Example Usage

Basic usage:

ln-230-story-prioritizer epic="Epic 7"

With parameters:

ln-230-story-prioritizer epic="Epic 7: Translation API" depth="deep"

Specific Stories:

ln-230-story-prioritizer epic="Epic 7" stories="US001,US002,US003"

Example output (docs/market/translation-api/prioritization.md):

| P0 | "Repeat translations cost GPU" | Translation Memory | Redis cache, 5ms lookup | 70-90% GPU cost reduction | High | $2B+ | M&M | 3

| P0 | "Can't translate PDF" | PDF Support | PDF parsing + layout | Enterprise blocker | High | $10B+ | Eden | 5

| P1 | "Need video subtitles" | SRT/VTT Support | Timing preservation | Blue Ocean opportunity | Medium | $5.7B | GMI | 2

Reference Files

| prioritization_template.md | Output markdown template

| rice_scoring_guide.md | RICE factor scales and examples

| research_queries.md | WebSearch query templates by domain

| competition_index.md | Blue/Red Ocean classification rules

Version: 1.0.0 Last Updated: 2025-12-23

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