Ln 230 Story Prioritizer

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

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):

Research Tools

Workflow

Phase 1: Discovery (2 min)

Objective: Validate input and prepare context.

Process:

1. Parse Epic input:

   • Accept: Epic ID, "Epic N", or Linear Project URL
   • Query: get_project(query=epic)
   • Extract: Epic ID, title, description

2. Auto-discover configuration:

   • Read docs/tasks/kanban_board.md for Team ID
   • Slugify Epic title for output path

3. Check existing prioritization:

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

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

4. 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:

1. Query Stories from Epic:

   list_issues(project=Epic.id, label="user-story")
   

2. Extract metadata only:

   • Story ID, title, status
   • DO NOT load full descriptions yet

3. Filter Stories:

   • Exclude: Done, Cancelled, Archived
   • Include: Backlog, Todo, In Progress

4. 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:

Step 3.4: Calculate RICE Score

RICE = (Reach x Impact x Confidence) / Effort

Reach (1-10): Users affected per quarter

Impact (0.25-3.0): Business value

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

Effort (1-10): Person-months

Step 3.5: Determine Priority

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:

1. Sort results:

   • Primary: Priority (P0 → P3)
   • Secondary: RICE score (descending)

2. Generate markdown:

   • Use template from references/prioritization_template.md
   • Fill: Priority Summary, Main Table, RICE Details, Sources

3. 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

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

1. Source all data - Every Market number needs source + date
2. Prefer recent data - 2024-2025, warn if older
3. Cross-reference - 2+ sources for Market size (reduce error)
4. Time-box strictly - Skip depth for speed if needed
5. Confidence levels - Mark High/Medium/Low for estimates
6. No speculation - Only sourced claims, note "[No data]" gaps
7. One Story at a time - Token efficiency critical
8. 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):

Reference Files

Version: 1.0.0 Last Updated: 2025-12-23