Paths: File paths (shared/, references/, ../ln-*) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root.

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:

• ln-200-scope-decomposer Phase 4 (optional, sequential per Epic)
• User (manual) - standalone after ln-220-story-coordinator

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):

Inputs

Resolution: Epic Resolution Chain. Status filter: Active (planned/started)

Tools Config

MANDATORY READ: Load shared/references/tools_config_guide.md, shared/references/storage_mode_detection.md, shared/references/input_resolution_pattern.md

Extract: task_provider = Task Management → Provider

Research Tools

Workflow

Phase 1: Discovery (2 min)

Objective: Validate input and prepare context.

Process:

1. Resolve epicId: Run Epic Resolution Chain per guide.

2. Load Epic details:

   • IF task_provider == "linear": get_project(query=epicId)
   • ELSE: Read("docs/tasks/epics/epic-{N}-*/epic.md")
   • Extract: Epic ID, title, description

3. Auto-discover configuration:

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

4. Check existing prioritization:

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

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

5. 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: IF task_provider == "linear":

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

   ELSE (file mode):

   Glob("docs/tasks/epics/epic-{N}-*/stories/*/story.md")
   

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

IF task_provider == "linear":

get_issue(id=storyId, includeRelations=false)

ELSE (file mode):

Read("docs/tasks/epics/epic-{N}-*/stories/us{NNN}-*/story.md")

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 {current_year}"
"[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 {current_year}"
"[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)

Data Confidence Assessment:

For each RICE factor, assess data confidence level:

Output: Show confidence per factor in prioritization table + RICE range (optimistic/pessimistic) to make uncertainty explicit.

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)
• Task provider: Linear MCP or file mode (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 - last 2 years, 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 (Linear or file mode)
• 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):

Phase 6: Meta-Analysis

MANDATORY READ: Load shared/references/meta_analysis_protocol.md

Skill type: planning-coordinator. Run after all phases complete. Output to chat using the planning-coordinator format.

Reference Files

• MANDATORY READ: shared/references/tools_config_guide.md
• MANDATORY READ: shared/references/storage_mode_detection.md
• MANDATORY READ: shared/references/research_tool_fallback.md

Version: 1.0.0 Last Updated: 2025-12-23