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

Parameter	Required	Description	Default
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):

Priority	Customer Problem	Feature	Solution	Rationale	Impact	Market	Sources	Competition
P0	User pain point	Story title	Technical approach	Why important	Business impact	$XB	[Link]	Blue 1-3 / Red 4-5

---

Research Tools

Tool	Purpose	Example Query
WebSearch	Market size, competitors	"[domain] market size {current_year}"
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:

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 {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:

Competitors Found	Competition Index	Ocean Type
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

Score	Users	Indicators
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

Score	Level	Indicators
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)

Data Confidence Assessment:

For each RICE factor, assess data confidence level:

Confidence	Criteria	Score Modifier
HIGH	Multiple authoritative sources (Gartner, Statista, SEC filings)	Factor used as-is
MEDIUM	1-2 sources, mixed quality (blog + report)	Factor ±25% range shown
LOW	No sources, team estimate only	Factor ±50% range shown

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

Effort (1-10): Person-months

Score	Time	Story Indicators
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

Priority	RICE Threshold	Competition Override
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:

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

Depth	Per-Story	Total (10 Stories)
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

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 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):

Priority	Customer Problem	Feature	Solution	Rationale	Impact	Market	Sources	Competition
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

File	Purpose
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