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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 typestandard - 5-7 min/Story, 2-3 WebSearches per typedeep - 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 |
Inputs
| Input | Required | Source | Description |
|---|
epicId | Yes | args, kanban, user | Epic to process |
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
| Tool | Purpose | Example Query |
|---|
| WebSearch | Market size, competitors | "[domain] market size {current_year}" |
| mcp__Ref | Industry reports | "[domain] market analysis report" |
| Task provider | Load Stories | IF linear: list_issues / ELSE: Glob story.md |
| Glob | Check existing | "docs/market/[epic]/*" |
Workflow
Phase 1: Discovery (2 min)
Objective: Validate input and prepare context.
Process:
-
Resolve epicId: Run Epic Resolution Chain per guide.
-
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
-
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:
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: 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:
| 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:
-
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
| 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)
- 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
- Source all data - Every Market number needs source + date
- Prefer recent data - last 2 years, 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 (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):
| 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 |
|
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
Weekly Installs
154
Repository
levnikolaevich/…e-skills
GitHub Stars
245
First Seen
Jan 24, 2026
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Installed on
claude-code140
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github-copilot133
