Agent Skills: Story Prioritizer

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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 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