levnikolaevich/ln-230-story-prioritizer

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Story Prioritizer

Type: L3 Worker Category: 2XX Planning

Evaluate Stories using RICE scoring with market research. Generate consolidated prioritization table for Epic.

Purpose & Scope

• Prioritize Stories AFTER ln-220 creates them
• Triage all Stories cheaply before doing deep research
• Research market size and competition only where it changes prioritization confidence
• 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/

---

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

Runtime Contract

MANDATORY READ: Load shared/references/planning_worker_runtime_contract.md, shared/references/coordinator_summary_contract.md

Runtime family: planning-worker-runtime

Identifier:

• epic-{epicId}

Phases:

1. PHASE_0_CONFIG
2. PHASE_1_DISCOVERY
3. PHASE_2_LOAD_STORY_METADATA
4. PHASE_3_ANALYZE_STORIES
5. PHASE_4_GENERATE_PRIORITIZATION
6. PHASE_5_WRITE_SUMMARY
7. PHASE_6_SELF_CHECK

Summary contract:

• summary_kind=story-prioritization-worker
• payload includes epic_id, depth, stories_analyzed, priority_distribution, top_story_ids, prioritization_path, warnings
• managed mode writes to caller-provided summaryArtifactPath
• default managed artifact path pattern: .hex-skills/runtime-artifacts/runs/{parent_run_id}/story-prioritization-worker/ln-230--{identifier}.json

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/environment_state_contract.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:

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

2. Load Epic details:

   • IF task_provider == "linear": get_project(query=epicId)
   • ELSE IF task_provider == "github": gh issue view {epicId} -R {REPO} --json number,title,body
   • 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 and prepare rough scoring inputs for all Stories.

Process:

1. Query Stories from Epic: IF task_provider == "linear":

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

   ELSE IF task_provider == "github":

   gh api /repos/{O}/{R}/issues/{epic_num}/sub_issues --jq '.[].number'
   → for each: gh issue view {num} -R {REPO} --json number,title,state,labels
   → filter: label "user-story"
   

   ELSE (file mode):

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

2. Extract metadata only:

   • Story ID, title, status
   • minimal Epic context if available
   • 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 + minimal context), ~50-80 tokens/Story

---

Phase 3: Two-Pass Story Analysis

Objective: Score all Stories cheaply first, then spend deep research only on candidates where it changes the decision.

Critical: Keep maximum context to one full Story at a time even during deep research.

Pass A: Cheap Triage For All Stories

For each Story, load only enough detail to estimate:

• customer problem
• rough solution shape
• likely reach
• likely impact
• likely effort
• initial confidence tier

Step 3.1: Load Story Description

IF task_provider == "linear":

get_issue(id=storyId, includeRelations=false)

ELSE IF task_provider == "github":

gh issue view {storyId} -R {REPO} --json number,title,body,state,labels

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: Build rough RICE estimate

Use Story + Epic context to assign:

• rough Reach
• rough Impact
• rough Effort
• initial Confidence

Mark one of:

• full_research_required
• rough_estimate_ok
• borderline_needs_review

Send to Pass B only if:

• candidate looks P0/P1 on rough score
• confidence is low
• Story is near a priority threshold
• Story has strategic or market-sensitive uncertainty

Pass B: Selective Deep Research

Only for Stories selected in Pass A, run full external research.

Step 3.3: 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 3.4: 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 3.5: Calculate final 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 3.6: 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 3.7: Store and Clear

• Append row to in-memory results table
• Mark whether row is full-research or rough-estimate
• Clear Story description from context
• Move to next Story in queue

Output per Story: Complete row for prioritization table with confidence tier

---

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
   • Explicitly show whether each Story used full research or rough estimate

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: keep rough estimate, mark lower confidence
• If total exceeds budget: reserve deep research only for high-potential or borderline 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

Structured worker output:

• return the prioritization summary envelope even in standalone mode
• write the same JSON artifact when summaryArtifactPath is provided

---

Critical Rules

1. Triage first - do cheap scoring across all Stories before deep research
2. Source all deep-research data - every Market number needs source + date
3. Prefer recent data - last 2 years, warn if older
4. Cross-reference when depth justifies it - use 2+ sources for market-sensitive Stories
5. Time-box strictly - keep rough estimates when deeper research will not change the decision
6. Confidence levels - mark High/Medium/Low and whether score is rough or full-research
7. No speculation - only sourced claims, note "[No data]" gaps
8. One Story at a time - token efficiency critical
9. Preserve language - if user asks in Russian, respond in Russian

---

Definition of Done

• [ ] Epic validated (Linear or file mode)
• [ ] All Stories loaded through metadata-first queue
• [ ] Pass A rough triage completed for all Stories
• [ ] Deep research limited to high-potential or low-confidence Stories
• [ ] RICE score calculated for each Story
• [ ] Competition index assigned (1-5)
• [ ] Priority assigned (P0/P1/P2/P3)
• [ ] Confidence tier and research depth visible in output
• [ ] Table sorted by Priority + RICE
• [ ] File saved to docs/market/[epic-slug]/prioritization.md
• [ ] Summary with top priorities and next steps
• [ ] Structured story-prioritization-worker summary returned
• [ ] Summary artifact written when summaryArtifactPath is provided
• [ ] 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 |

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Phase 6: Meta-Analysis

MANDATORY READ: Load shared/references/meta_analysis_protocol.md

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

Reference Files

• MANDATORY READ: Load shared/references/environment_state_contract.md
• MANDATORY READ: Load shared/references/storage_mode_detection.md
• MANDATORY READ: Load shared/references/research_tool_fallback.md

| 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 |

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Version: 2.0.0 Last Updated: 2026-04-05