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. If

shared/

is missing, fetch files via WebFetch from

https://raw.githubusercontent.com/levnikolaevich/claude-code-skills/master/skills/{path}

.

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

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):

Inputs

epicId

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

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 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:

Step 3.5: Calculate final 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 3.6: Determine Priority

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

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):

Phase 6: Meta-Analysis

MANDATORY READ: Load

shared/references/meta_analysis_protocol.md

Skill type:

planning-worker

planning-worker

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

Version: 2.0.0 Last Updated: 2026-04-05