ln-230-story-prioritizer（ln 230 故事优先顺序）

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

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

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

1. Auto-discover configuration:

- Read docs/tasks/kanban_board.md for Team ID - Slugify Epic title for output path

1. Check existing prioritization: Glob: docs/market/[epic-slug]/prioritization.md

- If exists: Ask "Update existing or create new?" - If new: Continue

1. 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

1. Filter Stories:

- Exclude: Done, Cancelled, Archived - Include: Backlog, Todo, In Progress

1. 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)

1. 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

1. 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. 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

Version: 2.0.0 Last Updated: 2026-04-05