ln-230-story-prioritizer

name	ln-230-story-prioritizer
description	RICE-scores Stories with market research and generates prioritization table. Use when Stories need business priority ranking for sprint planning.
license	MIT

Paths: File paths (references/, ../ln-*) are relative to this skill directory.

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 references/planning_worker_runtime_contract.md, references/coordinator_summary_contract.md MANDATORY READ: Load references/researchgraph_mcp_usage.md when Stories cite H/G/run IDs or project researchgraph evidence can change priority confidence.

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 references/environment_state_contract.md, references/storage_mode_detection.md, 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"
hex-research	Local hypothesis, goal, and benchmark evidence	find_hypotheses, inspect_goal, find_runs for explicit H/G/run context
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.

If a researchgraph layout exists, run local graph checks only for Stories whose priority depends on hypothesis status, goal coverage, benchmark evidence, or implementation readiness. Local graph evidence can change RICE confidence and risk; it does not replace market-size or competitor 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":

// configured tracker provider: getStory(id=storyId)

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/templates/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: configured tracker provider (load Epic, Stories) per references/tracker_provider_contract.md
• 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 via the configured tracker provider
• 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

---

Phase 6: Meta-Analysis

Optional reference: load references/meta_analysis_protocol.md only when the user asks for post-run meta-analysis or protocol-formatted run reflection.

Skill type: planning-worker. When requested, run after all phases complete. Output to chat using the planning-worker format.

Reference Files

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