RICE-scores Stories with market research and generates prioritization table. Use when Stories need business priority ranking for sprint planning.
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:
PHASE_0_CONFIG
PHASE_1_DISCOVERY
PHASE_2_LOAD_STORY_METADATA
PHASE_3_ANALYZE_STORIES
PHASE_4_GENERATE_PRIORITIZATION
PHASE_5_WRITE_SUMMARY
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:
Resolve epicId: Run Epic Resolution Chain per guide.
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
Auto-discover configuration:
Read docs/tasks/kanban_board.md for Team ID
Slugify Epic title for output path
Check existing prioritization:
Glob: docs/market/[epic-slug]/prioritization.md
If exists: Ask "Update existing or create new?"
If new: Continue
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:
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")
Extract metadata only:
Story ID, title, status
minimal Epic context if available
DO NOT load full descriptions yet
Filter Stories:
Exclude: Done, Cancelled, Archived
Include: Backlog, Todo, In Progress
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:
Sort results:
Primary: Priority (P0 → P3)
Secondary: RICE score (descending)
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
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
Triage first - do cheap scoring across all Stories before deep research
Source all deep-research data - every Market number needs source + date
Prefer recent data - last 2 years, warn if older
Cross-reference when depth justifies it - use 2+ sources for market-sensitive Stories
Time-box strictly - keep rough estimates when deeper research will not change the decision
Confidence levels - mark High/Medium/Low and whether score is rough or full-research
No speculation - only sourced claims, note "[No data]" gaps
One Story at a time - token efficiency critical
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
Version: 2.0.0
Last Updated: 2026-04-05don't have the plugin yet? install it then click "run inline in claude" again.