ln-230-story-prioritizer

SKILL.md

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: 2026-04-05