Ln 220 Story Coordinator

Core Pattern: Decompose-First

Key principle: Build IDEAL Story plan FIRST, THEN check existing Stories to determine mode:

• No existing Stories → CREATE MODE (delegate to ln-221-story-creator)
• Has existing Stories → REPLAN MODE (delegate to ln-222-story-replanner)

Rationale: Ensures consistent Story decomposition based on current Epic requirements, independent of existing Story structure (may be outdated).

Story Numbering Convention

MANDATORY READ: Load shared/references/numbering_conventions.md for Story numbering rules (US001 sequential across Epics, no Story 0).

Quality Criteria

MANDATORY READ: Load shared/references/creation_quality_checklist.md §Story Creation Checklist for validation criteria that ln-310 will enforce.

Workflow

Phase 1: Context Assembly

Objective: Gather context for Story planning (Epic details, planning questions, frontend context, fallback docs, user input)

Step 1: Discovery (Automated)

Auto-discovers from docs/tasks/kanban_board.md:

1. Team ID: Reads Linear Configuration table
2. Epic: Parses Epic number from request → Validates in Linear → Loads Epic description
   • User format: "Epic N" (Linear Project number, e.g., "Epic 7: OAuth Authentication")
   • Query: get_project(query="Epic N") → Fetch full Epic document
   • Extract: Goal, Scope In/Out, Success Criteria, Technical Notes (Standards Research if Epic created by ln-210 v7.0.0+)
   • Note: Epic N = Linear Project number (global), NOT initiative-internal index (Epic 0-N)
3. Next Story Number: Reads Epic Story Counters table → Gets next sequential number

Step 2: Extract Planning Information (Automated)

Parses Epic structure for Story planning questions:

Step 3: Frontend Research (Optional)

Trigger: If Q2 (capabilities) OR Q5 (AC) missing after Step 2

Process:

1. Scan HTML files: Glob **/*.html, src/**/*.html
2. Extract:
   • Forms → AC scenarios (e.g., <form id="login"> → "Given valid credentials, When submit, Then login success")
   • Buttons/Actions → capabilities (e.g., <button id="register"> → "User registration")
   • Validation rules → edge case AC (e.g., minlength="8" → "Given password <8 chars, Then error")
3. Combine with Epic context, deduplicate, prioritize Epic AC if conflict

Fallback: If no HTML → Skip to Step 4

Step 4: Fallback Search Chain

Objective: Fill missing Q1-Q6 BEFORE asking user.

For each question with no answer from Step 2-3:

Skip: Q2, Q5 (Epic + HTML are sources of truth), Q4 (already known)

Step 5: User Input (Only if Missing)

If still missing after Step 2 + 3 + 4:

• Show extracted: "From Epic: [Epic info]. From HTML: [HTML info]. From fallback: [fallback info]"
• Ask user to confirm or provide remaining missing details

If all questions answered from Epic OR HTML OR fallback: Skip user prompts, proceed to Phase 2

Output: Complete context (Epic details, next Story number, Q1-Q6 answers)

Phase 2: Standards Research (Delegated)

Objective: Research industry standards/patterns BEFORE Story generation to ensure implementation follows best practices.

Why: Prevents outdated patterns or RFC violations (e.g., OAuth without PKCE).

Process:

1. Parse Epic for domain keywords: Extract domain from Epic goal/Scope In (authentication, rate limiting, payments)
2. Delegate to ln-001-standards-researcher:
   • Call Skill(skill: "ln-001-standards-researcher", epic_description="[Epic full description]", story_domain="[domain]")
   • Wait for Standards Research (Markdown string)
3. Store: Cache for Phase 5a/5b (workers insert in Story Technical Notes)

Output: Standards Research stored for ALL Stories in Epic

Skip conditions:

• Epic has NO standards in Technical Notes
• Story domain is trivial CRUD
• Epic says "research not needed"

Time-box: 15-20 minutes (handled by ln-001)

Note: Research done ONCE per Epic, results reused for all Stories (5-10 Stories benefit from single research)

Phase 3: Planning

Objective: Build IDEAL Story plan, determine execution mode

Story Grouping Guidelines:

Each Story = ONE vertical slice of user capability (end-to-end: UI → API → Service → DB).

✅ GOOD Story Grouping (1 Story = 1 user journey):

• ✅ "User registration" (form → validation → API → database → email)
• ✅ "Password reset" (request link → verify token → set password → update DB)
• ✅ "Product search" (input → filter/sort → API → DB query → display)

❌ BAD Story Grouping (horizontal slices):

• ❌ "Create user table" (database only, no user value → Task, not Story)
• ❌ "User registration API endpoint" (API layer only, not vertical)
• ❌ "Registration UI form" (frontend only, not vertical)

Rule: 1 Story = 1 user capability (size limits per creation_quality_checklist.md #9)

Database Creation Principle (Incremental Schema Evolution):

Each Story creates ONLY the tables it needs (not all tables upfront).

✅ GOOD (Incremental):

• ✅ "User registration" → Creates Users table
• ✅ "Product search" → Creates Products table
• ✅ "Order checkout" → Creates Orders, Payments tables

❌ BAD (Big-Bang):

• ❌ "Setup database" → Creates all 50 tables (no user value, violates vertical slicing)
• ❌ "Database schema" → Creates Users, Products, Orders, Payments upfront

Rationale: Big-bang database setup violates incremental delivery. Each Story should deliver user value, not technical infrastructure.

Build IDEAL Plan (Automated):

0. Articulate REAL GOAL: MANDATORY READ: shared/references/goal_articulation_gate.md — State REAL GOAL of this Epic in one sentence (the user capability being enabled, not "create Stories"). Verify: does the decomposition serve THIS goal?

1. Analyze Epic Scope: Review features in Epic Scope In, identify user capabilities

2. Determine Story Count:

   • Simple Epic (1-3 features): 3-5 Stories
   • Medium Epic (4-7 features): 6-8 Stories
   • Complex Epic (8+ features): 8-10 Stories
   • Max 10 Stories per Epic

3. Story Size: Limits per creation_quality_checklist.md #9. Outside range → split or merge.

Each Story must pass these checks:

• ✅ Delivers user value (not purely technical)
• ✅ Title describes user capability, not implementation action
• ✅ Crosses 3+ layers (vertical slice: UI → API → Service → DB)

4. Build IDEAL Plan "in mind":

   • Each Story: persona + capability + business value
   • Each Story: testable AC per checklist #4
   • Stories ordered by dependency (no forward deps per checklist #18)
   • Each Story: Test Strategy section exists but is empty (tests planned later by test planner)
   • Each Story: Technical Notes (architecture, integrations, Standards Research from Phase 2, guide links)
   • Each Story: orchestratorBrief for ln-1000 pipeline lead:

     orchestratorBrief: {
       tech: "<languages, frameworks, key libraries from Epic context>",
       keyFiles: "<2-5 files/dirs most affected>",
       approach: "<1-line implementation strategy>",
       complexity: "Low|Medium|High (<reason>)"
     }
     

5. AC Quality Validation: Rules per creation_quality_checklist.md #4. Workers (ln-221, ln-222) must validate.

Examples:

• ❌ BAD: "User can login" (only happy path, no error/edge cases)
• ✅ GOOD: AC1: login success + AC2: 401 invalid password + AC3: 403 locked
• ❌ BAD: "Login should be fast" (vague, not measurable)
• ✅ GOOD: "Then receive token <200ms" (specific, measurable)

INVEST Score (0-6 per Story):

Gate: Score ≥ 4 → proceed. Score < 4 → rework Story before creation.

Examples:

• ❌ Score 2/6: "Create user table" (fails Independent, Valuable, Small, Testable)
• ✅ Score 6/6: "User registration" with 4 GWT AC, 12h, full vertical slice

Output: IDEAL Story plan (5-10 Stories) with titles, statements, core AC, ordering

Phase 4: Check Existing & Detect Mode

Objective: Determine execution mode based on existing Stories AND user intent

Process:

Query Linear for existing Stories in Epic:

list_issues(project=Epic.id, label="user-story")

Mode Detection:

1. Analyze user request for keywords:

   • ADD keywords: "add story", "one more story", "additional story", "append"
   • REPLAN keywords: "update plan", "revise", "requirements changed", "replan stories"

2. Decision matrix:

Important: Orchestrator loads metadata ONLY (ID, title, status). Workers load FULL descriptions (token efficiency).

Output: Execution mode determined + existingCount for workers

Phase 5a: Delegate CREATE (No Existing Stories)

Trigger: Epic has no Stories yet (first decomposition)

Delegation:

Call ln-221-story-creator via Skill tool:

Skill(
  skill: "ln-221-story-creator",
  epicData: {id, title, description},
  idealPlan: [ /* 5-10 Stories from Phase 3 */ ],
  standardsResearch: "Standards Research from Phase 2",
  teamId: "team-id",
  autoApprove: false  // or true for automation
)

Worker handles:

• Generate Story documents (8 sections, insert Standards Research)
• Validate INVEST criteria
• Show preview
• User confirmation (if autoApprove=false)
• Create in Linear (project=Epic, labels=user-story, state=Backlog)
• Update kanban_board.md (Epic Grouping Algorithm)

Output: Created Story URLs + summary from worker

Phase 5b: Delegate REPLAN (Existing Stories Found)

Trigger: Epic already has Stories (requirements changed)

Delegation:

Call ln-222-story-replanner via Skill tool:

Skill(
  skill: "ln-222-story-replanner",
  epicData: {id, title, description},
  idealPlan: [ /* 5-10 Stories from Phase 3 */ ],
  standardsResearch: "Standards Research from Phase 2",
  existingCount: N,
  teamId: "team-id",
  autoApprove: false  // or true for automation
)

Worker handles:

• Load existing Stories (Progressive Loading: ONE BY ONE for token efficiency)
• Compare IDEAL vs existing (KEEP/UPDATE/OBSOLETE/CREATE operations)
• Show replan summary with diffs (AC, Standards Research, Technical Notes)
• User confirmation (if autoApprove=false)
• Execute operations (respecting status constraints: Backlog/Todo only, warnings for In Progress/Review/Done)
• Update kanban_board.md (add NEW Stories only via Epic Grouping Algorithm)

Output: Operation results + warnings + affected Story URLs from worker

Phase 5c: Delegate ADD (Append to Existing Stories)

Trigger: Epic has Stories, user wants to ADD more (not replan existing)

Delegation:

Call ln-221-story-creator via Skill tool with appendMode:

Skill(
  skill: "ln-221-story-creator",
  appendMode: true,  // ADD to existing, don't replace
  epicData: {id, title, description},
  newStoryDescription: userRequestedStory,  // Single Story from user request
  standardsResearch: "Standards Research from Phase 2",
  teamId: "team-id",
  autoApprove: false
)

Key differences from CREATE MODE:

• appendMode: true → Skip full IDEAL plan, create only requested Story
• newStoryDescription → User's specific request (e.g., "add authorization Story")
• Does NOT require Phase 3 IDEAL plan for all Stories
• Preserves existing Stories without comparison

Worker handles:

• Research standards for NEW Story only
• Generate Story document (8 sections)
• Validate INVEST criteria
• Create in Linear (append to existing)
• Update kanban_board.md

Output: Created Story URL + summary from worker

Phase 6: Commit

After worker completes (any mode: CREATE/REPLAN/ADD):

1. git add docs/tasks/kanban_board.md (updated by worker)
2. git commit -m "ln-220: {MODE} Stories for Epic {N}"
   • CREATE: "ln-220: create Stories US{first}-US{last} for Epic {N}"
   • REPLAN: "ln-220: replan Stories for Epic {N}"
   • ADD: "ln-220: add Story US{num} to Epic {N}"

TodoWrite format (mandatory): Add phases to todos before starting:

- Phase 1: Context Assembly (in_progress)
- Phase 2: Standards Research via ln-221 (pending)
- Phase 3: Build IDEAL Story Plan (pending)
- Phase 4: Check Existing Stories (pending)
- Phase 5: Delegate to ln-221/ln-222 (pending)
- Wait for worker result (pending)
- Phase 6: Commit kanban changes (pending)

Mark each as in_progress when starting, completed when done.

Critical Rules

• Decompose-First: Build IDEAL Story plan before checking existing Stories (prevents anchoring to suboptimal structure)
• Vertical slicing only: Each Story = one user journey end-to-end (UI -> API -> Service -> DB); no horizontal/technical-only Stories
• Standards research before generation: Phase 2 (ln-001) must complete before Story documents are created; results go into all Story Technical Notes
• Orchestrator loads metadata only: ID, title, status (~50 tokens per Story); workers load full descriptions (~5,000 tokens) when needed
• Test Strategy section left empty: Tests are planned later by test planner, not at Story creation time

Integration with Ecosystem

Calls:

• ln-001-standards-researcher (Phase 2) - research standards/patterns for Epic
• ln-221-story-creator (Phase 5a, 5c) - CREATE and ADD worker
• ln-222-story-replanner (Phase 5b) - REPLAN worker

Called by:

• ln-200-scope-decomposer (Phase 3) - automated full decomposition (scope → Epics → Stories)
• Manual - user invokes for Epic Story creation/replanning

Upstream:

• ln-210-epic-coordinator - creates Epics (prerequisite for Story creation)

Downstream:

• ln-300-task-coordinator - creates implementation tasks for each Story
• ln-310-story-validator - validates Story structure/content
• ln-400-story-executor - orchestrates task execution for Story

Definition of Done

✅ Phase 1: Context Assembly Complete:

• Team ID, Epic number, Next Story Number loaded from kanban_board.md
• Q1-Q6 extracted from Epic (Step 2)
• Frontend Research attempted if Q2/Q5 missing (Step 3)
• Fallback Search attempted for missing info (Step 4)
• User input requested if still missing (Step 5)
• Complete Story planning context assembled

✅ Phase 2: Standards Research Complete:

• Epic parsed for domain keywords
• ln-001-standards-researcher invoked with Epic description + Story domain
• Standards Research cached for workers
• OR Phase 2 skipped (trivial CRUD, no standards, explicit skip)

✅ Phase 3: Planning Complete:

• Epic Scope analyzed
• Optimal Story count determined (5-10 Stories)
• IDEAL Story plan created (titles, statements, core AC, ordering)
• Story Grouping Guidelines validated (vertical slicing)
• INVEST checklist validated for all Stories

✅ Phase 4: Check Existing Complete:

• Queried Linear for existing Stories (count only)
• Execution mode determined (CREATE or REPLAN)

✅ Phase 5: Delegation Complete:

• Called ln-221-story-creator (Phase 5a) OR ln-222-story-replanner (Phase 5b) via Skill tool
• Passed epicData, idealPlan, standardsResearch, teamId, autoApprove
• Received output from worker (Story URLs + summary + next steps)

✅ Phase 6: Commit Complete:

• Kanban board changes committed with descriptive message

Example Usage

CREATE MODE (First Time):

"Create stories for Epic 7: OAuth Authentication"

Process:

1. Phase 1: Context Assembly → Discovery (Team "API", Epic 7, US004), Extract (Persona: API client, Value: secure API access), Frontend Research (HTML login/register forms → AC), Fallback Search (requirements.md for personas)
2. Phase 2: Standards Research → Epic mentions "OAuth 2.0", delegate ln-001 → Standards Research with RFC 6749, patterns
3. Phase 3: Planning → Build IDEAL (5 Stories: "Register client", "Request token", "Validate token", "Refresh token", "Revoke token")
4. Phase 4: Check Existing → Count = 0 → CREATE MODE
5. Phase 5a: Delegate CREATE → Call ln-221-story-creator → US004-US008 created with Standards Research

REPLAN MODE (Requirements Changed):

"Replan stories for Epic 7 - removed custom token formats, added scope management"

Process:

1. Phase 1: Context Assembly → Discovery (Team "API", Epic 7, has US004-US008), Extract (Removed custom formats, added scopes)
2. Phase 2: Standards Research → Epic mentions "OAuth 2.0 scopes", delegate ln-001 → Updated Standards Research with RFC 6749 Section 3.3
3. Phase 3: Planning → Build IDEAL (5 Stories: "Register client", "Request token", "Validate token", "Refresh token", "Manage scopes")
4. Phase 4: Check Existing → Count = 5 → REPLAN MODE
5. Phase 5b: Delegate REPLAN → Call ln-222-story-replanner → KEEP 4, UPDATE Technical Notes (scope research), OBSOLETE US008, CREATE US009

Reference Files

• [MANDATORY] Problem-solving approach: shared/references/problem_solving.md
• Orchestrator lifecycle: shared/references/orchestrator_pattern.md
• Auto-discovery patterns: shared/references/auto_discovery_pattern.md
• Decompose-first pattern: shared/references/decompose_first_pattern.md
• Numbering conventions: shared/references/numbering_conventions.md (Story sequential across Epics)

Best Practices

Story Content:

• Research-First: Always perform Phase 2 research (standards/patterns) before Story generation
  • Story level: STANDARDS/PATTERNS (OAuth RFC 6749, middleware pattern)
  • Task level: LIBRARIES (authlib vs oauthlib) - delegated by ln-300
• Business-oriented Stories: Each Story = USER JOURNEY (what user does, what they get), NOT technical tasks
  • ✅ GOOD: "As API client, I want to refresh expired token, so that I maintain session without re-authentication"
  • ❌ BAD: "Create token refresh endpoint in API" (Task, not Story)
• Vertical Slicing: Each Story delivers end-to-end functionality (UI → API → Service → DB)
• One capability per Story: Clear, focused persona + capability + value
• Testable AC: Given-When-Then, 3-5 AC, specific criteria ("<200ms" not "fast")
• Test Strategy: Section exists but is empty at Story creation (tests planned later by test planner)
• Standards Research: Include Phase 2 research in ALL Story Technical Notes

Story Decomposition:

• Decompose-First: Build IDEAL plan before checking existing - prevents anchoring to suboptimal structure
• INVEST validation: Validate every Story against INVEST criteria
• Size enforcement: 3-5 AC, 6-20 hours
• Avoid over-decomposition: <3 AC, <6 hours → Merge Stories

User Interaction:

• Epic extraction: Try to extract all planning info from Epic in Phase 1 Step 2 before asking user
• Frontend Research: HTML forms/validation → AC scenarios (Phase 1 Step 3)
• Fallback search: requirements.md, tech_stack.md if Epic incomplete (Phase 1 Step 4)
• Only ask user for missing info after Epic extraction AND frontend AND fallback search fail

Delegation:

• Orchestrator loads metadata only: ID, title, status (~50 tokens per Story)
• Workers load full descriptions: 8 sections (~5,000 tokens per Story)
• Token efficiency: 10 Stories × 50 tokens = 500 tokens (orchestrator) vs 10 Stories × 5,000 tokens = 50,000 tokens (workers load when needed)

Version: 5.0.0 Last Updated: 2026-02-03