product-owner

name	product-owner
description	[Project Management] Use when you need to capture ideas, manage product backlogs, apply prioritization frameworks (RICE, MoSCoW), and facilitate stakeholder communication.

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex uses static project-reference loading instead of runtime-injected project docs. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Missing/stale context route: If docs/project-config.json, the docs index, lessons.md, CLAUDE.md, AGENTS.md, or any task-required reference doc is missing or stale, auto-run $project-init or the narrow setup route ($project-config, $docs-init, $scan-all, $scan --target=<key>, $claude-md-init) before ordinary project-specific work. If Codex mirrors or AGENTS.md are missing/stale, ask the user to run $sync-codex; do not auto-run it.

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec authoring, docs/specs/ pathing, or TC format: feature-spec-reference.md, spec-system-reference.md, spec-principles.md
• Behavior/public-contract changes or spec-test-code sync: workflow-spec-test-code-cycle-reference.md plus the spec docs above
• Derived spec indexes/ERDs/reimplementation guides: spec-system-reference.md and source Feature Specs under docs/specs/
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

Quick Summary

Goal: Help Product Owners capture ideas, manage backlogs, and prioritize using RICE, MoSCoW, and Value/Effort frameworks.

MANDATORY IMPORTANT MUST ATTENTION Plan ToDo Task to READ the following project-specific reference doc:

• project-structure-reference.md -- project patterns and structure
• docs/project-reference/domain-entities-reference.md — Domain entity catalog, relationships, cross-service sync (read when task involves business entities/models)

If file not found, search for: project documentation, coding standards, architecture docs.

Workflow:

1. Idea Capture — Structure raw concepts with module detection and domain context
2. Backlog Management — Create/refine PBIs, track dependencies
3. Prioritization — Apply RICE score, MoSCoW, or Value/Effort matrix
4. Validation — MANDATORY interview to confirm assumptions before completion

Key Rules:

• Use numeric priority ordering (1-999), never High/Medium/Low categories
• Always detect project module and load feature context for domain ideas
• Post-refinement validation interview is NOT optional
• Use the project's domain-specific entity names (resolve them from the project's domain/feature docs)

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Product Owner Assistant

Help Product Owners capture ideas, manage backlogs, and make prioritization decisions using established frameworks.

---

Project Context Awareness

When working on domain ideas, automatically detect and load business feature context.

Module Detection

Dynamic Discovery:

1. Run: Glob("docs/specs/*/README.md")
2. Extract module names from paths
3. Match keywords (detect module from docs/specs/ directory names)

Detection Approach (silent auto-detect):

• Auto-detect module(s) without displaying confidence levels
• Only prompt when ambiguous: "Which project module is this for?" + list Glob results

Feature Context Loading

Once module detected:

1. Read docs/specs/{module}/README.md (first 200 lines for overview)
2. Extract feature list from Quick Navigation
3. Identify closest matching feature(s)
4. Note related entities and services

Multi-module support: If 2+ modules detected, load ALL modules.

Domain Vocabulary

Use exact entity names from docs:

• ServiceA: Order (not "Purchase"), Product, OrderLine, Shipment, Invoice
• ServiceB: Customer, Feedback, Review, CheckIn, Report
• Use the project's domain vocabulary for actors (resolve from project-reference) rather than a generic "User"
• Use the exact term the domain docs use (e.g. "Order" not "Purchase") — never a synonym

Token Budget

Target 8-12K tokens total for feature context loading:

• Module README overview: ~2K tokens
• Full feature doc sections: 3-5K tokens per feature
• Multi-module: Load all detected (may increase total)

---

Core Capabilities

1. Idea Capture

• Transform raw concepts into structured idea artifacts
• Identify problem statements and value propositions
• Tag and categorize for future refinement
• NEW: Detect module and inject feature context

2. Backlog Management

• Create and refine Product Backlog Items (PBIs)
• Maintain backlog ordering (not categories)
• Track dependencies and blockers

3. Prioritization Frameworks

RICE Score

RICE = (Reach × Impact × Confidence) / Effort

Reach: # users affected per quarter
Impact: 0.25 (minimal) | 0.5 (low) | 1 (medium) | 2 (high) | 3 (massive)
Confidence: 0.5 (low) | 0.8 (medium) | 1.0 (high)
Effort: Story points (1, 2, 3, 5, 8, 13, 21)

MoSCoW

• Must Have: Critical for release, non-negotiable
• Should Have: Important but not vital
• Could Have: Nice to have, low effort
• Won't Have: Out of scope this cycle

Value vs Effort Matrix

         High Value
             │
    Quick    │    Strategic
    Wins     │    Priorities
─────────────┼─────────────
    Fill     │    Time
    Ins      │    Sinks
             │
         Low Value
   Low Effort    High Effort

4. Sprint Planning Support

• Capacity planning based on velocity
• Sprint goal definition
• Commitment vs forecast distinction

---

Artifact Templates

Idea Template Generation

Include in frontmatter (if project domain):

module: ServiceB # Detected module
related_features: [OrderManagement, Feedback] # From README feature list
feature_doc_path: docs/specs/ServiceB/README.OrderManagementFeature.md
entities: [Order, Customer, Region] # From feature doc

Use domain vocabulary in idea description based on loaded context.

Template Locations

• Idea: .claude/docs/team-artifacts/templates/idea-template.md
• PBI: .claude/docs/team-artifacts/templates/pbi-template.md

---

Workflow Integration

Creating Ideas (with Domain Context)

When user says "new idea" or "feature request":

1. Use $idea command workflow
2. Detect module from conversation keywords
3. Load feature context from docs/specs/
4. Populate idea-template.md with domain fields
5. Save to team-artifacts/ideas/
6. Suggest next step: $refine {idea-file}

Prioritizing Backlog

When user says "prioritize" or "order backlog":

1. Read all PBIs in team-artifacts/pbis/
2. Apply requested framework (RICE, MoSCoW, Value/Effort)
3. Output ordered list with scores
4. Update priority field in PBI frontmatter

---

Role Context (path→role, canonical)

Applies to Writes under team-artifacts/ideas/.

• Active Role: product-owner · Skill: product-owner
• Path: team-artifacts/ideas/
• Template: .claude/docs/team-artifacts/templates/idea-template.md
• Naming: {YYMMDD}-po-{type}-{slug}.md
• Context: IDEA CAPTURE — use problem-focused language, identify value proposition, tag for refinement.
• Quality checklist: - [ ] Problem statement user-focused · - [ ] Value proposition quantified · - [ ] Priority numeric (not High/Med/Low) · - [ ] Dependencies listed

Output Conventions

File Naming

{YYMMDD}-po-idea-{slug}.md
{YYMMDD}-pbi-{slug}.md

Priority Values

• Numeric ordering: 1 (highest) to 999 (lowest)
• Never use High/Medium/Low categories

Status Values

draft | under_review | approved | rejected | in_progress | done

---

Anti-Patterns to Avoid

1. Category-based priority - Use ordered sequence, not High/Med/Low
2. Vague acceptance criteria - Require GIVEN/WHEN/THEN format
3. Scope creep - Explicitly list "Out of Scope"
4. Missing dependencies - Always identify upstream/downstream
5. Generic terminology - Use domain-specific entity names

---

Integration Points

When	Trigger	Action
Idea captured	$idea complete	Suggest $refine, note module context
PBI ready	PBI approved	Notify BA for stories
Sprint planned	Sprint goal set	Update PBI assignments
Domain feature	Module detected	Load business feature docs

---

Stakeholder Communication Templates

Sprint Review Summary

## Sprint {N} Review

**Sprint Goal:** {goal}
**Status:** {achieved | partially | not achieved}

### Completed Items

| PBI | Value Delivered |
| --- | --------------- |
|     |                 |

### Carried Over

| PBI | Reason | Plan |
| --- | ------ | ---- |
|     |        |      |

### Key Metrics

- Velocity: {points}
- Commitment: {%}

Roadmap Update

## Roadmap Update - {Date}

### This Quarter

| Priority | Item | Target | Status |
| -------- | ---- | ------ | ------ |
| 1        |      |        |        |

### Next Quarter

| Item | Dependencies | Notes |
| ---- | ------------ | ----- |
|      |              |       |

### Deferred

| Item | Reason |
| ---- | ------ |
|      |        |

---

Quality Checklist

Before completing PO artifacts:

• Problem statement is user-focused, not solution-focused
• Value proposition quantified or qualified
• Priority has numeric order
• Dependencies explicitly listed
• Status frontmatter current
• Module detected and context loaded (if domain-related)
• Domain vocabulary used correctly

---

Post-Refinement Validation (MANDATORY)

Every idea/PBI refinement must end with a validation interview.

After completing idea capture or PBI creation, validate with user to:

1. Confirm assumptions about user needs
2. Verify scope boundaries
3. Surface potential concerns
4. Brainstorm alternatives

Validation Interview Process

Use a direct user question tool with 3-5 questions:

Category	Example Questions
User Value	"Is the value proposition clear to stakeholders?"
Scope	"Should we explicitly exclude feature X?"
Priority	"Does this priority align with roadmap?"
Dependencies	"Are there blockers from other teams?"
Risk	"What's the biggest concern with this approach?"

Document Validation Results

Add to idea/PBI:

## Validation Summary

**Validated:** {date}

### Confirmed Decisions

- {decision}: {user choice}

### Concerns Raised

- {concern}: {resolution}

### Action Items

- [ ] {follow-up if any}

When to Escalate

• Priority conflicts with roadmap
• Resource constraints identified
• Stakeholder alignment needed
• Cross-team dependency discovered

This step is NOT optional - always validate before marking complete.

Related

• business-analyst
• project-manager

---

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

AI Mistake Prevention — Failure modes to avoid on every task:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

Sequential Thinking Protocol — Structured multi-step reasoning for complex/ambiguous work. Use when planning, reviewing, debugging, or refining ideas where one-shot reasoning is unsafe.

Trigger when: complex problem decomposition · adaptive plans needing revision · analysis with course correction · unclear/emerging scope · multi-step solutions · hypothesis-driven debugging · cross-cutting trade-off evaluation.

Format (explicit mode — visible thought trail):

1. Thought N/M: [aspect] — one aspect per thought, state assumptions/uncertainty
2. Thought N/M [REVISION of Thought K]: ... — when prior reasoning invalidated; state Original / Why revised / Impact
3. Thought N/M [BRANCH A from Thought K]: ... — explore alternative; converge with decision rationale
4. Thought N/M [HYPOTHESIS]: ... then [VERIFICATION]: ... — test before acting
5. Thought N/N [FINAL] — only when verified, all critical aspects addressed, confidence >80%

Mandatory closers: Confidence % stated · Assumptions listed · Open questions surfaced · Next action concrete.

Stop conditions: confidence <80% on any critical decision → escalate via ask the user directly · ≥3 revisions on same thought → re-frame the problem · branch count >3 → split into sub-task.

Implicit mode: apply methodology internally without visible markers when adding markers would clutter the response (routine work where reasoning aids accuracy).

Deep-dive: see $sequential-thinking skill (.claude/skills/sequential-thinking/SKILL.md) for worked examples (API design, debugging, architecture), advanced techniques (spiral refinement, hypothesis testing, convergence), and meta-strategies (uncertainty handling, revision cascades).

MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply sequential-thinking — multi-step Thought N/M, REVISION/BRANCH/HYPOTHESIS markers, confidence % closer; see $sequential-thinking skill.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

Closing Reminders

Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
• Critical Thinking: traced file:line proof per claim, confidence >80% to act, never guess as fact.
• Sequential Thinking: multi-step Thought N/M with REVISION/BRANCH/HYPOTHESIS markers, confidence-% closer.

IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act) IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/.ck.json + .claude/skills/shared/sync-inline-versions.md (:full blocks) + .claude/scripts/lib/hookless-prompt-protocol.cjs

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. For spec, test-case, behavior-change, public-contract, or docs/specs/ work, route through the local spec docs named by the docs index: feature-spec-reference.md, spec-system-reference.md, spec-principles.md, and workflow-spec-test-code-cycle-reference.md when specs/tests/code must stay synchronized. If either file or a required reference doc is missing or stale, auto-run $project-init (or the narrow lower-level route such as $project-config, $docs-init, $scan-all, or $scan --target=<key>) before ordinary project-specific work. Any supported AI tool may execute when this shared context and local docs are available.

1. DETECT: If the prompt starts with an explicit slash skill/workflow command, execute it directly. Otherwise match the prompt against the workflow catalog and skill list.
2. ANALYZE: Choose the best option: execute directly, invoke a skill, activate a standard workflow, or compose a custom step combination.
3. AUTO-SELECT: Pick the best option yourself. Do not ask the user to choose between direct execution, skill, standard workflow, or custom workflow.
4. ACTIVATE: For a selected workflow, call $start-workflow <workflowId>; for a selected skill, invoke that skill; for a custom workflow, sequence custom steps directly; for direct execution, proceed with the task.
5. CREATE TASKS: task tracking for ALL workflow/skill/custom steps before execution when the selected path has multiple steps.
6. EXECUTE: Advance per the Workflow Step Advancement & Parallel Phases rule in your context instructions — model-driven; a sub-agent completion advances a step identically to an inline call; a parallel-phase group is an all-return barrier (advance only after ALL members return, never serialize it)

Shared AI-SDD Protocol Markers

Source: .claude/skills/shared/sync-inline-versions.md

SYNC:ai-sdd-artifact-contract

AI-SDD Artifact Contract — Shared spec-driven development rules stay portable and source-owned.

1. Keep reusable AI-SDD principles in .claude; put repository-specific paths, commands, owners, products, and formats in project config/reference docs.
2. Preserve cycle: spec -> plan -> tasks -> implement -> verify -> update spec/docs.
3. Trace every requirement or invariant through decision, task, TC/test, source evidence, and docs/spec update.
4. Treat code-to-spec extraction as reference-only until accepted by the canonical spec owner.
5. Any supported AI tool may plan, implement, review, or verify with synced context; using multiple tools is optional.
6. Update .claude source first, then sync generated mirrors; do not manually edit .agents, .codex, or AGENTS.md. — why: mirrors are generated artifacts; hand-edits are overwritten on the next sync
7. If docs/project-config.json, root instruction files, or a required project-reference doc is missing or stale, auto-run $project-init or the narrow lower-level route before ordinary project-specific work.

Active reference: shared/sdd-artifact-contract.md in the active skills root.

---

SYNC:ai-sdd-artifact-contract:reminder

• MANDATORY Apply shared/sdd-artifact-contract.md; keep reusable AI-SDD in .claude and local rules in project docs.
• MANDATORY Code-to-spec extraction is reference-only until canonical acceptance; any supported AI tool may execute with synced context.
• MANDATORY Update .claude source before syncing generated mirrors; do not manually edit .agents, .codex, or AGENTS.md.
• MANDATORY Missing or stale project config, root instruction files, or required reference docs route project-specific work through $project-init or the narrow setup route automatically. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security-review/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

Common AI Mistake Prevention (System Lessons)

• Re-read files after context compaction. Edit requires prior Read in same context; compaction wipes read state. Re-read before editing.
• Grep for old terms after bulk replacements. AI over-trusts find/replace completeness. Grep full repo after bulk edits for missed refs in docs/configs/catalogs.
• Check downstream references before deleting. Deletions cascade doc/code staleness. Map referencing files before removal.
• After memory loss, check existing state before creating new. Compaction wipes prior-work memory. Query current state to resume — never blindly duplicate.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, method signatures. Grep to confirm existence before documenting/referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream consumers. Trace the full chain.
• When renaming, grep ALL consumer file types. Some file types silently ignore missing refs (no compile error). Search code, templates, configs, generated files.
• Trace ALL code paths when verifying correctness. Code existing ≠ code executing. Trace early exits, error branches, conditional skips — not just happy path.
• Update docs that embed canonical data when source changes. Docs inlining derived data (workflows, schemas, configs) go stale silently. Update all embedding docs alongside source.
• Verify sub-agent results after context recovery. Background agents may finish while parent compacted — grep-verify output, don't trust assumed completion.
• Cross-check full target list against sub-agent assignments. Parallel sub-agents by category miss boundary items. Reconcile union of assignments against target list before proceeding.
• Sub-agents inherit knowledge only from their agent .md definition — use custom agent types, not built-in Explore. Tool adoption = permission + knowledge + enforcement (numbered workflow step).
• Persist sub-agent findings incrementally, not as a final batch. Long sub-agents hit cutoffs before final write — findings lost. Instruct append-per-section to report file.
• When debugging, ask "whose responsibility?" before fixing. Trace caller (wrong data) vs callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Grep ALL removed names after extraction/refactoring. Primary file "done" ≠ secondary files clean. Grep entire scope for every removed symbol before declaring complete.
• Assume existing values are intentional — ask WHY before changing. Pattern-matching as "wrong" skips context. Before changing any constant/limit/flag: read comments, git blame, surrounding code.