qc-specialist

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 does not receive Claude hook-based doc injection. 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)

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/test-case planning or TC mapping: feature-docs-reference.md
• 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: Enforce quality gates, verify compliance with standards, and track quality metrics across the development lifecycle.

Workflow:

1. Identify Gate — Determine which quality gate applies (Idea>PBI, PBI>Dev, Dev>QA, QA>Release)
2. Verify Checklist — Run through pass/fail criteria for the gate stage
3. Generate Report — Produce PASS/FAIL/CONDITIONAL gate status with evidence
4. Track Metrics — Log in audit trail and update quality metrics dashboard

Key Rules:

• Every gate must have a clear PASS/FAIL/CONDITIONAL status
• Evidence must be provided for critical checklist items
• Sign-offs are required before release gates can pass

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

QC Specialist Assistant

Help QC Specialists enforce quality gates, verify compliance with standards, and track quality metrics across the development lifecycle.

---

Core Capabilities

1. Quality Gates

Define pass/fail criteria at each stage:

Gate: Idea → PBI

• Problem statement present
• Business value articulated
• No technical solution prescribed
• Target users identified

Gate: PBI → Development

• Acceptance criteria in GIVEN/WHEN/THEN
• Out of scope defined
• Dependencies identified
• Design approved (if applicable)

Gate: Development → QA

• Code review approved
• Unit tests >80% coverage
• No P1/P2 linting errors
• Documentation updated

Gate: QA → Release

• All test cases executed
• No open P1/P2 bugs
• Regression suite passed
• PO sign-off received

2. Compliance Verification

• Code follows architecture patterns
• Security requirements met
• Accessibility standards (WCAG 2.1 AA)
• Performance benchmarks

3. Audit Trail

Track artifact lifecycle:

{Artifact} | {Action} | {By} | {Date} | {Notes}

4. Quality Metrics

Code Quality

• Cyclomatic complexity
• Code coverage %
• Technical debt ratio
• Duplication %

Process Quality

• Defect escape rate
• First-time-right %
• Cycle time
• Lead time

---

Quality Gate Checklists

Pre-Development Checklist

## Quality Gate: PBI Ready for Development

**PBI:** {PBI-ID}
**Reviewer:** {Name}
**Date:** {Date}

### Requirements

- [ ] Clear problem statement
- [ ] User value articulated
- [ ] Acceptance criteria in GIVEN/WHEN/THEN format
- [ ] Out of scope explicitly listed

### Design

- [ ] Design spec approved (if UI changes)
- [ ] API contract defined (if backend changes)
- [ ] Database changes documented (if applicable)

### Dependencies

- [ ] Upstream dependencies identified
- [ ] No blocking dependencies
- [ ] Integration points documented

### Gate Status: PASS / FAIL / CONDITIONAL

**Notes:**
{Any concerns or conditions}

Pre-Release Checklist

## Quality Gate: Ready for Release

**Feature:** {Feature name}
**Release:** {Version}
**Date:** {Date}

### Testing

- [ ] All test cases executed
- [ ] Pass rate: \_\_\_\_%
- [ ] No open P1 bugs
- [ ] No open P2 bugs (or exceptions approved)

### Code Quality

- [ ] Code review approved
- [ ] Coverage > 80%
- [ ] No security vulnerabilities
- [ ] Performance benchmarks met

### Documentation

- [ ] User documentation updated
- [ ] API documentation current
- [ ] Release notes drafted

### Sign-Offs

- [ ] QA Lead: **\*\***\_**\*\*** Date: **\_\_\_**
- [ ] Dev Lead: **\*\***\_**\*\*** Date: **\_\_\_**
- [ ] PO: **\*\*\*\***\_\_**\*\*\*\*** Date: **\_\_\_**

### Gate Status: PASS / FAIL

**Release Decision:**
{Go / No-Go with notes}

---

Workflow Integration

Running Quality Gate

When user runs $quality-gate {artifact-or-pr}:

1. Identify gate type based on artifact/stage
2. Load appropriate checklist
3. Verify each criterion
4. Generate pass/fail report
5. Log in audit trail

---

Metrics Dashboard Template

## Quality Metrics - Sprint {N}

### Code Quality

| Metric      | Target | Actual | Trend |
| ----------- | ------ | ------ | ----- |
| Coverage    | >80%   |        | ↑↓→   |
| Complexity  | <15    |        |       |
| Duplication | <5%    |        |       |
| Debt Ratio  | <10%   |        |       |

### Process Quality

| Metric            | Target | Actual |
| ----------------- | ------ | ------ |
| Defect Escape     | <5%    |        |
| First-Time-Right  | >90%   |        |
| Avg Review Cycles | <2     |        |

### Defect Trends

| Sprint | Found | Fixed | Escaped |
| ------ | ----- | ----- | ------- |
| N-2    |       |       |         |
| N-1    |       |       |         |
| N      |       |       |         |

---

Output Conventions

File Naming

{YYMMDD}-qc-gate-{stage}-{slug}.md
{YYMMDD}-qc-audit-{feature}.md
{YYMMDD}-qc-metrics-sprint-{n}.md

---

Quality Checklist

Before completing QC artifacts:

• All checklist items verified
• Evidence provided for critical items
• Sign-offs captured
• Gate status clearly stated
• Audit trail updated

Related

• tdd-spec
• code-review

---

[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.

• docs/project-reference/domain-entities-reference.md — Domain entity catalog, relationships, cross-service sync (read when task involves business entities/models) (read directly when relevant; do not rely on hook-injected conversation text)

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

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

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, debug, architecture), advanced techniques (spiral refinement, hypothesis testing, convergence), and meta-strategies (uncertainty handling, revision cascades).

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

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 — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

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/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[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. Any supported AI tool may execute when this shared context and local docs are available.

1. DETECT: Match prompt against workflow catalog
2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
3. ASK (REQUIRED FORMAT): Use a direct user question with this structure unless the user explicitly invoked a workflow/skill and the local protocol treats explicit invocation as confirmation:
   • Question: "Which workflow do you want to activate?"
   • Option 1: "Activate [BestMatch Workflow] (Recommended)"
   • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
5. CREATE TASKS: task tracking for ALL workflow steps
6. EXECUTE: Follow each step in sequence [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.

[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/$security/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [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.