EasyPlatform tdd-spec-review

[Code Quality] Review test specifications for coverage, completeness, and correctness before implementation. AI self-review gate after /tdd-spec.

install
source · Clone the upstream repo
git clone https://github.com/duc01226/EasyPlatform
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/duc01226/EasyPlatform "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/tdd-spec-review" ~/.claude/skills/duc01226-easyplatform-tdd-spec-review && rm -rf "$T"
manifest: .claude/skills/tdd-spec-review/SKILL.md
source content

[IMPORTANT] Use

TaskCreate
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.

Evidence Gate: MANDATORY IMPORTANT MUST ATTENTION — every claim, finding, and recommendation requires

file:line
proof or traced evidence with confidence percentage (>80% to act, <80% must verify first).

OOP & DRY Enforcement: MANDATORY IMPORTANT MUST ATTENTION — flag duplicated patterns that should be extracted to a base class, generic, or helper. Classes in the same group or suffix (ex *Entity, *Dto, *Service, etc...) MUST ATTENTION inherit a common base (even if empty now — enables future shared logic and child overrides). Verify project has code linting/analyzer configured for the stack.

External Memory: For complex or lengthy work (research, analysis, scan, review), write intermediate findings and final results to a report file in

plans/reports/
— prevents context loss and serves as deliverable.

<!-- SYNC:critical-thinking-mindset -->

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.

<!-- /SYNC:critical-thinking-mindset --> <!-- SYNC:ai-mistake-prevention -->

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.
<!-- /SYNC:ai-mistake-prevention --> <!-- SYNC:double-round-trip-review -->

Deep Multi-Round Review — Escalating rounds. Round 1 in main session. Round 2+ and EVERY recursive re-review iteration MUST use a fresh sub-agent.

Round 1: Main-session review. Read target files, build understanding, note issues. Output baseline findings.

Round 2: MANDATORY fresh sub-agent review — see

SYNC:fresh-context-review
for the spawn mechanism and
SYNC:review-protocol-injection
for the canonical Agent prompt template. The sub-agent re-reads ALL files from scratch with ZERO Round 1 memory. It must catch:

  • Cross-cutting concerns missed in Round 1
  • Interaction bugs between changed files
  • Convention drift (new code vs existing patterns)
  • Missing pieces that should exist but don't
  • Subtle edge cases the main session rationalized away

Round 3+ (recursive after fixes): After ANY fix cycle, MANDATORY fresh sub-agent re-review. Spawn a NEW Agent tool call each iteration — never reuse Round 2's agent. Each new agent re-reads ALL files from scratch with full protocol injection. Continue until PASS or 3 fresh-subagent rounds max, then escalate to user via

AskUserQuestion
.

Rules:

  • NEVER declare PASS after Round 1 alone
  • NEVER reuse a sub-agent across rounds — every iteration spawns a NEW Agent call
  • Main agent READS sub-agent reports but MUST NOT filter, reinterpret, or override findings
  • Max 3 fresh-subagent rounds per review — if still FAIL, escalate via
    AskUserQuestion
    (do NOT silently loop)
  • Track round count in conversation context (session-scoped)
  • Final verdict must incorporate ALL rounds

Report must include

## Round N Findings (Fresh Sub-Agent)
for every round N≥2.

<!-- /SYNC:double-round-trip-review --> <!-- SYNC:fresh-context-review -->

Fresh Sub-Agent Review — Eliminate orchestrator confirmation bias via isolated sub-agents.

Why: The main agent knows what it (or

/cook
) just fixed and rationalizes findings accordingly. A fresh sub-agent has ZERO memory, re-reads from scratch, and catches what the main agent dismissed. Sub-agent bias is mitigated by (1) fresh context, (2) verbatim protocol injection, (3) main agent not filtering the report.

When: Round 2 of ANY review AND every recursive re-review iteration after fixes. NOT needed when Round 1 already PASSes with zero issues.

How:

  1. Spawn a NEW
    Agent
    tool call — use
    code-reviewer
    subagent_type for code reviews,
    general-purpose
    for plan/doc/artifact reviews
  2. Inject ALL required review protocols VERBATIM into the prompt — see
    SYNC:review-protocol-injection
    for the full list and template. Never reference protocols by file path; AI compliance drops behind file-read indirection (see
    SYNC:shared-protocol-duplication-policy
    )
  3. Sub-agent re-reads ALL target files from scratch via its own tool calls — never pass file contents inline in the prompt
  4. Sub-agent writes structured report to
    plans/reports/{review-type}-round{N}-{date}.md
  5. Main agent reads the report, integrates findings into its own report, DOES NOT override or filter

Rules:

  • NEVER reuse a sub-agent across rounds — every iteration spawns a NEW
    Agent
    call
  • NEVER skip fresh-subagent review because "last round was clean" — every fix triggers a fresh round
  • Max 3 fresh-subagent rounds per review — escalate via
    AskUserQuestion
    if still failing; do NOT silently loop or fall back to any prior protocol
  • Track iteration count in conversation context (session-scoped, no persistent files)
<!-- /SYNC:fresh-context-review --> <!-- SYNC:review-protocol-injection -->

Review Protocol Injection — Every fresh sub-agent review prompt MUST embed 10 protocol blocks VERBATIM. The template below has ALL 10 bodies already expanded inline. Copy the template wholesale into the Agent call's

prompt
field at runtime, replacing only the
{placeholders}
in Task / Round / Reference Docs / Target Files / Output sections with context-specific values. Do NOT touch the embedded protocol sections.

Why inline expansion: Placeholder markers would force file-read indirection at runtime. AI compliance drops significantly behind indirection (see

SYNC:shared-protocol-duplication-policy
). Therefore the template carries all 10 protocol bodies pre-embedded.

Subagent Type Selection

  • code-reviewer
    — for code reviews (reviewing source files, git diffs, implementation)
  • general-purpose
    — for plan / doc / artifact reviews (reviewing markdown plans, docs, specs)

Canonical Agent Call Template (Copy Verbatim)

Agent({
  description: "Fresh Round {N} review",
  subagent_type: "code-reviewer",
  prompt: `
## Task
{review-specific task — e.g., "Review all uncommitted changes for code quality" | "Review plan files under {plan-dir}" | "Review integration tests in {path}"}

## Round
Round {N}. You have ZERO memory of prior rounds. Re-read all target files from scratch via your own tool calls. Do NOT trust anything from the main agent beyond this prompt.

## Protocols (follow VERBATIM — these are non-negotiable)

### Evidence-Based Reasoning
Speculation is FORBIDDEN. Every claim needs proof.
1. Cite file:line, grep results, or framework docs for EVERY claim
2. Declare confidence: >80% act freely, 60-80% verify first, <60% DO NOT recommend
3. Cross-service validation required for architectural changes
4. "I don't have enough evidence" is valid and expected output
BLOCKED until: Evidence file path (file:line) provided; Grep search performed; 3+ similar patterns found; Confidence level stated.
Forbidden without proof: "obviously", "I think", "should be", "probably", "this is because".
If incomplete → output: "Insufficient evidence. Verified: [...]. Not verified: [...]."

### Bug Detection
MUST check categories 1-4 for EVERY review. Never skip.
1. Null Safety: Can params/returns be null? Are they guarded? Optional chaining gaps? .find() returns checked?
2. Boundary Conditions: Off-by-one (< vs <=)? Empty collections handled? Zero/negative values? Max limits?
3. Error Handling: Try-catch scope correct? Silent swallowed exceptions? Error types specific? Cleanup in finally?
4. Resource Management: Connections/streams closed? Subscriptions unsubscribed on destroy? Timers cleared? Memory bounded?
5. Concurrency (if async): Missing await? Race conditions on shared state? Stale closures? Retry storms?
6. Stack-Specific: JS: === vs ==, typeof null. C#: async void, missing using, LINQ deferred execution.
Classify: CRITICAL (crash/corrupt) → FAIL | HIGH (incorrect behavior) → FAIL | MEDIUM (edge case) → WARN | LOW (defensive) → INFO.

### Design Patterns Quality
Priority checks for every code change:
1. DRY via OOP: Same-suffix classes (*Entity, *Dto, *Service) MUST share base class. 3+ similar patterns → extract to shared abstraction.
2. Right Responsibility: Logic in LOWEST layer (Entity > Domain Service > Application Service > Controller). Never business logic in controllers.
3. SOLID: Single responsibility (one reason to change). Open-closed (extend, don't modify). Liskov (subtypes substitutable). Interface segregation (small interfaces). Dependency inversion (depend on abstractions).
4. After extraction/move/rename: Grep ENTIRE scope for dangling references. Zero tolerance.
5. YAGNI gate: NEVER recommend patterns unless 3+ occurrences exist. Don't extract for hypothetical future use.
Anti-patterns to flag: God Object, Copy-Paste inheritance, Circular Dependency, Leaky Abstraction.

### Logic & Intention Review
Verify WHAT code does matches WHY it was changed.
1. Change Intention Check: Every changed file MUST serve the stated purpose. Flag unrelated changes as scope creep.
2. Happy Path Trace: Walk through one complete success scenario through changed code.
3. Error Path Trace: Walk through one failure/edge case scenario through changed code.
4. Acceptance Mapping: If plan context available, map every acceptance criterion to a code change.
NEVER mark review PASS without completing both traces (happy + error path).

### Test Spec Verification
Map changed code to test specifications.
1. From changed files → find TC-{FEAT}-{NNN} in docs/business-features/{Service}/detailed-features/{Feature}.md Section 15.
2. Every changed code path MUST map to a corresponding TC (or flag as "needs TC").
3. New functions/endpoints/handlers → flag for test spec creation.
4. Verify TC evidence fields point to actual code (file:line, not stale references).
5. Auth changes → TC-{FEAT}-02x exist? Data changes → TC-{FEAT}-01x exist?
6. If no specs exist → log gap and recommend /tdd-spec.
NEVER skip test mapping. Untested code paths are the #1 source of production bugs.

### Fix-Layer Accountability
NEVER fix at the crash site. Trace the full flow, fix at the owning layer. The crash site is a SYMPTOM, not the cause.
MANDATORY before ANY fix:
1. Trace full data flow — Map the complete path from data origin to crash site across ALL layers (storage → backend → API → frontend → UI). Identify where bad state ENTERS, not where it CRASHES.
2. Identify the invariant owner — Which layer's contract guarantees this value is valid? Fix at the LOWEST layer that owns the invariant, not the highest layer that consumes it.
3. One fix, maximum protection — If fix requires touching 3+ files with defensive checks, you are at the wrong layer — go lower.
4. Verify no bypass paths — Confirm all data flows through the fix point. Check for direct construction skipping factories, clone/spread without re-validation, raw data not wrapped in domain models, mutations outside the model layer.
BLOCKED until: Full data flow traced (origin → crash); Invariant owner identified with file:line evidence; All access sites audited (grep count); Fix layer justified (lowest layer that protects most consumers).
Anti-patterns (REJECT): "Fix it where it crashes" (crash site ≠ cause site, trace upstream); "Add defensive checks at every consumer" (scattered defense = wrong layer); "Both fix is safer" (pick ONE authoritative layer).

### Rationalization Prevention
AI skips steps via these evasions. Recognize and reject:
- "Too simple for a plan" → Simple + wrong assumptions = wasted time. Plan anyway.
- "I'll test after" → RED before GREEN. Write/verify test first.
- "Already searched" → Show grep evidence with file:line. No proof = no search.
- "Just do it" → Still need TaskCreate. Skip depth, never skip tracking.
- "Just a small fix" → Small fix in wrong location cascades. Verify file:line first.
- "Code is self-explanatory" → Future readers need evidence trail. Document anyway.
- "Combine steps to save time" → Combined steps dilute focus. Each step has distinct purpose.

### Graph-Assisted Investigation
MANDATORY when .code-graph/graph.db exists.
HARD-GATE: MUST run at least ONE graph command on key files before concluding any investigation.
Pattern: Grep finds files → trace --direction both reveals full system flow → Grep verifies details.
- Investigation/Scout: trace --direction both on 2-3 entry files
- Fix/Debug: callers_of on buggy function + tests_for
- Feature/Enhancement: connections on files to be modified
- Code Review: tests_for on changed functions
- Blast Radius: trace --direction downstream
CLI: python .claude/scripts/code_graph {command} --json. Use --node-mode file first (10-30x less noise), then --node-mode function for detail.

### Understand Code First
HARD-GATE: Do NOT write, plan, or fix until you READ existing code.
1. Search 3+ similar patterns (grep/glob) — cite file:line evidence.
2. Read existing files in target area — understand structure, base classes, conventions.
3. Run python .claude/scripts/code_graph trace <file> --direction both --json when .code-graph/graph.db exists.
4. Map dependencies via connections or callers_of — know what depends on your target.
5. Write investigation to .ai/workspace/analysis/ for non-trivial tasks (3+ files).
6. Re-read analysis file before implementing — never work from memory alone.
7. NEVER invent new patterns when existing ones work — match exactly or document deviation.
BLOCKED until: Read target files; Grep 3+ patterns; Graph trace (if graph.db exists); Assumptions verified with evidence.

## Reference Docs (READ before reviewing)
- docs/project-reference/code-review-rules.md
- {skill-specific reference docs — e.g., integration-test-reference.md for integration-test-review; backend-patterns-reference.md for backend reviews; frontend-patterns-reference.md for frontend reviews}

## Target Files
{explicit file list OR "run git diff to see uncommitted changes" OR "read all files under {plan-dir}"}

## Output
Write a structured report to plans/reports/{review-type}-round{N}-{date}.md with sections:
- Status: PASS | FAIL
- Issue Count: {number}
- Critical Issues (with file:line evidence)
- High Priority Issues (with file:line evidence)
- Medium / Low Issues
- Cross-cutting findings

Return the report path and status to the main agent.
Every finding MUST have file:line evidence. Speculation is forbidden.
`
})

Rules

  • DO copy the template wholesale — including all 10 embedded protocol sections
  • DO replace only the
    {placeholders}
    in Task / Round / Reference Docs / Target Files / Output sections with context-specific content
  • DO choose
    code-reviewer
    subagent_type for code reviews and
    general-purpose
    for plan / doc / artifact reviews
  • DO NOT paraphrase, summarize, or skip any protocol section
  • DO NOT pass file contents inline — the sub-agent reads via its own tool calls so it has a fresh context
  • DO NOT reference protocols by file path or tag name — the bodies are already embedded above
  • DO NOT introduce placeholder markers for the protocols — they must stay literally expanded
<!-- /SYNC:review-protocol-injection --> <!-- SYNC:graph-impact-analysis -->

Graph Impact Analysis — When

.code-graph/graph.db
exists, run
blast-radius --json
to detect ALL files affected by changes (7 edge types: CALLS, MESSAGE_BUS, API_ENDPOINT, TRIGGERS_EVENT, PRODUCES_EVENT, TRIGGERS_COMMAND_EVENT, INHERITS). Compute gap: impacted_files - changed_files = potentially stale files. Risk: <5 Low, 5-20 Medium, >20 High. Use
trace --direction downstream
for deep chains on high-impact files.

<!-- /SYNC:graph-impact-analysis -->
  • docs/test-specs/
    — Test specifications by module (cross-reference during review to verify TC completeness and avoid duplicates)
  • docs/project-reference/integration-test-reference.md
    — Integration test patterns, fixture setup, seeder conventions, lessons learned (MUST READ before reviewing/writing integration tests)

Quick Summary

Goal: Auto-review test specifications for coverage completeness, TC format correctness, and no missing test cases before implementation proceeds.

Key distinction: AI self-review (automatic), NOT user interview.

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

Adversarial Review Mindset (NON-NEGOTIABLE)

Default stance: SKEPTIC probing test coverage gaps, not confirming coverage completeness.

Coverage illusion trap: A spec with many TCs feels complete. But TCs that test implementation details, trivial happy paths, or vague outcomes provide false coverage confidence. This section forces quality challenge beyond count.

Adversarial Techniques (apply ALL before concluding)

1. Mutation Analysis Mindset For each TC: "If I changed ONE line of the implementation this TC is testing, would this TC fail?" If the TC would still pass after a subtle bug is introduced — it is not testing behavior, it is testing presence. Flag it.

2. Negative Test Adequacy List the 3 most likely production failures for this feature (invalid input, service timeout, data corruption, race condition). Does at least one TC cover each failure mode? If a production failure has no corresponding TC, it will not be caught until production.

3. TC Quality Challenge For each TC, check its assertion: "Could this TC PASS even if the feature is broken?" (e.g., "Response is 200 OK" without checking response body). Flag TCs where the assertion is too coarse to catch regressions.

4. Coverage Gap Hunt Identify code paths that EXIST but have NO corresponding TC. Common gaps: admin/superuser paths, concurrent access, partial data states, idempotency, rollback behavior. If a gap exists — flag it, don't rationalize it.

5. Boundary Condition Probe For each TC that tests a value (count, length, amount): "Is there a TC for the value-1, value, value+1 boundary?" Off-by-one errors are the most common business logic bug. If boundary TCs are missing, flag them.

6. Contrarian Pass Before writing any verdict, generate at least 2 sentences arguing the OPPOSITE conclusion. Then decide which argument is stronger.

Forbidden Patterns

  • "Coverage looks complete" → Count is NOT quality. Would a mutation pass these TCs?
  • "Happy path is tested" → The happy path is the least likely failure mode in production.
  • "Edge cases included" → Are they the RIGHT edge cases? Name the 3 most likely production failures.
  • "Assertions are clear" → Can the feature be broken while the assertion still passes?
  • Approving test specs without adversarial quality challenge → Forbidden.

Anti-Bias Gate (MANDATORY before finalizing verdict)

  • Applied mutation analysis to at least 1 TC per feature area
  • Listed 3 production failure modes and verified TCs cover them
  • Checked TC assertion specificity (can it pass even if feature breaks?)
  • Identified at least 1 coverage gap (code path with no TC)
  • Verified boundary TCs exist for value-based assertions
  • Generated at least 2 sentences arguing the opposite verdict

If any box is unchecked → adversarial review incomplete. Go back.

Workflow

  1. Locate test specs — Find TCs in feature doc Section 15 or
    docs/test-specs/
  2. Load source — Read stories/PBI/acceptance criteria that TCs should cover
  3. Evaluate checklist — Score each check
  4. Calculate coverage — % of stories/AC with corresponding TCs
  5. Classify — PASS/WARN/FAIL
  6. Output verdict

Checklist

Required (all must pass)

#CheckPresenceQuality Depth
1TC ID format — All TCs follow
TC-{FEATURE}-{NNN}
format
Do all TCs use the
TC-{FEATURE}-{NNN}
pattern?
Are IDs unique per TC? Does the FEATURE code match the actual feature?
2Story coverage — Every user story has at least one corresponding TCDoes every story ID appear in at least one TC?Does each TC actually test the story behavior, or does it just reference the story ID in a comment?
3AC coverage — Every acceptance criterion has a test caseIs every AC traceable to at least one TC?Does each AC have a TC that would FAIL if the AC is violated?
4Happy path — Each story has at least one happy path TCIs a happy path TC present per story?Does the happy path TC verify the full end-to-end scenario, or just a happy-path stub?
5Error path — Each story has at least one error/failure TCIs an error/failure TC present per story?Does the error TC verify the exact error response (code + message), not just that an error occurred?
6No duplicates — No duplicate TCs testing the same scenarioAre all TC IDs unique with distinct scenarios?Are there TCs that test the same scenario with slightly different input? Flag near-duplicates.
7Testable assertions — Each TC has clear expected result (not vague "should work")Does each TC have a specific expected result?Is each assertion specific enough to catch regressions? Would it pass if the return value is wrong?
8Authorization TCs — At least 1 TC per story verifying unauthorized access is rejectedIs an authorization TC present per story?Does the authorization TC test a realistic access scenario, not just "wrong role → 403 without body check"?

Recommended (>=50% should pass)

#CheckPresenceQuality Depth
1Edge cases — Boundary values, empty inputs, max limits testedAre edge case TCs listed?Are these the RIGHT edge cases? Do they cover the 3 most likely production failure modes for this feature?
2Integration points — Cross-service scenarios coveredAre cross-service TCs present where applicable?Do integration TCs verify actual data flow across services, or just that a downstream call was made?
3Performance TCs — Response time or throughput expectations where relevant; production-like data volume TCs if >1000 records expected (ref: protocol §4)Are performance TCs present where data volume or SLA expectations exist?Do performance TCs use production-like data volumes, not toy datasets that trivially pass?
4Security TCs — Auth, authorization, input validation testedAre security TCs present for auth, authz, and input validation?Do security TCs attempt realistic attack vectors (SQLi, over-posting, privilege escalation) not just "invalid token → 401"?
5Seed data TCs — If feature needs reference data, TCs verify data exists and seeder runs correctly (ref: protocol §2)If reference data is needed, does a seed data TC exist (or N/A)?If present, does the TC assert the exact seeded data shape, not just that the seeder ran without error?
6Data migration TCs — If schema changes exist, TCs verify data transforms correctly, rollback works, no data loss (ref: protocol §5)If schema changes exist, does a migration TC exist (or N/A)?If present, does the TC verify rollback behavior and zero data loss, not just forward migration success?

Output

## Test Spec Review Result

**Status:** PASS | WARN | FAIL
**TCs reviewed:** {count}
**Coverage:** {X}% of stories, {Y}% of acceptance criteria

### Coverage Matrix

| Story/AC | TC IDs | Happy | Error | Edge |
| -------- | ------ | ----- | ----- | ---- |

### Required ({X}/{Y})

- ✅/❌ Check description

### Recommended ({X}/{Y})

- ✅/⚠️ Check description

### Missing Coverage

- {Stories/AC without TCs}

### Verdict

{PROCEED | REVISE_FIRST}

Round 2+ : Fresh Sub-Agent Re-Review (MANDATORY)

Protocol:

SYNC:double-round-trip-review
+
SYNC:fresh-context-review
+
SYNC:review-protocol-injection
(all inlined above in this file).

After completing Round 1 checklist evaluation, spawn a fresh

general-purpose
sub-agent for Round 2 using the canonical Agent template from
SYNC:review-protocol-injection
above. Test specification reviews are NOT code reviews — use
subagent_type: "general-purpose"
. When constructing the Agent call prompt:

  1. Copy the Agent call shape from the
    SYNC:review-protocol-injection
    template verbatim
  2. Set
    subagent_type: "general-purpose"
  3. Embed the full verbatim body of these SYNC blocks:
    SYNC:evidence-based-reasoning
    ,
    SYNC:rationalization-prevention
    ,
    SYNC:understand-code-first
    (omit code-specific protocols like
    SYNC:bug-detection
    ,
    SYNC:design-patterns-quality
    ,
    SYNC:fix-layer-accountability
    which are not applicable to test specification artifacts)
  4. Set the Task as
    "Review the test specification artifacts for coverage completeness and quality. Focus on: implicit assumptions not validated, missing story/AC coverage, edge cases not addressed, cross-references not verified, vague expected results, duplicate TCs, missing authorization TCs."
  5. Set Target Files as the explicit test specification file paths being reviewed
  6. Set report path as
    plans/reports/tdd-spec-review-round{N}-{date}.md

After sub-agent returns:

  1. Read the sub-agent's report
  2. Integrate findings as
    ## Round {N} Findings (Fresh Sub-Agent)
    in the main report — DO NOT filter or override
  3. If FAIL: fix issues in the specs, then spawn a NEW Round N+1 fresh sub-agent (new Agent call — never reuse Round 2's agent)
  4. Max 3 fresh rounds — escalate to user via
    AskUserQuestion
    if still failing after 3 rounds
  5. Final verdict must incorporate findings from ALL rounds

Key Rules

  • FAIL blocks workflow — If FAIL, do NOT proceed to implementation.
  • Coverage >= 100% required — Every story and AC must have at least one TC.
  • No guessing — Reference specific TC IDs and story references.
  • Quality over quantity — Flag duplicate TCs, prefer fewer meaningful tests.

Next Steps

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS after completing this skill, you MUST ATTENTION use

AskUserQuestion
to present these options. Do NOT skip because the task seems "simple" or "obvious" — the user decides:

  • "/plan (Recommended)" — Create implementation plan with validated test specs
  • "/tdd-spec" — Re-generate specs if FAIL verdict
  • "/integration-test" — Generate integration test code from specs
  • "Skip, continue manually" — user decides

Closing Reminders

MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using

TaskCreate
BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION validate decisions with user via
AskUserQuestion
— never auto-decide. MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality. MANDATORY IMPORTANT MUST ATTENTION READ the following files before starting:

<!-- SYNC:double-round-trip-review:reminder -->
  • MANDATORY IMPORTANT MUST ATTENTION execute TWO review rounds. Round 2 delegates to fresh code-reviewer sub-agent (zero prior context) — never skip or combine with Round 1. <!-- /SYNC:double-round-trip-review:reminder --> <!-- SYNC:graph-impact-analysis:reminder -->
  • IMPORTANT MUST ATTENTION run graph blast-radius on changed files to find potentially stale consumers/handlers (when graph.db exists). <!-- /SYNC:graph-impact-analysis:reminder --> <!-- SYNC:critical-thinking-mindset:reminder -->
  • MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact. <!-- /SYNC:critical-thinking-mindset:reminder --> <!-- SYNC:ai-mistake-prevention:reminder -->
  • MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction. <!-- /SYNC:ai-mistake-prevention:reminder -->