spec-audit

name	spec-audit
description	Audit and improve spec coverage for a given spec. Use when (1) a spec has low or 0% requirement coverage, (2) tests exist but lack @req tags, (3) code behaviors have drifted from the spec's success criteria, (4) you need to identify unspecified behaviors in the codebase. Covers the full audit loop: analyze coverage -> tag existing tests -> identify spec gaps -> propose spec updates.
user_invocable	true

Spec Coverage Audit

When to Use

• A spec shows low coverage in just spec-coverage
• Tests are tagged @spec:X but not linked to requirements (@req)
• You suspect the spec's success criteria don't match the implemented code
• Issue #85 work items for filling spec gaps

Audit Workflow

Follow these steps in order. Do not over-research — each step builds on the previous one.

Step 1: Get the current state (< 2 minutes)

Run these in parallel:

# Current coverage numbers (JSON for programmatic use)
just spec-coverage --json | jq '.specs.SPEC_NAME'

# The spec's success criteria
# Read specs/SPEC_NAME.md — focus on the "Success Criteria" section

From the JSON output, note:

• covered / total requirements
• uncovered — requirements needing @req tags or new tests
• unlinked_tests — tests tagged @spec but missing @req (these are the quick wins)

Step 2: Delegate to spec-tester agents

For a single spec: Spawn one spec-tester agent with the spec name and uncovered requirements list.

For multiple specs: Spawn spec-tester agents in parallel, one per spec:

Spawn these spec-tester agents in parallel:
- Agent 1: RUBRIC_SPEC, mode=tag-only, requirements: [list from Step 1]
- Agent 2: BUILD_AND_DEPLOY_SPEC, mode=tag-only, requirements: [list from Step 1]
- Agent 3: AUTHENTICATION_SPEC, mode=full, requirements: [list from Step 1]

For a large spec with many requirements: Split into requirement groups and spawn parallel agents:

Spawn these spec-tester agents in parallel for RUBRIC_SPEC:
- Agent 1: requirements in "Parsing & Serialization" category
- Agent 2: requirements in "CRUD Lifecycle" category
- Agent 3: requirements in "AI-Powered Generation" category

Each agent reads the spec, tags existing tests, writes new tests if needed, and verifies.

Step 3: Collect results and verify

After agents return, run the global check:

just spec-coverage    # verify overall improvement
just test-server      # full suite still passes

Step 4: Identify spec drift (only if asked)

Only do this if the user asks to find unspecified behaviors.

This requires reading implementation code — spawn parallel Explore agents per layer:

Spawn these explore agents in parallel:
- Agent 1: Read all router endpoints for this spec's domain, list business rules
- Agent 2: Read all service methods for this spec's domain, list edge cases and side effects
- Agent 3: Read all frontend components for this spec's domain, list user interactions

Compare findings to the spec's success criteria. Look for:

• CRUD operations not in success criteria (create, edit, delete)
• Phase/workflow preconditions (must be in phase X, must have Y first)
• Side effects (background jobs, MLflow sync, cache invalidation)
• Validation rules (input constraints, error responses)
• AI/external service integration (generation, export, sync)

Step 5: Propose spec additions (protected operation)

Draft new success criteria grouped by category. Present to user before editing — /specs/ files require approval.

Tagging Reference

Framework	Format	Scope
pytest	@pytest.mark.req("Exact text from success criteria")	Per-test (decorator)
Playwright	tag: ['@spec:X', '@req:Exact text from success criteria']	Per-test (in test options)
Vitest	// @req Exact text from success criteria	Per-file only (analyzer limitation)

Critical: The @req text must match a - [ ] item from the spec exactly.

Vitest limitation: The analyzer caches one @req per vitest file. If a file covers multiple requirements, add @req markers to pytest or Playwright tests for the additional requirements instead.

Anti-Patterns

• Don't spawn broad research agents before reading the coverage JSON. The JSON tells you exactly what's covered and uncovered.
• Don't read all implementation code up front. Start with tagging existing tests (Step 2). Only read implementation code for spec drift (Step 4).
• Don't write new tests before tagging existing ones. Unlinked tests are free coverage — just add markers.
• Don't guess at @req text. Copy it exactly from the spec's - [ ] items.
• Don't put multiple // @req comments in one vitest file expecting them all to be picked up. Only the first one works.
• Don't run each spec sequentially when auditing multiple specs. Spawn parallel agents.

Example: Auditing RUBRIC_SPEC

# Step 1: Get state
just spec-coverage --json | jq '.specs.RUBRIC_SPEC'
# Shows: 0/10 covered, 51 unlinked tests

# Step 2: Spawn spec-tester agent
# Agent reads spec, tags 10 existing tests with @req markers

# Step 3: Verify
just test-spec RUBRIC_SPEC   # 30 passed
just spec-coverage           # RUBRIC_SPEC now 10/10 (100%)

# Step 4: Spec drift analysis (user asked)
# Spawned 3 explore agents in parallel -> found 15 unspecified behaviors

# Step 5: Proposed 15 new success criteria -> user approved -> 10/25 covered

Example: Auditing all low-coverage specs in parallel

# Step 1: Get state for all specs
just spec-coverage --json | jq '[.specs | to_entries[] | select(.value.coverage_pct < 50)] | .[].key'
# Returns: RUBRIC_SPEC, BUILD_AND_DEPLOY_SPEC, DESIGN_SYSTEM_SPEC, UI_COMPONENTS_SPEC

# Step 2: Spawn 4 spec-tester agents in parallel (one per spec, mode=tag-only)
# Each agent independently reads its spec, tags tests, verifies

# Step 3: Collect results, run full suite
just test-server && just spec-coverage

Reference

• Spec files: specs/*.md
• Coverage analyzer: tools/spec_coverage_analyzer.py
• Coverage map: specs/SPEC_COVERAGE_MAP.md
• Spec-tester agent: .claude/agents/spec-tester.md
• Test tagging conventions: .claude/skills/verification-testing/SKILL.md