test-plan-review

Test Plan Reviewer

Internal orchestrator that reviews and scores a test plan using the quality rubric (5 criteria, 0-2 each, 10-point scale). Auto-revises failing plans and re-scores up to 2 times.

Usage

This skill is not user-invocable. It is called by:

• test-plan.create (Step 4)
• automation/orchestrator flows that need score + auto-revision behavior

Inputs

From arguments

Parse $ARGUMENTS to extract:

1. Feature directory (required): path to directory containing TestPlan.md

Auto-detection

If no arguments provided and test-plan.create just generated a test plan in this session, use that feature directory automatically.

Process

Step 0: Python dependencies

Install the test-plan package (makes all scripts importable):

(cd $(git -C ${CLAUDE_SKILL_DIR} rev-parse --show-toplevel) && uv sync --extra dev)

If installation fails, inform the user and do NOT proceed. Once installed, all Python scripts will work from any directory.

Step 1: Read Test Plan and Source Strategy

1. Read <feature_dir>/TestPlan.md

2. Read frontmatter to extract source_key:

   (cd $(git -C ${CLAUDE_SKILL_DIR} rev-parse --show-toplevel) && uv run python scripts/frontmatter.py read <feature_dir>/TestPlan.md)
   

3. Fetch the source strategy from Jira using the source_key:

   mcp__atlassian__getJiraIssue with issueIdOrKey=<source_key>
   

   If MCP is unavailable, check for a local strategy file in artifacts/strat-tasks/<source_key>.md (rfe-creator convention). If neither is available, warn the user that grounding and scope fidelity scoring will be degraded, and proceed with the test plan content only.

4. Store the raw strategy text for passing to sub-agents.

Step 2: Score (fork)

Read the score agent prompt from ${CLAUDE_SKILL_DIR}/prompts/score-agent.md.

Launch a forked score agent with these substitutions:

• {FEATURE_DIR} = feature directory path
• {TEST_PLAN_PATH} = <feature_dir>/TestPlan.md
• {STRATEGY_TEXT} = raw strategy description text from Step 1
• {CALIBRATION_DIR} = ${CLAUDE_SKILL_DIR}/calibration/

The score agent evaluates the test plan against a 5-criterion rubric (specificity, grounding, scope fidelity, actionability, consistency) and returns a structured assessment with per-criterion scores and a grounding cross-reference table.

Completeness checks performed by the score agent:

Section	Check
1.1 Purpose	Does it clearly state what is being tested and why?
1.2 Scope	Are in-scope and out-of-scope explicitly defined?
1.3 Test Objectives	Are there 3-7 concrete, measurable objectives?
2.1 Test Levels	Are the selected levels appropriate for the feature type?
2.3 Priorities	Are P0/P1/P2 definitions specific to this feature, not generic?
3.1 Cluster Config	Are versions and dependencies specified or marked TBD?
3.2 Test Data	Are test data requirements concrete enough to act on?
4 Endpoints/Methods	Are entries grounded in source documents, not fabricated?
6.1 E2E Scenarios	Is the E2E Scenario Summary populated with TC-E2E-* entries? (Note: expected to be empty until create-cases runs)
6.2 E2E Coverage	Does each P0 endpoint from Section 4 have E2E scenario coverage in Section 6.2? (Note: expected to be empty until create-cases runs)
7.1 Disconnected	Addressed with testing considerations or explicitly marked Not Applicable with justification?
7.2 Upgrade	Addressed with testing considerations or explicitly marked Not Applicable with justification?
7.3 Performance	Addressed with testing considerations or explicitly marked Not Applicable with justification?
7.4 RBAC	Addressed with testing considerations or explicitly marked Not Applicable with justification?
8 Risks	Are risks specific to this feature, not boilerplate?
9 Environment	Is there enough detail to set up a test environment?

Step 3: Review (fork)

Read the review agent prompt from ${CLAUDE_SKILL_DIR}/prompts/review-agent.md.

Launch a forked review agent with these substitutions:

• {FEATURE_DIR} = feature directory path
• {ASSESSMENT_TEXT} = full output from the score agent (Step 2)
• {FIRST_PASS} = true (first assessment cycle)

The review agent writes <feature_dir>/TestPlanReview.md with rubric scores, feedback, and validated frontmatter.

Consistency checks performed by the review agent:

• Do the endpoints in Section 4 align with the scope in Section 1.2?
• Do the test levels in Section 2.1 match the interface types in Section 4?
• Are priority assignments in Section 4 consistent with the definitions in Section 2.3?
• Does Section 10.2 list all endpoints from Section 4?
• Are NFR categories in Section 7 consistent with the feature scope? (e.g., a feature that pulls images should not mark Disconnected as N/A)
• Does Section 6.2 E2E Coverage Matrix include all P0 endpoints from Section 4? (Note: expected to be empty until create-cases runs)

Step 4: Check Criteria and Revise (max 2 cycles)

After the review agent completes, read the review frontmatter:

(cd $(git -C ${CLAUDE_SKILL_DIR} rev-parse --show-toplevel) && uv run python scripts/frontmatter.py read <feature_dir>/TestPlanReview.md)

If all five criteria in scores.* are 2, proceed to Step 5 (done).

If any criterion in scores.* is < 2, enter the revision loop.

Revision Loop

Initialize cycle counter: reassess_cycle=0

4a. Filter for revision:

(cd $(git -C ${CLAUDE_SKILL_DIR} rev-parse --show-toplevel) && uv run python scripts/filter_for_revision.py <feature_dir>)

If output is SKIP, stop the loop and proceed to Step 5.

4b. Launch revise agent (fork):

Read the revise agent prompt from ${CLAUDE_SKILL_DIR}/prompts/revise-agent.md.

Launch with substitutions:

• {FEATURE_DIR} = feature directory path
• {STRATEGY_TEXT} = raw strategy text from Step 1

The revise agent edits TestPlan.md (only sections mapped to failing criteria) and sets auto_revised=true.

4c. Check if reassessment is needed:

(cd $(git -C ${CLAUDE_SKILL_DIR} rev-parse --show-toplevel) && uv run python scripts/frontmatter.py read <feature_dir>/TestPlanReview.md)

If auto_revised is false, the revise agent found nothing to change — stop the loop.

Increment reassess_cycle. If reassess_cycle >= 2, stop — max cycles reached. Proceed to Step 5.

4d. Save cumulative state:

(cd $(git -C ${CLAUDE_SKILL_DIR} rev-parse --show-toplevel) && uv run python scripts/preserve_review_state.py save <feature_dir>)

4e. Re-score:

Delete the existing review file to force a clean re-assessment:

rm <feature_dir>/TestPlanReview.md

Repeat Step 2 (score agent) with the revised TestPlan.md.

4f. Re-review:

Repeat Step 3 (review agent) with {FIRST_PASS}=false.

4g. Restore before_scores and revision history:

(cd $(git -C ${CLAUDE_SKILL_DIR} rev-parse --show-toplevel) && uv run python scripts/preserve_review_state.py restore <feature_dir>)

4h. Check criteria again:

Read the review frontmatter. If all criteria are now 2, stop. If any criterion remains < 2 and cycles remain, go back to 4a. If cycles are exhausted, stop and proceed to Step 5.

Step 5: Present Results

Read the final review file and present a summary to the user:

## Test Plan Review — {feature_name}

**Score: {score}/10 — Verdict: {verdict}**

| Criterion | Score |
|-----------|-------|
| Specificity | {n}/2 |
| Grounding | {n}/2 |
| Scope Fidelity | {n}/2 |
| Actionability | {n}/2 |
| Consistency | {n}/2 |

{If before_score differs from score:}
**Delta: {before_score} → {score} ({+/-difference})**

{If verdict = Ready:}
The test plan is ready for test case generation. Run `/test-plan-create-cases <feature_dir>` to proceed.

{If verdict = Revise (after max cycles):}
The test plan improved but still has issues. Review `<feature_dir>/TestPlanReview.md` for remaining feedback. Consider providing additional source documents (ADR, API spec) to resolve grounding gaps.

{If verdict = Rework:}
The test plan needs significant rework. This may indicate the source strategy lacks sufficient detail. Review `<feature_dir>/TestPlanReview.md` for specific issues.

{If this plan is already in an open PR and reviewer comments exist:}
Use `/test-plan-resolve-feedback <PR_URL>` to triage and apply PR feedback items.

Anti-hallucination Rules

When reviewing and suggesting improvements, the score agent MUST follow these constraints:

NEVER:

• Invent resolution paths for TBDs (e.g., "check version in ADR section 3" when no ADR exists or that section doesn't specify versions)
• Add specific requirements, API endpoints, or version constraints not present in source documents
• Fabricate documentation references ("see design doc for details" when no design doc exists)
• Assume information exists in documents without verifying
• Create specificity improvements by inventing details

ALWAYS:

• Leave TBD as plain "TBD" if the strategy doesn't specify where to find the information
• Ground all improvements in actual source document content (strategy, ADR, additional_docs)
• Flag missing information as a gap rather than inventing a solution
• Defer to TestPlanGaps.md for unresolved items
• Only suggest changes that are directly traceable to source material

Why these rules matter: The reviewer's job is to assess completeness and consistency against source documents, not to fill gaps with assumptions. Inventing resolution paths or fabricating details creates false confidence - better to acknowledge gaps explicitly so they can be resolved with real documentation.

What This Skill Does NOT Do

• Does NOT generate test plans (use /test-plan-create)
• Does NOT generate test cases (use /test-plan-create-cases)
• Does NOT modify the source strategy
• Does NOT submit anything to Jira
• Does NOT resolve GitHub PR comments (use /test-plan-resolve-feedback <PR_URL>)

$ARGUMENTS