Test Code Review

SkillDev tools

Lets your agent review test code for assertion, isolation, determinism, fixture, and maintainability issues.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Test Code Review skill

About this capability

Use this skill when you need to review test code for assertions, isolation, determinism, fixtures, and maintainability; triggers include 测试代码评审 and test code review.

What this skill tells your AI

The instructions your AI receives, as published by naodeng/awesome-qa-skills in skills/en/testing-types/test-code-review/SKILL.md and read by ahel’s review.

identify assertion, isolation, fixture, determinism, and maintainability risks from test code, run evidence, and change scope. Produce TCR-## findings. This Skill organizes traceable test-engineering quality candidates only; it does not execute tests or turn a design inventory into coverage, pass, or release evidence.

When to Use

  • When you need test code review candidates from test code, fixtures, assertions, dependency injection, run history, failure logs, change diffs, and CI configuration.
  • When you need selection rationale, applicability constraints, evidence gaps, and the smallest validation action.
  • When inputs are incomplete but a bounded first pass can preserve blocked or unassessed boundaries.

Do not use it to execute tests, invent behavior conclusions, replace a complete strategy, or accept risk for a Human.

Output Format Options

  • Use Markdown by default; use tables, JSON, or CSV only when explicitly requested or required by the delivery format.
  • Separate static analysis, unexecuted work, evidence states, and Human decisions; keep items unassessed, blocked, or NOT_RUN when runtime evidence is absent.

How to Use

  1. Read prompts/test-code-review.md and provide the objective, scope, material, environment, and evidence.
  2. Complete the known, missing, conflicting, stale, out_of_scope, and assumptions input audit before findings.
  3. Record TCR-## with the subject, preconditions, concern, source evidence, and validation, plus impact/priority, owner role, close condition, and evidence state.
  4. Preserve conflicts, unknown constraints, and open questions when evidence is incomplete.

Core Constraints

  • Do not execute tests, assume missing rules, versions, thresholds, data, or outcomes, or treat candidate counts as coverage proof.
  • File presence, names, design declarations, and Eval configuration are not runtime evidence.
  • Mark unknowns unassessed, blocked, or pending clarification instead of filling them with convention.
  • Do not edit requirements, code, test assets, or target systems.

Pre-delivery Check

  • Recorded the known, missing, conflicting, stale, out_of_scope, and assumptions input audit.
  • Every TCR-## has source, evidence state, impact/priority, owner role, close condition, and validation.
  • Facts, inferences, recommendations, unexecuted work, and Human decisions remain separate.
  • Findings are not execution results, coverage proof, or release claims.

Reference Files

  • Read evals/eval.yaml and matching cases for regression; configuration does not prove project results.
  • Use evals/trigger-prompts.csv and evals/local-rules.json for trigger checks; missing skill.selection evidence is BLOCKED.

Common Pitfalls

  • Do not turn a method name, file presence, or candidate count into test execution, coverage, pass, or release evidence when scope or evidence is incomplete.
  • Do not fill in missing rules, thresholds, data, environments, or results from convention; preserve unassessed, blocked, and pending items.
  • Do not expand this specialist design or review into a complete strategy, full test cases, runtime execution, or a release decision.

Best Practices

  • Complete the six-part input audit before selecting the smallest traceable and verifiable finding scope.
  • Keep the source, evidence state, impact/priority, owner role, close condition, validation method, and residual risk for every finding.
  • Write validation suggestions as next actions; do not upgrade package structure, candidate counts, or local Eval configuration into real quality conclusions.

Signals

GitHub stars
217
Forks
31
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
test-code-review
Source
github.com/naodeng/awesome-qa-skills