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qa-agent-testing质量保证 Agent 测试

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

总安装

2,517

周安装

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GitHub Stars

60

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882
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:qa-agent-testing(质量保证 Agent 测试)
来源仓库:https://github.com/vasilyu1983/ai-agents-public
仓库路径:skills/qa-agent-testing
安装命令:
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill qa-agent-testing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill qa-agent-testing

简介

qa-agent-testing 用于辅助测试设计、自动化测试、用例整理和回归验证,适合编写单元测试或定位问题。

  • 适用于需要生成测试计划、端到端测试或分析失败日志的场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 需确认项目测试框架和运行命令,避免误改真实逻辑。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

QA Agent Testing (Jan 2026)

Design and run reliable evaluation suites for LLM agents/personas, including tool-using and multi-agent systems.

Default QA Workflow

  1. Define the Persona Under Test (PUT): scope, out-of-scope, and safety boundaries.
  2. Define 10 representative tasks (Must Ace).
  3. Define 5 refusal edge cases (Must Decline + redirect).
  4. Define an output contract (format, tone, structure, citations).
  5. Run the suite with determinism controls and tool tracing.
  6. Score with the 6-dimension rubric; track variance across reruns.
  7. Log baselines and regressions; gate merges/deploys on thresholds.

Use the copy-paste templates in assets/ for day-0 setup.

Determinism and Flake Control

  • Control inputs: pin prompts/config, fixtures, stable tool responses, frozen time/timezone where possible.
  • Control sampling: fixed seeds/temperatures where supported; log model/config versions.
  • Record tool traces: tool name, args, outputs, latency, errors, retries, and side effects.

Two-Layer Evaluation (2026)

Evaluate reasoning and action layers separately:

LayerWhat to TestKey Metrics
ReasoningPlanning, decision-making, intentIntent resolution, task adhesion, context retention
ActionTool calls, execution, side effectsTool call accuracy, completion rate, error recovery

Evaluation Dimensions (Score What Matters)

DimensionWhat to MeasureLevel
Task successCorrect outcome and constraints metAgent
Safety/policyCorrect refusals and safe alternativesAgent
ReliabilityStability across reruns and small prompt changesAgent
Latency/costBudgets per task and per suiteBusiness
DebuggabilityFailures produce evidence (logs, traces)Agent
Factual groundingHallucination rate, citation accuracyModel
Bias detectionFairness across demographic inputsModel

CI Economics

  • PR gate: small, high-signal smoke eval suite.
  • Scheduled: full scenario suites, adversarial inputs, and cost/latency regression checks (track separately from quality scoring).

Robustness and Security Tests (Recommended)

  • Metamorphic tests: run small, meaning-preserving prompt/input rewrites; enforce invariants on outputs.
  • Prompt injection tests: treat tool outputs, retrieved text, and user-provided documents as untrusted; verify the agent does not follow embedded instructions that conflict with system/developer constraints.
  • Tool fault injection: simulate timeouts, retries, partial data, and tool errors; verify graceful recovery.
  • Differential testing: compare behavior across model/config versions for regressions and unexpected shifts.

Do / Avoid

Do:

  • Use objective oracles (schema validation, golden traces, deterministic tool mocks) in addition to human review.
  • Quarantine flaky evals with owners and expiry, just like flaky tests in CI.

Avoid:

  • Evaluating only "happy prompts" with no tool failures and no adversarial inputs.
  • Letting self-evaluations substitute for ground-truth checks.

Quick Reference

NeedUseLocation
Build the 10 tasksTask patterns + examplesreferences/test-case-design.md
Design refusalsRefusal categories + templatesreferences/refusal-patterns.md
Score runsDetailed rubric + thresholdsreferences/scoring-rubric.md
Compute suite math quicklyCLI utility scriptscripts/score_suite.py
Manage regressionsRe-run workflow + baseline policyreferences/regression-protocol.md
Sandbox toolsIsolation tiers + hardeningreferences/tool-sandboxing.md
Test multi-agent systemsCoordination patterns + suite templatereferences/multi-agent-testing.md
Use LLM-as-judge safelyBiases + mitigationsreferences/llm-judge-limitations.md
Test prompt injection attacksInjection taxonomy + test casesreferences/prompt-injection-testing.md
Detect hallucinationsDetection methods + scoringreferences/hallucination-detection.md
Design eval datasetsDataset construction + maintenancereferences/eval-dataset-design.md
Start from templatesHarness + scoring sheet + logassets/

Decision Tree

Testing an agent?
  - New agent?
    - Create QA harness -> Define 10 tasks + 5 refusals -> Run baseline
  - Prompt changed?
    - Re-run full 15-check suite -> Compare to baseline
  - Tool/knowledge changed?
    - Re-run affected tests -> Log in regression log
  - Quality review?
    - Score against rubric -> Identify weak areas -> Fix prompt

Scoring and Gates

  • Score each run with the 6-dimension rubric (0-3 each; max 18 per task).
  • Prefer suite-level gating that accounts for variance; avoid treating non-determinism as a free pass.
  • Use scripts/score_suite.py to compute averages, normalized scores, and basic PASS/CONDITIONAL/FAIL classification.
  • For detailed methodology (including judge calibration and variance metrics), see references/scoring-rubric.md.

Navigation

Resources

  • references/test-case-design.md - 10-task patterns + validation + metamorphic add-ons
  • references/refusal-patterns.md - refusal categories + response templates + test tactics
  • references/scoring-rubric.md - scoring guide, thresholds, variance metrics, judge calibration
  • references/regression-protocol.md - re-run scope, baseline policy, recovery procedures
  • references/tool-sandboxing.md - sandbox tiers, tool hardening, injection/exfil test ideas
  • references/multi-agent-testing.md - coordination testing patterns + suite template
  • references/llm-judge-limitations.md - LLM-as-judge biases, limits, mitigations
  • references/prompt-injection-testing.md - Injection taxonomy, test cases, and defense validation
  • references/hallucination-detection.md - Hallucination detection methods, scoring, and benchmarks
  • references/eval-dataset-design.md - Evaluation dataset construction, versioning, and maintenance

Templates

  • assets/qa-harness-template.md - copy-paste harness
  • assets/scoring-sheet.md - scoring tracker
  • assets/regression-log.md - version tracking

External Resources

See data/sources.json for:

  • LLM evaluation research
  • Red-teaming methodologies
  • Prompt testing frameworks

Related Skills

Quick Start

  1. Copy assets/qa-harness-template.md
  2. Fill in PUT (Persona Under Test) section
  3. Define 10 representative tasks for your agent
  4. Add 5 refusal edge cases
  5. Specify output contracts
  6. Run baseline test
  7. Log results in regression log
Success Criteria: Each of the 10 tasks scores >= 12/18 and each refusal scores >= 2/3 (or PASS by your policy oracle), with stable results across reruns and no new hard failures.

Fact-Checking

  • Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
  • Prefer primary sources; report source links and dates for volatile information.
  • If web access is unavailable, state the limitation and mark guidance as unverified.

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能力 1

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能力 2

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能力 3

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能力 4

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.76%
按下载量换算254

Antigravity

22.16%
按下载量换算195

Cursor

17.3%
按下载量换算153

OpenCode

11.42%
按下载量换算101

Gemini CLI

6.58%
按下载量换算58

Codex

3.38%
按下载量换算30

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Snyk

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