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openclaw-qa-testingOpenClaw QA 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。

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1,307

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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

2

许可证

unknown

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:openclaw-qa-testing(OpenClaw QA 测试)
来源仓库:https://github.com/steipete/clawdis
仓库路径:skills/openclaw-qa-testing
安装命令:
npx skills add https://github.com/steipete/clawdis --skill openclaw-qa-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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/steipete/clawdis --skill openclaw-qa-testing

简介

用于辅助测试设计、自动化测试和回归验证。

  • 适合让 Agent 编写单元测试、端到端测试或根据日志定位问题。
  • 支持测试框架集成、用例整理和失败分析。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。
  • 使用时需确认运行命令、夹具数据及环境隔离要求。
  • openclaw-qa-testing 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

OpenClaw QA Testing

Use this skill for qa-lab / qa-channel work. Repo-local QA only.

Read first

  • docs/concepts/qa-e2e-automation.md
  • docs/help/testing.md
  • docs/channels/qa-channel.md
  • qa/README.md
  • qa/scenarios/index.md
  • extensions/qa-lab/src/suite.ts
  • extensions/qa-lab/src/character-eval.ts

Model policy

  • Live OpenAI lane: openai/gpt-5.4
  • Fast mode: on
  • Do not use:

- openai/gpt-5.4-pro - openai/gpt-5.4-mini

  • Only change model policy if the user explicitly asks.

Default workflow

  1. Read the scenario pack and current suite implementation.
  2. Decide lane:

- mock/dev: mock-openai - real validation: live-frontier

  1. For live OpenAI, use:
OPENCLAW_LIVE_OPENAI_KEY="${OPENAI_API_KEY}" \
pnpm openclaw qa suite \
  --provider-mode live-frontier \
  --model openai/gpt-5.4 \
  --alt-model openai/gpt-5.4 \
  --output-dir .artifacts/qa-e2e/run-all-live-frontier-<tag>
  1. Watch outputs:

- summary: .artifacts/qa-e2e/run-all-live-frontier-<tag>/qa-suite-summary.json - report: .artifacts/qa-e2e/run-all-live-frontier-<tag>/qa-suite-report.md

  1. If the user wants to watch the live UI, find the current openclaw-qa listen port and report http://127.0.0.1:<port>.
  2. If a scenario fails, fix the product or harness root cause, then rerun the full lane.

OTEL smoke

For local QA-lab OpenTelemetry validation, use:

pnpm qa:otel:smoke

This starts a local OTLP/HTTP trace receiver, runs the otel-trace-smoke scenario through qa-channel, decodes the emitted protobuf spans, and verifies the exported trace names and privacy contract. It does not require Opik, Langfuse, or external collector credentials.

Matrix live profiles

pnpm openclaw qa matrix defaults to the full all profile. Use explicit profiles for faster CI/release proof:

OPENCLAW_QA_MATRIX_NO_REPLY_WINDOW_MS=3000 \
pnpm openclaw qa matrix --profile fast --fail-fast
  • fast: release-critical transport contract, excluding generated image and deep E2EE recovery inventory.
  • transport, media, e2ee-smoke, e2ee-deep, e2ee-cli: sharded full Matrix coverage.
  • QA-Lab - All Lanes uses explicit fast Matrix on scheduled runs. Manual dispatch keeps matrix_profile=all as the default and always shards that full Matrix selection.

QA credentials and 1Password

  • Use op only inside tmux for QA secret lookup in this repo.
  • Quick auth check inside tmux:
op account list
  • Direct Telegram npm live test secrets currently live in 1Password item:

- vault: OpenClaw - item: Telegram E2E

  • That item is the first place to look for:

- OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN - OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN - OPENCLAW_QA_PROVIDER_MODE - OPENCLAW_NPM_TELEGRAM_PACKAGE_SPEC

  • Convex QA secrets currently live in 1Password items:

- vault: OpenClaw - item: OPENCLAW_QA_CONVEX_SITE_URL - item: OPENCLAW_QA_CONVEX_SECRET_MAINTAINER - item: OPENCLAW_QA_CONVEX_SECRET_CI

  • Additional related notes/login items seen during QA credential work:

- vault: Private - items: OPENCLAW QA, Convex, Telegram

  • If a required value is missing from those notes:

- do not guess - ask the maintainer/operator for the current value or the current 1Password item name - for Telegram direct runs, OPENCLAW_QA_TELEGRAM_GROUP_ID may be stored separately from Telegram E2E - for Convex runs, the leased Telegram credential should provide the Telegram group id and bot tokens together; do not require a separate OPENCLAW_QA_TELEGRAM_GROUP_ID - for Convex runs, prefer OpenClaw/OPENCLAW_QA_CONVEX_SITE_URL; if that is stale or unclear, ask for the active pool URL before running

  • Prefer direct Telegram envs for the npm Telegram Docker lane when available:
OPENCLAW_QA_TELEGRAM_GROUP_ID="..." \
OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN="..." \
OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN="..." \
OPENCLAW_QA_PROVIDER_MODE="mock-openai" \
OPENCLAW_NPM_TELEGRAM_PACKAGE_SPEC="openclaw@beta" \
pnpm test:docker:npm-telegram-live
  • Prefer Convex mode when the goal is stable shared QA infra:

- round-robin credential leasing - thinner wrapper for channel-specific setup - CLI/admin flows around the pooled credentials

  • Live npm Telegram Docker lane note:

- scripts/e2e/npm-telegram-live-runner.ts reads OPENCLAW_NPM_TELEGRAM_PROVIDER_MODE - do not assume OPENCLAW_QA_PROVIDER_MODE is consumed by that wrapper - if a 1Password note only gives OPENCLAW_QA_PROVIDER_MODE, map it explicitly to OPENCLAW_NPM_TELEGRAM_PROVIDER_MODE before running the Docker lane

  • Verified live shape:

- Convex mode can pass the real Docker lane without direct Telegram env vars - leased Telegram payload includes the group id coupled to the driver/SUT tokens - a real run of pnpm test:docker:npm-telegram-live passed with: - OPENCLAW_QA_CREDENTIAL_SOURCE=convex - OPENCLAW_QA_CREDENTIAL_ROLE=maintainer - OPENCLAW_QA_CONVEX_SITE_URL - OPENCLAW_QA_CONVEX_SECRET_MAINTAINER - OPENCLAW_NPM_TELEGRAM_PROVIDER_MODE=mock-openai

Character evals

Use qa character-eval for style/persona/vibe checks across multiple live models.

pnpm openclaw qa character-eval \
  --model openai/gpt-5.4,thinking=xhigh \
  --model openai/gpt-5.2,thinking=xhigh \
  --model openai/gpt-5,thinking=xhigh \
  --model anthropic/claude-opus-4-6,thinking=high \
  --model anthropic/claude-sonnet-4-6,thinking=high \
  --model zai/glm-5.1,thinking=high \
  --model moonshot/kimi-k2.5,thinking=high \
  --model google/gemini-3.1-pro-preview,thinking=high \
  --judge-model openai/gpt-5.4,thinking=xhigh,fast \
  --judge-model anthropic/claude-opus-4-6,thinking=high \
  --concurrency 16 \
  --judge-concurrency 16 \
  --output-dir .artifacts/qa-e2e/character-eval-<tag>
  • Runs local QA gateway child processes, not Docker.
  • Preferred model spec syntax is provider/model,thinking=<level>[,fast|,no-fast|,fast=<bool>] for both --model and --judge-model.
  • Do not add new examples with separate --model-thinking; keep that flag as legacy compatibility only.
  • Defaults to candidate models openai/gpt-5.4, openai/gpt-5.2, openai/gpt-5, anthropic/claude-opus-4-6, anthropic/claude-sonnet-4-6, zai/glm-5.1, moonshot/kimi-k2.5, and google/gemini-3.1-pro-preview when no --model is passed.
  • Candidate thinking defaults to high, with xhigh for OpenAI models that support it. Prefer inline --model provider/model,thinking=<level>; --thinking <level> and --model-thinking <provider/model=level> remain compatibility shims.
  • OpenAI candidate refs default to fast mode so priority processing is used where supported. Use inline ,fast, ,no-fast, or ,fast=false for one model; use --fast only to force fast mode for every candidate.
  • Judges default to openai/gpt-5.4,thinking=xhigh,fast and anthropic/claude-opus-4-6,thinking=high.
  • Report includes judge ranking, run stats, durations, and full transcripts; do not include raw judge replies. Duration is benchmark context, not a grading signal.
  • Candidate and judge concurrency default to 16. Use --concurrency <n> and --judge-concurrency <n> to override when local gateways or provider limits need a gentler lane.
  • Scenario source should stay markdown-driven under qa/scenarios/.
  • For isolated character/persona evals, write the persona into SOUL.md and blank IDENTITY.md in the scenario flow. Use SOUL.md + IDENTITY.md only when intentionally testing how the normal OpenClaw identity combines with the character.
  • Keep prompts natural and task-shaped. The candidate model should receive character setup through SOUL.md, then normal user turns such as chat, workspace help, and small file tasks; do not ask "how would you react?" or tell the model it is in an eval.
  • Prefer at least one real task, such as creating or editing a tiny workspace artifact, so the transcript captures character under normal tool use instead of pure roleplay.

Codex CLI model lane

Use model refs shaped like codex-cli/<codex-model> whenever QA should exercise Codex as a model backend.

Examples:

pnpm openclaw qa suite \
  --provider-mode live-frontier \
  --model codex-cli/<codex-model> \
  --alt-model codex-cli/<codex-model> \
  --scenario <scenario-id> \
  --output-dir .artifacts/qa-e2e/codex-<tag>
pnpm openclaw qa manual \
  --model codex-cli/<codex-model> \
  --message "Reply exactly: CODEX_OK"
  • Treat the concrete Codex model name as user/config input; do not hardcode it in source, docs examples, or scenarios.
  • Live QA preserves CODEX_HOME so Codex CLI auth/config works while keeping HOME and OPENCLAW_HOME sandboxed.
  • Mock QA should scrub CODEX_HOME.
  • If Codex returns fallback/auth text every turn, first check CODEX_HOME, ~/.profile, and gateway child logs before changing scenario assertions.
  • For model comparison, include codex-cli/<codex-model> as another candidate in qa character-eval; the report should label it as an opaque model name.

Repo facts

  • Seed scenarios live in qa/.
  • Main live runner: extensions/qa-lab/src/suite.ts
  • QA lab server: extensions/qa-lab/src/lab-server.ts
  • Child gateway harness: extensions/qa-lab/src/gateway-child.ts
  • Synthetic channel: extensions/qa-channel/

What “done” looks like

  • Full suite green for the requested lane.
  • User gets:

- watch URL if applicable - pass/fail counts - artifact paths - concise note on what was fixed

Common failure patterns

  • Live timeout too short:

- widen live waits in extensions/qa-lab/src/suite.ts

  • Discovery cannot find repo files:

- point prompts at repo/... inside seeded workspace

  • Subagent proof too brittle:

- prefer stable final reply evidence over transient child-session listing

  • Harness “rebuild” delay:

- dirty tree can trigger a pre-run build; expect that before ports appear

When adding scenarios

  • Add or update scenario markdown under qa/scenarios/
  • Keep kickoff expectations in qa/scenarios/index.md aligned
  • Add executable coverage in extensions/qa-lab/src/suite.ts
  • Prefer end-to-end assertions over mock-only checks
  • Save outputs under .artifacts/qa-e2e/

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能力 3

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能力 4

展示第三方安全扫描或审计结果

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

Codex

33.94%
按下载量换算155

Claude

30.7%
按下载量换算141

Cursor

21.55%
按下载量换算99

Gemini CLI

10.9%
按下载量换算50

安全审计

Gen Agent Trust Hub

可疑

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Snyk

通过

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