Agent skill

Openclaw QA Testing

by openclaw in openclaw/openclaw

Run, watch, debug, extend, or explain OpenClaw qa-lab and qa-channel scenarios, artifacts, and live lanes.

MITAuto-check passedTesting & QA

Install Openclaw QA Testing

skills CLI
$ npx skills add openclaw/openclaw --skill openclaw-qa-testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openclaw/openclaw openclaw-qa-testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openclaw/openclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/openclaw-qa-testing .claude/skills/openclaw-qa-testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
openclaw-qa-testing
GitHub stars
392k
Token cost
~2.9k tokens
SKILL.md length
1,258 words
Files
2
Skills in repo
93
Repo updated
First seen
Licence
MIT

At a glance

Run, watch, debug, extend, or explain OpenClaw qa-lab and qa-channel scenarios, artifacts, and live lanes.

  • Works in 6 steps: Read the scenario pack and current suite… → Decide lane → For a normal live suite, use → …
  • Tasks that involve QA and bug reports
  • SKILL.md covers Read first, Model policy, Default workflow and OTEL smoke, plus 8 more sections
  • Calls pnpm and gh; needs OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN and OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN

What it does

Openclaw QA Testing is an agent skill from openclaw/openclaw. Run, watch, debug, extend, or explain OpenClaw qa-lab and qa-channel scenarios, artifacts, and live lanes.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Testing & QA, covering QA and bug reports. It works with OpenTelemetry and Telegram. The repository describes itself as: The AI that really does things. Any OS. Any Platform. The lobster way. 🦞. The licence is MIT.

When your agent uses it

  • Tasks that involve QA and bug reports

Example prompts

  • “/openclaw-qa-testing”

Requirements

  • Docker
  • A credential in OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN
  • A credential in OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Read the scenario pack and current suite implementation.
  2. Decide lane
  3. For a normal live suite, use
  4. Watch outputs
  5. If the user wants to watch the live UI, find the current openclaw-qa listen port and report http://127.0.0.1:.
  6. If a scenario fails, fix the product or harness root cause, then rerun the full lane.

What it can do on your machine

Read from SKILL.md and the folder at commit 1eb5970. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pnpm
    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pnpm and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN
    • OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Openclaw QA Testing loads about 2.9k tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 1,258 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~32
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from openclaw/openclaw at commit 1eb5970, republished under its MIT licence (© openclaw). 1,258 words, ~2,903 tokens.

Download SKILL.mdSave it as .claude/skills/openclaw-qa-testing/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
openclaw-qa-testing
description
Run, watch, debug, extend, or explain OpenClaw qa-lab and qa-channel scenarios, artifacts, and live lanes.

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.yaml
  • extensions/qa-lab/src/suite.ts
  • extensions/qa-lab/src/character-eval.ts

Model policy

  • Normal live suite runs rely on QA Lab source- and auth-aware defaults.
  • Do not pass --model, --alt-model, or --fast by default. Omitted --fast does not mean fast is disabled; fast behavior is source-owned.
  • For scenario-specific runs, the complete execution.summary is authoritative and overrides generic default guidance, including when it requires other flags. Add explicit provider/model pins only when execution.config.requiredProvider or requiredModel requires them.

Default workflow

  1. Read the scenario pack and current suite implementation.
  2. Decide lane:
    • mock/dev: mock-openai
    • real validation: live-frontier
  3. For a normal live suite, use:
bash
pnpm openclaw qa suite \
  --provider-mode live-frontier \
  --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
  2. If the user wants to watch the live UI, find the current openclaw-qa listen port and report http://127.0.0.1:<port>.
  3. 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:

bash
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.

QA credentials and 1Password

  • Use op only inside tmux for QA secret lookup in this repo.
  • Quick auth check inside tmux:
bash
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:
bash
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
  • If direct Telegram env is missing locally and op signin blocks, prefer dispatching the manual GitHub lane because the qa-live-shared environment already has Convex CI credentials:
bash
gh workflow run "NPM Telegram Beta E2E" --repo openclaw/openclaw --ref main \
  -f package_spec=openclaw@YYYY.M.D-beta.N \
  -f package_label=openclaw@YYYY.M.D-beta.N \
  -f provider_mode=mock-openai
  • Poll the exact run id from the dispatch URL. gh run view --json artifacts is not supported; list artifacts with:
bash
gh api repos/openclaw/openclaw/actions/runs/<run-id>/artifacts

WhatsApp live credentials

Use this when setting up or replacing Convex kind=whatsapp credentials.

  • Treat WhatsApp QA credentials as operator-owned live accounts, not generated fixtures.
  • Use two dedicated WhatsApp-capable test numbers: one driver account and one SUT account. Do not use personal numbers or personal OpenClaw WhatsApp accounts in the shared pool.
  • Register and link each account manually with WhatsApp or WhatsApp Business, storing Web auth only in isolated local auth dirs outside the repo.
  • For group coverage, create a dedicated test group that includes both QA accounts and store its JID as groupJid; otherwise the group mention-gating scenario should be skipped by default and fail when explicitly requested.
  • Package the two Baileys auth dirs into base64 .tgz payload fields and add a new active Convex credential row. Prefer adding a fresh row and disabling stale/broken rows over overwriting credentials in place.
  • Expected payload fields: driverPhoneE164, sutPhoneE164, driverAuthArchiveBase64, sutAuthArchiveBase64, and optional groupJid.
  • Keep credential material out of the repo, logs, PRs, and screenshots. Redact phone numbers unless the operator explicitly asks for local debugging.
  • Validate with pnpm openclaw qa whatsapp --credential-source convex --credential-role maintainer --provider-mode mock-openai and preserve artifact paths plus redacted pass/fail summaries.
  • If WhatsApp expires or invalidates a linked Web session, relink locally, package fresh auth archives, add a new Convex row, then disable the stale row.
Show full SKILL.md (519 more words)Show less

Character evals

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

bash
pnpm openclaw qa character-eval \
  --output-dir .artifacts/qa-e2e/character-eval-<tag>
  • Runs local QA gateway child processes, not Docker.
  • Packaged pnpm build omits QA Lab + qa-channel by design (source-checkout only). To exercise openclaw qa/qa-channel from a built dist, build with OPENCLAW_BUILD_PRIVATE_QA=1 pnpm build (emits dist/plugin-sdk/qa-lab.js, qa-runtime.js, dist/extensions/{qa-lab,qa-channel}) or run via pnpm dev.
  • With no model flags, character eval uses its current source-defined candidate, judge, thinking, and fast defaults.
  • Repeat --model provider/model,thinking=<level>[,fast|,no-fast|,fast=<bool>] or --judge-model ... only to replace the corresponding inventory explicitly.
  • Do not add new examples with separate --model-thinking; keep that flag as legacy compatibility only.
  • 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 is YAML-only under qa/scenarios/: use index.yaml and per-scenario *.yaml files with top-level title, scenario, and optional flow. Never add fenced qa-scenario / qa-flow Markdown files.
  • 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:

bash
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>
bash
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, relevant secret-backed auth, 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/scenarios/index.yaml and qa/scenarios/<theme>/*.yaml.
  • 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 YAML under qa/scenarios/; do not add .md scenario files or fenced YAML blocks.
  • Keep kickoff expectations in qa/scenarios/index.yaml 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/

© openclaw, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/openclaw-qa-testing of openclaw/openclaw.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 1eb5970

Compare with similar skills

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Dogfood Exploratory QAvercel-labs/agent-browser44k8 repos~2.7kAutomated safety check: PassApache-2.0
Codex Plugin QAcode-yeongyu/oh-my-openagent70k1 repos~1.9kAutomated safety check: PassCustom licence

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Categories

Questions about Openclaw QA Testing

What does Openclaw QA Testing do?

Run, watch, debug, extend, or explain OpenClaw qa-lab and qa-channel scenarios, artifacts, and live lanes. Openclaw QA Testing is an agent skill from openclaw/openclaw. Run, watch, debug, extend, or explain OpenClaw qa-lab and qa-channel scenarios, artifacts, and live lanes.

When should I use Openclaw QA Testing?

Openclaw QA Testing fits situations like: tasks that involve QA and bug reports.

How do I install Openclaw QA Testing in Claude Code?

Run `npx skills add openclaw/openclaw --skill openclaw-qa-testing -a claude-code`. Or copy the skill folder (.agents/skills/openclaw-qa-testing in openclaw/openclaw) into .claude/skills/openclaw-qa-testing in your project. Claude Code loads it when a task matches its description.

How do I install Openclaw QA Testing in Codex?

Run `npx skills add openclaw/openclaw --skill openclaw-qa-testing -a codex`. Or copy the skill folder (.agents/skills/openclaw-qa-testing in openclaw/openclaw) into .agents/skills/openclaw-qa-testing in your project. Codex loads it when a task matches its description.

Can I use Openclaw QA Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openclaw/openclaw --skill openclaw-qa-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/openclaw-qa-testing, .gemini/skills/openclaw-qa-testing, .github/skills/openclaw-qa-testing and .opencode/skills/openclaw-qa-testing in your project.

What does Openclaw QA Testing need to run?

Going by SKILL.md and its folder, Openclaw QA Testing needs the command-line tools its instructions call (pnpm and gh) and credentials named OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN and OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN. Our summary lists: Docker; A credential in OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN; A credential in OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN.

Does Openclaw QA Testing access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Openclaw QA Testing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Openclaw QA Testing use?

Openclaw QA Testing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Openclaw QA Testing use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Openclaw QA Testing?

Skills that share tags, products or a category with Openclaw QA Testing: Tg Bot Ops (serejaris/personal-corp-os, 229 stars), Deploying Scalable Agents (microsoft/ai-agents-for-beginners, 77k stars), Deploying Scalable Agents (microsoft/ai-agents-for-beginners, 77k stars) and Dogfood Exploratory QA (vercel-labs/agent-browser, 44k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Openclaw QA Testing?

openclaw (a GitHub organization) maintains it in openclaw/openclaw, which has 391,610 GitHub stars. The repository holds 93 skills in this directory. The repository was last updated on October 8, 2026.

Source: openclaw/openclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.