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Dogfood

作者 thedalbee · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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当前安装
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在 OpenClaw 中安装
/install dogfood
功能描述
Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find i...
使用说明 (SKILL.md)

Dogfood

Systematically explore a web application, find issues, and produce a report with full reproduction evidence for every finding.

Setup

Only the Target URL is required. Everything else has sensible defaults -- use them unless the user explicitly provides an override.

Parameter Default Example override
Target URL (required) vercel.com, http://localhost:3000
Session name Slugified domain (e.g., vercel.com -> vercel-com) --session my-session
Output directory ./dogfood-output/ Output directory: /tmp/qa
Scope Full app Focus on the billing page
Authentication None Sign in to [email protected]

If the user says something like "dogfood vercel.com", start immediately with defaults. Do not ask clarifying questions unless authentication is mentioned but credentials are missing.

Always use agent-browser directly -- never npx agent-browser. The direct binary uses the fast Rust client. npx routes through Node.js and is significantly slower.

Workflow

1. Initialize    Set up session, output dirs, report file
2. Authenticate  Sign in if needed, save state
3. Orient        Navigate to starting point, take initial snapshot
4. Explore       Systematically visit pages and test features
5. Document      Screenshot + record each issue as found
6. Wrap up       Update summary counts, close session

1. Initialize

mkdir -p {OUTPUT_DIR}/screenshots {OUTPUT_DIR}/videos

Copy the report template into the output directory and fill in the header fields:

cp {SKILL_DIR}/templates/dogfood-report-template.md {OUTPUT_DIR}/report.md

Start a named session:

agent-browser --session {SESSION} open {TARGET_URL}
agent-browser --session {SESSION} wait --load networkidle

2. Authenticate

If the app requires login:

agent-browser --session {SESSION} snapshot -i
# Identify login form refs, fill credentials
agent-browser --session {SESSION} fill @e1 "{EMAIL}"
agent-browser --session {SESSION} fill @e2 "{PASSWORD}"
agent-browser --session {SESSION} click @e3
agent-browser --session {SESSION} wait --load networkidle

For OTP/email codes: ask the user, wait for their response, then enter the code.

After successful login, save state for potential reuse:

agent-browser --session {SESSION} state save {OUTPUT_DIR}/auth-state.json

3. Orient

Take an initial annotated screenshot and snapshot to understand the app structure:

agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/initial.png
agent-browser --session {SESSION} snapshot -i

Identify the main navigation elements and map out the sections to visit.

4. Explore

Read references/issue-taxonomy.md for the full list of what to look for and the exploration checklist.

Strategy -- work through the app systematically:

  • Start from the main navigation. Visit each top-level section.
  • Within each section, test interactive elements: click buttons, fill forms, open dropdowns/modals.
  • Check edge cases: empty states, error handling, boundary inputs.
  • Try realistic end-to-end workflows (create, edit, delete flows).
  • Check the browser console for errors periodically.

At each page:

agent-browser --session {SESSION} snapshot -i
agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/{page-name}.png
agent-browser --session {SESSION} errors
agent-browser --session {SESSION} console

Use your judgment on how deep to go. Spend more time on core features and less on peripheral pages. If you find a cluster of issues in one area, investigate deeper.

5. Document Issues (Repro-First)

Steps 4 and 5 happen together -- explore and document in a single pass. When you find an issue, stop exploring and document it immediately before moving on. Do not explore the whole app first and document later.

Every issue must be reproducible. When you find something wrong, do not just note it -- prove it with evidence. The goal is that someone reading the report can see exactly what happened and replay it.

Choose the right level of evidence for the issue:

Interactive / behavioral issues (functional, ux, console errors on action)

These require user interaction to reproduce -- use full repro with video and step-by-step screenshots:

  1. Start a repro video before reproducing:
agent-browser --session {SESSION} record start {OUTPUT_DIR}/videos/issue-{NNN}-repro.webm
  1. Walk through the steps at human pace. Pause 1-2 seconds between actions so the video is watchable. Take a screenshot at each step:
agent-browser --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-step-1.png
sleep 1
# Perform action (click, fill, etc.)
sleep 1
agent-browser --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-step-2.png
sleep 1
# ...continue until the issue manifests
  1. Capture the broken state. Pause so the viewer can see it, then take an annotated screenshot:
sleep 2
agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/issue-{NNN}-result.png
  1. Stop the video:
agent-browser --session {SESSION} record stop
  1. Write numbered repro steps in the report, each referencing its screenshot.

Static / visible-on-load issues (typos, placeholder text, clipped text, misalignment, console errors on load)

These are visible without interaction -- a single annotated screenshot is sufficient. No video, no multi-step repro:

agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/issue-{NNN}.png

Write a brief description and reference the screenshot in the report. Set Repro Video to N/A.


For all issues:

  1. Append to the report immediately. Do not batch issues for later. Write each one as you find it so nothing is lost if the session is interrupted.

  2. Increment the issue counter (ISSUE-001, ISSUE-002, ...).

6. Wrap Up

Aim to find 5-10 well-documented issues, then wrap up. Depth of evidence matters more than total count -- 5 issues with full repro beats 20 with vague descriptions.

After exploring:

  1. Re-read the report and update the summary severity counts so they match the actual issues. Every ### ISSUE- block must be reflected in the totals.
  2. Close the session:
agent-browser --session {SESSION} close
  1. Tell the user the report is ready and summarize findings: total issues, breakdown by severity, and the most critical items.

Guidance

  • Repro is everything. Every issue needs proof -- but match the evidence to the issue. Interactive bugs need video and step-by-step screenshots. Static bugs (typos, placeholder text, visual glitches visible on load) only need a single annotated screenshot.
  • Don't record video for static issues. A typo or clipped text doesn't benefit from a video. Save video for issues that involve user interaction, timing, or state changes.
  • For interactive issues, screenshot each step. Capture the before, the action, and the after -- so someone can see the full sequence.
  • Write repro steps that map to screenshots. Each numbered step in the report should reference its corresponding screenshot. A reader should be able to follow the steps visually without touching a browser.
  • Be thorough but use judgment. You are not following a test script -- you are exploring like a real user would. If something feels off, investigate.
  • Write findings incrementally. Append each issue to the report as you discover it. If the session is interrupted, findings are preserved. Never batch all issues for the end.
  • Never delete output files. Do not rm screenshots, videos, or the report mid-session. Do not close the session and restart. Work forward, not backward.
  • Never read the target app's source code. You are testing as a user, not auditing code. Do not read HTML, JS, or config files of the app under test. All findings must come from what you observe in the browser.
  • Check the console. Many issues are invisible in the UI but show up as JS errors or failed requests.
  • Test like a user, not a robot. Try common workflows end-to-end. Click things a real user would click. Enter realistic data.
  • Type like a human. When filling form fields during video recording, use type instead of fill -- it types character-by-character. Use fill only outside of video recording when speed matters.
  • Pace repro videos for humans. Add sleep 1 between actions and sleep 2 before the final result screenshot. Videos should be watchable at 1x speed -- a human reviewing the report needs to see what happened, not a blur of instant state changes.
  • Be efficient with commands. Batch multiple agent-browser commands in a single shell call when they are independent (e.g., agent-browser ... screenshot ... && agent-browser ... console). Use agent-browser --session {SESSION} scroll down 300 for scrolling -- do not use key or evaluate to scroll.

References

Reference When to Read
references/issue-taxonomy.md Start of session -- calibrate what to look for, severity levels, exploration checklist

Templates

Template Purpose
templates/dogfood-report-template.md Copy into output directory as the report file
安全使用建议
This skill appears to do what it says: automated exploratory testing and reporting. Before running it, consider: (1) it will capture screenshots, console logs, videos, and will save authentication state (auth-state.json) — treat those outputs as sensitive; (2) avoid using real-production user accounts or secrets during runs; prefer a throwaway or test account and clear saved auth-state files after use; (3) confirm where OUTPUT_DIR points and ensure the directory is secure (or use a temp directory); (4) the skill assumes an 'agent-browser' CLI is available — verify that binary and its provenance; (5) if you need tighter control, require the agent to ask for explicit permission before starting a session or before saving auth state. Overall the skill is internally consistent with its purpose, but exercise standard caution around captured credentials and test artifacts.
功能分析
Type: OpenClaw Skill Name: dogfood Version: 1.0.0 The 'dogfood' skill bundle is a legitimate tool designed for automated exploratory testing and QA of web applications. It provides structured instructions in SKILL.md for an AI agent to navigate a target URL, document bugs with screenshots and videos using the 'agent-browser' tool, and generate a comprehensive report based on a provided template. The workflow is transparent, requires user-provided credentials for authentication, and lacks any indicators of malicious intent, data exfiltration, or unauthorized system access.
能力评估
Purpose & Capability
The name/description (exploratory QA / 'dogfooding') match the runtime instructions and included templates. The skill uses a browser-automation CLI (agent-browser) and local report templates and checklists — all expected for this purpose. No unrelated binaries, cloud credentials, or config paths are requested.
Instruction Scope
Instructions direct the agent to fully exercise the app, take annotated screenshots, record repro videos, capture console/errors, and save session state (auth-state.json). These actions are appropriate for QA but will collect potentially sensitive data (authentication cookies, PII seen during testing). The SKILL.md also says not to ask clarifying questions except for missing credentials, which may cause the agent to proceed automatically; and it tells callers to prefer the direct 'agent-browser' binary while allowed-tools also lists 'npx agent-browser' — a minor inconsistency.
Install Mechanism
No install spec and no code files — instruction-only. This is the lowest-risk install mechanism (nothing is downloaded or executed beyond using existing CLI tools).
Credentials
The skill declares no required environment variables or credentials, which is reasonable. At runtime it expects the user to provide login credentials or OTPs when testing authenticated areas, and it explicitly saves auth state to disk. That behavior is proportional to QA but means sensitive credentials/tokens will be written to the OUTPUT_DIR unless the user avoids providing real credentials.
Persistence & Privilege
always is false and the skill does not request elevated or persistent platform privileges. It writes artifacts to an output directory and reads its own template files (SKILL_DIR); it does not modify other skills or global agent config.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install dogfood
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /dogfood 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release: systematic web app QA with agent-browser, repro videos, step-by-step screenshots, structured report
元数据
Slug dogfood
版本 1.0.0
许可证 MIT-0
累计安装 17
当前安装数 14
历史版本数 1
常见问题

Dogfood 是什么?

Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find i... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 252 次。

如何安装 Dogfood?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install dogfood」即可一键安装,无需额外配置。

Dogfood 是免费的吗?

是的,Dogfood 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Dogfood 支持哪些平台?

Dogfood 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Dogfood?

由 thedalbee(@thedalbee)开发并维护,当前版本 v1.0.0。

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