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DD ManoBrowser

Your hands in the user's real browser. Operate the user's own Chrome with their logged-in sessions — extract data behind login walls, reverse-engineer website APIs into reusable skills, and automate any browser workflow. Use when you need login-required data extraction, API reverse engineering, browser automation, or social media data collection. 给你一双手，像用户一样使用浏览器。在用户已登录的 Chrome 中工作——提取登录墙后的数据、逆向 API 生成可复用 Skill、自动化浏览器工作流。

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: Sophieyou v2.2.7 MIT-0 28 files body ≈ 2 642 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Your hands in the user's real browser.

As a process D 35/100 · Unfinished process — References files that are not bundled: browser-automation/SKILL.md, web-data-extractor/SKILL.md, platform-data-explorer/SKILL.md

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
D
73/100
safety, quality, tests
Safety 60%
82
Quality 40%
60
Run on models
none yet
Process rating
D
35/100
Unfinished process
References files that are not bundled: browser-automation/SKILL.md, web-data-extractor/SKILL.md, platform-data-explorer/SKILL.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Exfiltration
If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

  • high Exfiltration intent-browser-credential-store skill-card.md:3
    Accesses a browser credential / cookie store
    ManoBrowser operates a user's own Chrome browser with logged-in sessions to extract login-gated web data, reverse-engineer website APIs into reusable skills, and automate browser workflows.

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning missing-ref reference to a missing file: browser-automation/SKILL.md
  • warning missing-ref reference to a missing file: web-data-extractor/SKILL.md
  • warning missing-ref reference to a missing file: platform-data-explorer/SKILL.md
  • warning missing-ref reference to a missing file: api-skill-builder/SKILL.md
  • warning missing-ref reference to a missing file: chrome-workflow-build/SKILL.md

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: browser-automation/SKILL.md, web-data-extractor/SKILL.md, platform-data-explorer/SKILL.md
  • 0Tools and files. 5 referenced file(s) missing: browser-automation/SKILL.md, web-data-extractor/SKILL.md, platform-data-explorer/SKILL.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Steps. 92 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2642 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • -237 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 425: enough signal without eating the budget
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 92 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: suspicious
This skill is a powerful logged-in browser automation package, but it needs careful review because it can access private browser sessions, handle cookies unsafely, persist agent rules and logs, and route browser data through external services.
LLM: suspicious (high) · VirusTotal: · 16 Sept 2026