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BD xfetch

Fast X/Twitter CLI scraper. Use when you need to fetch tweets, user profiles, search results, timelines, followers, or any X/Twitter data. No API keys required - uses cookie-based auth.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 776 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Fast X/Twitter CLI scraper.

As a process D 40/100 · Unfinished process — weak spots: steps, when it triggers, inputs and preconditions

IntegrationData and analyticsAI and agentsMedia and videotype 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
B
83/100
safety, quality, tests
Safety 60%
82
Quality 40%
84
Run on models
none yet
Process rating
D
40/100
Unfinished process
Steps w 15
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
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.md:31
    Accesses a browser credential / cookie store
    **Get cookies from browser:** Open X.com in Chrome DevTools → Application → Cookies → Copy `auth_token` and `ct0` values.

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

✓ No remarks against the Agent Skills spec

Process rating: all ten parameters 40/100

  • 0Steps. Prose only: no discrete steps
  • 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
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 776 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)
  • +3No numbered steps or checklist
  • +1No license
  • +2Single-language instructions
  • +3Description length 185: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Output format is stated explicitly
  • +4Has examples (15 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.