SKILLEMALL.ai
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BD x-twitter-scraper

X API & Twitter scraper skill for AI coding agents. Builds integrations with the Xquik REST API, MCP server & webhooks: tweet search, user lookup, follower extraction, engagement metrics, giveaway contest draws, trending topics, account monitoring, reply/retweet/quote extraction, community & Space data, mutual follow checks, write actions (tweet, like, retweet, follow, DM, profile, media upload, communities), Telegram integrations. Works with Claude Code, Cursor, Codex, Copilot, Windsurf & 40+ agents.

Not recommendedcritical or high security findings
LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 10 files body ≈ 6 724 tokens Open the sourcegithub.com↗ analyzed 2 d ago

X API & Twitter scraper skill for AI coding agents.

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationTelegramAI 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
B
77/100
safety, quality, tests
Safety 60%
82
Quality 40%
70
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 exfil-webhook-url references/webhooks.md:195
    Webhook / callback URL commonly used for exfiltration (verify the destination)
    Or use [RequestBin](https://requestbin.com) for quick inspection without running a server.

Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6724 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 446, 512): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (x-twitter-scraper) differs from the folder (xquik-x-twitter-scraper)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6724 tokens
  • 85Steps. 42 steps, 1 vague phrases
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 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)
  • +2Single-language instructions
  • +3Description length 506: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 42 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)
  • +1License stated

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