SKILLEMALL.ai
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Ranks your daily AI news feed by personal relevance — scores every article and surfaces the most useful ones first. To fetch X/Twitter posts, the daily run reads your local Chrome x.com cookies (via browser-cookie3, in-memory only) and calls the twitter-cli subprocess; it may auto-detect a local proxy on 127.0.0.1 to reach x.com, and writes reports/history/dedup state to your local data_dir. Sources include HuggingFace Papers, OpenAI, Anthropic, DeepMind, Simon Willison, GitHub Trending, HN, Reddit, and more, plus X/Twitter accounts.

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: Pudge v1.0.5 MIT-0 16 files · 5 scripts body ≈ 2 683 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Ranks your daily AI news feed by personal relevance — scores every article and surfaces the most useful ones first.

As a process C 55/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

GeneratorGitHubAI 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
67/100
safety, quality, tests
Safety 60%
64
Quality 40%
71
Run on models
none yet
Process rating
C
55/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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.
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 · 2

  • high Exfiltration intent-browser-credential-store sections/daily-mode.md:32
    Accesses a browser credential / cookie store
    | `type: twitter` | fetch.py (Step 1) | Reads x.com cookies from local Chrome, auto-detects proxy |
  • high Exfiltration intent-browser-credential-store sections/daily-mode.md:39
    Accesses a browser credential / cookie store
    **X/Twitter source (`type: twitter`):** Already handled by fetch.py (Step 1). The script extracts x.com cookies from local Chrome via `brow…ie3` and calls `twitter-cli` for each account in `sou

Files scanned: 16. 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")
  • note frontmatter-key unknown frontmatter key "emoji"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 55/100

  • 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
  • 30Running it twice. 4 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, read, web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 36 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2683 tokens

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)
  • -31 of 3 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 539: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 36 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
The skill is a coherent AI news digest tool, but its default daily run reads local Chrome X/Twitter session cookies and passes them to a subprocess without a clear per-run opt-in.
LLM: suspicious (high) · 25 Jun 2026