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AC openclaw-trends

Fetch and aggregate OpenClaw-related content from across the internet. Use when the user asks about OpenClaw trends, news, tutorials, videos, community discussions, or what people are saying about OpenClaw. Triggers on phrases like "what's new with OpenClaw", "find OpenClaw tutorials", "OpenClaw news", "OpenClaw YouTube videos", or checking for OpenClaw mentions online.

ClawHub Agent Skills author: Andrey Mashukov v1.0.0 MIT-0 3 files body ≈ 350 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

ProcedureYouTubeMedia and videoLearningtype 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
A
94/100
safety, quality, tests
Safety 60%
94
Quality 40%
95
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Secrets in code medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

How to improve

    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

    ✓ No critical or high findings

    Medium and low: 2
    • medium Secrets in code secret-google-key scripts/fetch_trends.py:26
      Google API key (quoted — discussed, not commanded)
      YOUTUBE_API_KEY = os.environ.get("YOUTUBE_API_KEY", "AIza…G88")
      quoted
    • low Secrets in code secret-high-entropy-token scripts/fetch_trends.py:26
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      YOUTUBE_API_KEY = os.environ.get("YOUTUBE_API_KEY", "AIza…G88")
      quoted

    Files scanned: 3. 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 58/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 350 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 372: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: clean
    This skill is a disclosed public trend-search tool, but users should understand it makes external searches and includes a hardcoded YouTube API key.
    LLM: benign (high) · VirusTotal: · 29 May 2026