SKILLEMALL.ai

AC news-for-ai

Fetches real-time AI industry news, daily AI digests, and searches AI topics including products, models, and MCP services. Outputs structured JSON with clean text content and separated media resources. Use when the user mentions AI新闻, AI资讯, AI日报, AI热点, AI热榜, AI前沿, AI动态, AI行业趋势, AI产品, AI模型, 最新AI, 今日AI, AI圈, 人工智能资讯, ai news, ai daily, ai trending, AI选题, AI文章素材, or wants to look up a specific AI topic, product, or model by keyword. Also triggers on general questions like "今天AI圈有什么新闻", "有什么新的AI进展", "帮我查一下XX模型".

ClawHub Agent Skills author: sskun v1.0.1 MIT-0 5 files body ≈ 634 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, progress reporting

ProcedureInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
64/100
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

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 · 0

    ✓ No critical or high findings

    Files scanned: 5. 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 64/100

    • 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
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 75Steps. 3 steps
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 634 tokens
    • 100Running it twice. No mutating operations

    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 3 example trigger phrases
    • +3Description length 512: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 3 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This is a straightforward AI-news fetching skill with ordinary network scraping behavior and some dependency hygiene concerns, but no hidden local data access, persistence, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026