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AC google-news-api-skill

This skill automates the extraction of structured news data from Google News via BrowserAct API. Use this skill when the user asks for tasks such as: 1. Searching for news about a specific topic; 2. Tracking industry trends; 3. Monitoring public relations or sentiment; 4. Collecting competitor updates; 5. Getting the latest reports on specific keywords; 6. Monitoring brand exposure in media; 7. Researching market hot topics; 8. Summarizing daily industry news; 9. Tracking media activities of specific individuals; 10. Retrieving hot events from the past 24 hours; 11. Extracting structured data for market research; 12. Monitoring global breaking news.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 325 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill automates the extraction of structured news data from Google News via BrowserAct API.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationInfrastructuretype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
59/100
Has gaps
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

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: 2. 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 59/100

    • 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
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 42 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1325 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 657: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 42 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.