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AC Briefing Room

Daily news briefing generator — produces a conversational radio-host-style audio briefing + DOCX document covering weather, X/Twitter trends, web trends, world news, politics, tech, local news, sports, markets, and crypto. macOS only (uses Apple TTS and afplay). Use when user asks for a news briefing, morning briefing, daily update, or similar.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files · 1 script body ≈ 4 664 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Daily news briefing generator — produces a conversational radio-host-style audio briefing + DOCX document covering weather, X/Twitter trends, web trends…

As a process C 58/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

GeneratorWordData and analyticsInfrastructuretype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Consistency w 8
40
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

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 58/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (Briefing Room) differs from the folder (briefing-room)
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4664 tokens
    • 85Steps. 83 steps, 3 vague phrases
    • 100Failures and branches. 3 branches, has a failure section
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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)
    • -215 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 346: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 83 items
    • +3Output format is stated explicitly
    • +4Has examples (28 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.