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AC daily-briefing-hub

All-in-one daily briefing that combines Google Calendar events, Gmail/Outlook email highlights, weather forecast, GitHub PR and CI status, Hacker News and RSS feeds, and Todoist/ClickUp/Linear tasks into a single prioritized morning summary delivered via Telegram, Slack, WhatsApp, or Discord. Use this skill for: morning briefing, daily digest, daily summary, daily standup prep, "what's on my plate today", "brief me", "what did I miss", end-of-day recap, personalized news digest, schedule overview, inbox summary, daily notification, recurring morning update via cron, or any request to see a combined overview of calendar plus email plus tasks plus news. This is your AI chief of staff that replaces checking 6 apps every morning. Works with whatever tools you have configured — skips what's missing, uses what's available.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 680 tokens Open the sourcegithub.com↗ analyzed 2 d ago

All-in-one daily briefing that combines Google Calendar events, Gmail/Outlook email highlights, weather forecast, GitHub PR and CI status, Hacker News and RSS…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubTelegramSlackDiscordPersonal productivityWriting and documentstype 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%
100
Quality 40%
86
Run on models
none yet
Process rating
C
53/100
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: 1. 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 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 44 steps
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1680 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)
    • +3Description length 828: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 44 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.