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AC smart-daily-assistant

When user asks to set reminders, save quick notes, get morning briefing, draft messages, use quick reply templates, translate text, plan day, schedule tasks, track habits, track birthdays, log expenses, save links, manage contacts, change message tone, plan weekend, or any daily personal assistant task. 20-feature AI personal assistant with reminders, notes, briefings, message drafting, quick replies, templates, translate, daily planner, habit tracker, and gamification. All data stays local — NO external API calls, NO network requests, NO data sent to any server.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 3 877 tokens Open the sourcegithub.com↗ analyzed 2 d ago

When user asks to set reminders, save quick notes, get morning briefing, draft messages, use quick reply templates, translate text, plan day, schedule tasks…

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationWriting and documentsPersonal productivitytype 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%
84
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 41 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3877 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 29 top-level sections: this looks like several domains in one skill

    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 569: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 41 items
    • +4Has examples (45 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.