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AD notion-mcp

Notion MCP integration with managed authentication. Query databases, create and update pages, manage blocks. Use this skill when users want to interact with Notion workspaces via MCP. For REST API, use the notion skill (https://clawhub.ai/byungkyu/notion-api-skill). For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway).

modbender/skill-library-mcp Agent Skills author: modbender MIT 13 files body ≈ 4 705 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Notion MCP integration with managed authentication.

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationNotionAI and agentsSoftware developmenttype 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
D
47/100
Unfinished process
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: 13. 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 47/100

    • 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. 18 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4705 tokens
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • low 11 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 363: enough signal without eating the budget
    • +4Structure: 34 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (50 code blocks)

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