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Gmail inbox intelligence for OpenClaw. Reads, classifies, and digests your Gmail so the important stuff surfaces without the noise. Use when fetching new emails, running an inbox digest, classifying messages into categories (newsletter, finance, travel, work, action-required, etc.), or building downstream bots that consume email data. Provides BotContext (per-bot label isolation), EmailFetcher, EmailParser, EmailClassifier (15 categories), and EmailStore (deduplicated JSONL).

modbender/skill-library-mcp Agent Skills author: modbender MIT 23 files body ≈ 634 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Gmail inbox intelligence for OpenClaw.

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

IntegrationGmailWriting 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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: 16. 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 50/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 75Steps. 3 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 634 tokens
    • 100Running it twice. No mutating operations

    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
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 480: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.