SKILLEMALL.ai

AC gmail-labeler

Gmail inbox triage, labeling, and safe archiving with gog plus a configurable lightweight LLM review layer. Use when building or running Gmail automation that separates actionable vs non-actionable mail, applies Gmail labels, archives low-value messages, keeps important human replies in Inbox, routes urgent items for notification, supports multilingual inboxes (English, Portuguese, Spanish), and needs a clean publishable skill with private local overlays kept outside the skill folder.

ClawHub Agent Skills author: Felipe Matos v0.1.3 MIT-0 12 files · 1 script body ≈ 1 237 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

AnalyzerGmailAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
50/100
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: 12. 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
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 12 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 79 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1237 tokens
    • 100Progress reporting. Reports progress
    • low 10 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
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +1No license
    • +2Single-language instructions
    • +3Description length 489: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 79 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This Gmail automation is purpose-aligned, but it needs Review because it can alter mailbox state, send email excerpts to an LLM tool by default, fetch a hard-coded production secret in its launcher, and persist sensitive email metadata in local logs.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026