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BC Gmail Label Manager Skill

This skill automates the organization of unread Gmail messages by applying labels, removing unnecessary labels, and archiving emails, based on predefined patterns from archived emails.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files · 1 script body ≈ 386 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill automates the organization of unread Gmail messages by applying labels, removing unnecessary labels, and archiving emails, based on predefined…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureGmailInfrastructuretype 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
B
82/100
safety, quality, tests
Safety 60%
94
Quality 40%
63
Run on models
none yet
Process rating
C
51/100
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Obfuscation medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Obfuscation uni-zero-width script.sh:905
    Zero-width / invisible characters (possible hidden text) (4 occurrences)
    local telegram_message="<b>␀ school Parent-Teacher Conference</b>
  • low Exfiltration exfil-webhook-url script.sh:294
    Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
    "https://api.telegram.org/bot${TELEGRAM_BOT_TOKEN}/sendMessage" \
    placeholder

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 51/100

  • 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
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (Gmail Label Manager Skill) differs from the folder (gmail-label-manager)
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 7 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Execution cost. Instruction body is 386 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

  • +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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 184: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 7 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.