SKILLEMALL.ai

BF agent-email-inbox

Use when setting up an email inbox for an AI agent (Moltbot, Clawdbot, or similar) - configuring inbound email, webhooks, tunneling for local development, and implementing security measures to prevent prompt injection attacks.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 10 377 tokens Open the sourcegithub.com analyzed 4 d ago

Use when setting up an email inbox for an AI agent (Moltbot, Clawdbot, or similar) - configuring inbound email, webhooks, tunneling for local development, and…

As a process F 46/100 · Will not run — References files that are not bundled: ../send-email/references/installation.md

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
96
Quality 40%
65
Run on models
none yet
Process rating
F
46/100
Will not run
References files that are not bundled: ../send-email/references/installation.md
Tools and files w 18
0
Result and completion w 14
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. The text references files that are not there: add them or drop the references.
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Exfiltration read-dotenv SKILL.md:664
    Reads a .env file (documentation of a security skill)
    # with open('.env', 'a') as f:
    security skill

A further 3 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 10377 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: ../send-email/references/installation.md
  • note frontmatter-key unknown frontmatter key "inputs"

Process rating: all ten parameters 46/100

Will not run. References files that are not bundled: ../send-email/references/installation.md
  • 0Tools and files. 1 referenced file(s) missing: ../send-email/references/installation.md
  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 58 mutating operations with no state check
  • 40Execution cost. Instruction body is 10377 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Steps. 77 steps, 4 vague phrases
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 18 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (6 tags): a typed call is more reliable

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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 226: enough signal without eating the budget
  • +4Structure: 67 headings
  • +3Step-by-step instructions: 77 items
  • +4Has examples (28 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.