SKILLEMALL.ai

BD 🛡️ AgentShield Audit

✅ Zero data leaves your system ✅ 52+ tests run locally in your agent ✅ Only certificate public key is shared ✅ Open source - verify every test

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 2 641 tokens Open the sourcegithub.com analyzed 2 d ago

✅ Zero data leaves your system ✅ 52+ tests run locally in your agent ✅ Only certificate public key is shared ✅ Open source - verify every test

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
97
Quality 40%
69
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Risky intent intent-offensive-security CHANGELOG.md:266
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    **Bug Bounty:** See [SECURITY.md](./SECURITY.md#bug-bounty)
  • low Risky intent intent-offensive-security SECURITY.md:170
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    ### Bug Bounty
  • low Risky intent intent-offensive-security SKILL.md:160
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - Privilege escalation attempts

Files scanned: 8. 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 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 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. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (🛡️ AgentShield Audit) differs from the folder (agentshield-audit)
  • 100Tools and files. No external tools needed
  • 100Steps. 77 steps
  • 100Execution cost. Instruction body is 2641 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 16 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
  • -272 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 142: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 77 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (1 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.