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BC Agent Self-Assessment

Security self-assessment tool for AI agents. Run this against your own configuration to get a structured threat model report with RED/AMBER/GREEN ratings across six security domains — decision boundaries, audit trail, credential scoping, plane separation, economic accountability, and memory safety.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 911 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Security self-assessment tool for AI agents.

As a process C 55/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

AnalyzerDiscordAI and agentsSecuritytype 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
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
55/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
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 · 0

✓ No critical or high findings

Files scanned: 1. 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")
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 55/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 10 mutating operations with no state check
  • 40Consistency. Frontmatter name (Agent Self-Assessment) differs from the folder (agent-self-assessment)
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 56 steps
  • 100Execution cost. Instruction body is 2911 tokens
  • 100Progress reporting. Reports progress

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)
  • -218 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 299: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 56 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.