SKILLEMALL.ai
GitHub ↗ RU Sign in

AB soul-audit

Evaluate any AI agent's soul file, system prompt, or AGENTS.md against the Guardian Angel v0.7 framework (Forrest Landry's Immanent Metaphysics). Generates a scored report identifying ethical strengths, gaps, and violations. Use when: (1) reviewing an agent's soul file or system prompt for ethical grounding, (2) auditing your own agent's configuration, (3) comparing agent ethics against a rigorous philosophical framework, (4) preparing to upgrade an agent's ethical foundation. Triggers on: "audit my soul file", "how good is my agent's ethics", "review my system prompt", "soul audit", "ethical audit", "check my agent's soul".

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 700 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Evaluate any AI agent's soul file, system prompt, or AGENTS.md against the Guardian Angel v0.7 framework (Forrest Landry's Immanent Metaphysics). Generates a…

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerAI and agentstype 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
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
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: 2. 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 65/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 700 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 632: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 12 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.