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AC clawsouls

Manage AI agent personas (Souls) for OpenClaw. Use when the user wants to install, switch, list, or restore AI personalities/personas. Triggers on requests like "install a soul", "switch persona", "change personality", "list souls", "restore my old soul", "use minimalist", "browse personas", "what souls are available", "publish a soul", or "login to clawsouls".

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files · 1 script body ≈ 2 112 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Manage AI agent personas (Souls) for OpenClaw.

As a process C 62/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

ProcedureAI 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
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security SKILL.md:146
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - **Security**: 53 pattern checks (prompt injection, code execution, XSS, data exfiltration, privilege escalation, social engineering, harmful content, secret detection)
      detector

    Files scanned: 6. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 13 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 39 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2112 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)
    • +3Output format is not stated: the model decides each time
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +5Description quotes 10 example trigger phrases
    • +3Description length 363: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 39 items
    • +4Has examples (18 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.