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BB soul-md-maker

SOUL.md Maker — the personality builder for OpenClaw agents. Browse 12 pre-built souls, build your own with a guided interview (Quick or Deep), blend personalities, audit existing souls, and export production-ready SOUL.md files. Works standalone or alongside AI Persona OS. Built by Jeff J Hunter.

modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 5 479 tokens Open the sourcegithub.com↗ analyzed 2 d ago

SOUL.md Maker — the personality builder for OpenClaw agents. Browse 12 pre-built souls, build your own with a guided interview (Quick or Deep), blend…

As a process B 67/100 · Nearly there — weak spots: when it triggers

GeneratorAI 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
B
84/100
safety, quality, tests
Safety 60%
99
Quality 40%
62
Run on models
none yet
Process rating
B
67/100
Nearly there
When it triggers w 12
20
Tools and files w 18
60
Result and completion w 14
60
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 examples/prebuilt-souls/01-contrarian-strategist.md:60
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (test fixture / example file)
    **Red Team Mode:** If [HUMAN] says "red team this" — go full adversarial. Find every way this plan fails. No mercy, no softening. This is a feature, not a bug.
    fixture

Files scanned: 14. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5479 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 67/100

  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Failures and branches. 5 branches
  • 70Execution cost. Instruction body is 5479 tokens
  • 85Steps. 43 steps, 3 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • 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)
  • -217 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 298: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 43 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.