SKILLEMALL.ai
GitHub ↗ RU Sign in

BC beauty-generation-free

FREE AI portrait generation with 140+ nationalities, diverse styles, professional headshots, character design, and fashion visualization. Fast generation (3-5 seconds), built-in content safety, API key authentication, daily quota management. Perfect for creative projects, character design, professional portraits, and diverse representation.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 3 files body ≈ 6 715 tokens Open the sourcegithub.com↗ analyzed 2 d ago

FREE AI portrait generation with 140+ nationalities, diverse styles, professional headshots, character design, and fashion visualization.

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

GeneratorAI and agentsSales and CRMtype 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
83/100
safety, quality, tests
Safety 60%
99
Quality 40%
59
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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 Secrets in code secret-password-literal SKILL.md:361
    Hard-coded password / key literal (may be an example)
    api_key = sys.argv[1]

Files scanned: 3. 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 ≈ 6715 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (beauty-generation-free) differs from the folder (beauty-generation-api)
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6715 tokens
  • 85Steps. 208 steps, 1 vague phrases
  • 100Failures and branches. 4 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • -275 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 342: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 208 items
  • +4Has examples (23 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.