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AC prompt-generator

Generate high-quality, customizable AI prompts for various use cases including creative writing, problem-solving, education, business, programming, Flutter development, and game development. Use when a user asks for help creating prompts, wants to improve their AI interactions, or needs structured prompt templates for specific tasks.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 27 files body ≈ 4 179 tokens Open the sourcegithub.com↗ analyzed 26 h ago

Generate high-quality, customizable AI prompts for various use cases including creative writing, problem-solving, education, business, programming, Flutter…

As a process C 63/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

GeneratorFirebaseAI 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
93/100
safety, quality, tests
Safety 60%
97
Quality 40%
86
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token .github/FUNDING.yml:34
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - **Bitcoin**: `1Ope…789`
      quoted
    • low Secrets in code secret-high-entropy-token .github/FUNDING.yml:35
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - **Ethereum**: `0xOp…789`
      quoted
    • low Secrets in code secret-high-entropy-token .github/FUNDING.yml:36
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - **USDT**: `TOpe…789`
      quoted

    Files scanned: 26. 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 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (prompt-generator) differs from the folder (smart-prompt-generator)
    • 50Failures and branches. 0 branches, has a failure section
    • 70Execution cost. Instruction body is 4179 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 89 steps
    • 100Result and completion. Output format and completion criterion are stated
    • low 14 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)
    • -212 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +3Description length 335: enough signal without eating the budget
    • +4Structure: 65 headings
    • +3Step-by-step instructions: 89 items
    • +3Output format is stated explicitly
    • +4Has examples (31 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.