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AB prompt-engineer

Generates high-quality, structured, engineering-grade prompts. Use when the user asks to create, generate, or write a prompt for technical tasks — software development, backend/frontend/fullstack, architecture, infrastructure, DevOps, Docker, databases, APIs, AI systems, agents, MCP, testing, observability, runtime, refactoring, audits, admin panels, automation, integrations. Also triggers on "write me a prompt", "create a prompt", "generate a prompt", "make a prompt for X". NOT for general Q&A or non-prompt tasks.

ClawHub Agent Skills author: ZoriyAI v1.0.0 MIT-0 3 files body ≈ 609 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 72/100 · Nearly there — weak spots: inputs and preconditions, consistency, progress reporting

GeneratorDockerAI 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
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
100
Run on models
none yet
Process rating
B
72/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Consistency w 8
40
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: 3. 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 72/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (prompt-engineer) differs from the folder (zoriy-prompt-engineer)
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 21 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 609 tokens
    • 100Running it twice. No mutating operations

    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

    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 520: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 21 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: 100.

    External checks

    ClawHub: clean
    This skill only generates technical prompts and has no evidence of installing code, reading files, using credentials, or running background actions.
    LLM: benign (high) · VirusTotal: · 29 May 2026