SKILLEMALL.ai
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Spec-driven development assistant: helps write precise behavioral specs before coding, or reverse-engineer specs from existing code for understanding and alignment. Use this skill when the user mentions "write a spec", "generate spec", "spec-driven", "behavioral contract", "reverse spec from code", "clarify this module's behavior", or "align on intent". Also applies when the user says "I want to add a feature, help me think it through first" or "what does this code actually do, walk me through it".

ClawHub Agent Skills author: yuanyan v0.0.1 MIT-0 22 files body ≈ 1 604 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

GeneratorSoftware developmenttype 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%
100
Quality 40%
84
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 64/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 65Failures and branches. 3 branches
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 50 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1604 tokens
    • 100Running it twice. No mutating operations
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    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
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +3Description length 503: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 50 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a markdown-only spec-writing skill with no install-time execution or hidden runtime behavior, though generated specs and code still need normal security review.
    LLM: benign (high) · VirusTotal: · 29 May 2026