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BA WheelSpotter

A wheel-spotting scout that finds reusable solutions before you build from scratch. Cost-controlled intelligent search with complexity-aware filtering, intent-based platform selection, and form consistency checks.

ClawHub Agent Skills author: GARYLOooP v1.0.0 MIT-0 4 files · 1 script body ≈ 3 628 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

A wheel-spotting scout that finds reusable solutions before you build from scratch.

As a process A 80/100 · Runs to the end — no weak spots found

GeneratorGitHubExcelSoftware developmentAI 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
A
80/100
Runs to the end
Tools and files w 18
60
Result and completion w 14
60
When it triggers w 12
70
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "dependency"
    • note frontmatter-key unknown frontmatter key "triggers"
    • note edit-residue the text marks something as outdated (lines 137, 201, 202, 203): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 80/100

    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 27 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3628 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • 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)
    • -212 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +3Description length 213: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 27 items
    • +3Output format is stated explicitly
    • +4Has examples (18 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    WheelSpotter mostly does what it claims, but it needs review because it handles a GitHub token through command-line arguments and uses untrusted package metadata in agent evaluation prompts without clear guardrails.
    LLM: suspicious (high) · VirusTotal: · 16 Sept 2026