SKILLEMALL.ai
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AD vibetrading

Build, backtest, and deploy cryptocurrency trading strategies using the vibetrading Python framework. Use when: (1) generating trading strategies from natural language, (2) backtesting strategies on historical data, (3) deploying strategies to live exchanges (Hyperliquid, Paradex, Lighter, Aster), (4) comparing strategy performance, (5) working with crypto trading indicators, position sizing, or risk management. NOT for: general finance questions, non-crypto trading, or strategies outside the vibetrading framework.

ClawHub Agent Skills author: crabbytt v1.0.1 3 files body ≈ 1 437 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorInfrastructuretype 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
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
92
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-password-literal SKILL.md:74
      Hard-coded password / key literal (may be an example)
      api_key="0xWa…ess",

    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 47/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 7 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1437 tokens
    • low 12 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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 520: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    This documentation-only crypto trading skill is purpose-aligned, but its live-trading examples can use private keys to place real trades without enough safety guidance.
    LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026