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BC cross-funding-arb

跨交易所资金费率套利策略。在费率低的交易所做多永续、费率高的交易所做空永续，Delta-neutral 赚取 funding spread。支持 Hyperliquid + Binance，自动扫描机会、稳定性验证、原子开仓、健康检查、自动切仓。适用于资金费率套利、Delta 中性、跨所套利场景。

ClawHub Agent Skills author: SynthThoughts v2.8.0 MIT-0 7 files body ≈ 2 621 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureInfrastructuretype 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
84/100
safety, quality, tests
Safety 60%
96
Quality 40%
67
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Exfiltration read-dotenv README.md:27
    Reads a .env file
    cp .env.example ~/.openclaw/skills/cross-funding-arb/references/.env
  • low Exfiltration exfil-webhook-url references/cross_funding.py:759
    Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    placeholder
  • low Exfiltration read-dotenv SKILL.md:349
    Reads a .env file
    cp .env.example ~/.openclaw/skills/cross-funding-arb/references/.env
  • low Exfiltration read-dotenv SKILL.md:353
    Reads a .env file
    cp .env.example ~/.zeroclaw/skills/cross-funding-arb/references/.env

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 56/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 44 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2621 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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)
  • +3Output format is not stated: the model decides each time
  • -215 emoji in the instructions: noise for the model
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • +2Single-language instructions
  • +3Description length 149: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (12 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
This is a coherent crypto arbitrage bot, but it needs Review because it can trade real futures accounts unattended and reuse local messaging credentials for financial notifications.
LLM: suspicious (high) · VirusTotal: · 29 May 2026