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BD Trading_Agents_for_Futures

期货六维分析数据引擎。两种运行模式： (1) 数据模式：python main.py -s RB → 结构化 JSON 指标 + data_gap_report（数据缺口报告 + AI 搜索指令） (2) 决策模式：python main.py -s RB --decision → 指标 + 数据来源追溯 + 多空辩论 + 风控 + CIO决策报告 零 API Key，纯规则引擎。API 缺失时自动生成 search_actions，AI Agent 可按 fillability 回填 ai_fill 槽位。

ClawHub Agent Skills author: 7haoge v2.0.19 MIT-0 20 files body ≈ 4 139 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 40/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSoftware developmentAI and agentsInfrastructuretype 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
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
D
40/100
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

  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 · 0

✓ No critical or high findings

Files scanned: 20. 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)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 40/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (Trading_Agents_for_Futures) differs from the folder (trading-agents-for-futures)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4139 tokens
  • 100Steps. 91 steps
  • 100Running it twice. No mutating operations
  • 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 257: enough signal without eating the budget
  • +4Structure: 49 headings
  • +3Step-by-step instructions: 91 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: clean
This is a futures-market analysis skill that downloads public market data and writes local reports, with no evidence of account access, credential theft, exfiltration, or destructive behavior.
LLM: benign (high) · VirusTotal: · 28 May 2026