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AF precise-t-trading

Professional T+0 intraday trading system for Chinese A-shares. Uses Bayesian inference, Kelly criterion, and VaR risk management to optimize day-trading decisions. Supports real-time quotes from Tencent Finance API. Ideal for active traders seeking quantitative edge in volatile markets. Includes risk control, position sizing, and automated monitoring.

ClawHub Agent Skills author: yang77160 v1.0.4 MIT-0 9 files body ≈ 1 836 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 45/100 · Will not run — References files that are not bundled: assets/e3809b1ca279c53750dfe37b4ade419a.jpg, scripts/config.py

IntegrationInfrastructuretype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
F
45/100
Will not run
References files that are not bundled: assets/e3809b1ca279c53750dfe37b4ade419a.jpg, scripts/config.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: assets/e3809b1ca279c53750dfe37b4ade419a.jpg
  • warning missing-ref reference to a missing file: scripts/config.py
  • note frontmatter-key unknown frontmatter key "required_env_vars"
  • note frontmatter-key unknown frontmatter key "optional_env_vars"
  • note frontmatter-key unknown frontmatter key "network"
  • note frontmatter-key unknown frontmatter key "writes"
  • note frontmatter-key unknown frontmatter key "install"

Process rating: all ten parameters 45/100

Will not run. References files that are not bundled: assets/e3809b1ca279c53750dfe37b4ade419a.jpg, scripts/config.py
  • 0Tools and files. 2 referenced file(s) missing: assets/e3809b1ca279c53750dfe37b4ade419a.jpg, scripts/config.py
  • 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
  • 20When it triggers. No condition that starts the skill
  • 100Steps. 74 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1836 tokens
  • 100Running it twice. No mutating operations
  • low 19 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
  • -213 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 353: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 74 items
  • +4Has examples (13 code blocks)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
This is not malware, but it gives actionable trading guidance from simulated and hard-coded inputs, so users should review it carefully before relying on it.
LLM: suspicious (high) · VirusTotal: · 29 May 2026