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BF 小果股票量化分析助手

小果(微信:xg_quant)股票量化分析助手专注于股票量化分析工具，基于小果量化策略系统，提供股票历史行情数据、股票分钟数据、股票因子数据、股票财务数据、指数数据、 股票策略回测、股票组合分析等核心功能。 适用于股票投资者、量化研究员和策略开发者。 触发关键词：股票量化、股票回测、股票分析、股票策略、股票数据、分钟数据、财务数据、指数数据。

ClawHub Agent Skills author: li152 v1.0.0 MIT-0 14 files body ≈ 57 061 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

小果(微信:xgquant)股票量化分析助手专注于股票量化分析工具，基于小果量化策略系统，提供股票历史行情数据、股票分钟数据、股票因子数据、股票财务数据、指数数据、 股票策略回测、股票组合分析等核心功能。 适用于股票投资者、量化研究员和策略开发者。…

As a process F 21/100 · Will not run — weak spots: steps, result and completion, when it triggers

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
78/100
safety, quality, tests
Safety 60%
100
Quality 40%
46
Run on models
none yet
Process rating
F
21/100
Will not run
Steps w 15
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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 14. 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")
  • warning body-long SKILL.md body ≈ 57061 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "contact"

Process rating: all ten parameters 21/100

  • 0Steps. Prose only: no discrete steps
  • 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
  • 10Execution cost. Instruction body is 57061 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (小果股票量化分析助手) differs from the folder (xg-stock-quant)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Running it twice. No mutating operations
  • low 52 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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • -46 reference files, but SKILL.md never points to them: the model will not open them
  • -35 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 172: enough signal without eating the budget
  • +4Structure: 68 headings
  • +4Has examples (55 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 46.

External checks

ClawHub: suspicious
The skill is mainly a stock quant tool, but it also sends credentials and code to a remote HTTP service and can create or delete persistent strategy records.
LLM: suspicious (high) · 2 Sept 2026