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BF 小果基金量化分析助手

小果(微信:xg_quant)基金量化分析助手专注于基金量化分析工具，基于小果量化策略系统，提供基金历史行情数据、基金因子数据、基金策略回测、基金组合分析等核心功能。适用于基金投资者、资产配置研究员和量化策略开发者。

ClawHub Agent Skills author: li152 v1.0.0 MIT-0 14 files body ≈ 59 290 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

小果(微信:xgquant)基金量化分析助手专注于基金量化分析工具，基于小果量化策略系统，提供基金历史行情数据、基金因子数据、基金策略回测、基金组合分析等核心功能。适用于基金投资者、资产配置研究员和量化策略开发者。

As a process F 18/100 · 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
76/100
safety, quality, tests
Safety 60%
100
Quality 40%
41
Run on models
none yet
Process rating
F
18/100
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 ≈ 59290 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "contact"

Process rating: all ten parameters 18/100

  • 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 59290 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 40Consistency. Frontmatter name (小果基金量化分析助手) differs from the folder (xg-fund-quant)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare

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)
  • +3Description length 108: 120–800 characters recommended
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • -2133 emoji in the instructions: noise for the model
  • -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
  • +4Structure: 243 headings
  • +4Has examples (55 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 41.

External checks

ClawHub: suspicious
This skill is a disclosed quant-analysis API manual, but it grants broad credentialed network, code-submission, deletion, and file-write capabilities that are under-scoped for a fund-analysis skill.
LLM: suspicious (high) · 2 Sept 2026