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BD BaoStock 金融数据

获取A股历史K线数据、季频财务数据、宏观经济数据、板块成分股等

ClawHub Agent Skills author: jasonpro22 v1.0.1 MIT-0 9 files body ≈ 1 905 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

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
79/100
safety, quality, tests
Safety 60%
99
Quality 40%
48
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
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.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token docs/python_api_full.txt:1
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "首页\nBaostock知识库\n交易技术商城\n统计与数据\n登录\n注册\n平台介绍\nPython API文档\n--A股K线数据\n--PY开发资源\n--指数数据\n--估值指标(日频)\n--除权除息信息\n--复权因子信息\n--季频盈利能力\n--季频营运能力\n--季频成长能力\n--季频偿债能力\n--季频现金流量\n--季频杜邦指数\n--季频业绩快报\n--季频业绩预告\
    quoted

Files scanned: 9. 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-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "read_when"

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 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
  • 40Consistency. Frontmatter name (BaoStock 金融数据) differs from the folder (baostock-tt-skills)
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 52 steps
  • 100Execution cost. Instruction body is 1905 tokens
  • 100Running it twice. No mutating operations

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 31: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -32 of 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 52 items
  • +4Has examples (11 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 48.

External checks

ClawHub: clean
This is a coherent BaoStock market-data and technical-analysis skill, but its trading-style outputs should be treated cautiously because one RSI calculation appears mislabeled.
LLM: benign (medium) · VirusTotal: · 29 May 2026