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BF Kerrystock

个股/ETF 的「日历效应（季节性）买卖点分析」端到端工作流。当用户要求分析某只股票或基金的 月度/年度季节性规律、确定基于日历效应的买入卖出时间点与买卖策略、或做"季节性 + 技术指标" 复合选股时使用本技能。本技能串联 westock-data（行情/技术指标/投资日历）、 neodata-financial-search（历史区间涨跌幅交叉验证）、wb-finance-skill 的 seasonality.py （季节性信号引擎）与 trade-plan 框架（买卖点：位置判断→分仓→止盈止损→失效条件）， 最后用 westock-tool 按策略信号选股。This skill should be used when the user asks for calendar/seasonal effect analysis, buy/sell timing, or seasonal+technical stock screening.

ClawHub Agent Skills author: caoshun-sudo v1.0.0 MIT-0 9 files body ≈ 1 243 tokens Open the sourceclawhub.ai↗ analyzed 25 h ago

个股/ETF 的「日历效应（季节性）买卖点分析」端到端工作流。当用户要求分析某只股票或基金的 月度/年度季节性规律、确定基于日历效应的买入卖出时间点与买卖策略、或做"季节性 + 技术指标" 复合选股时使用本技能。本技能串联 westock-data（行情/技术指标/投资日历）、…

As a process F 41/100 · Will not run — References files that are not bundled: scripts/quant/seasonality.py, references/trade-plan.md, scripts/export_kline.py

AnalyzerPersonal productivitySoftware developmentData and analyticstype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
F
41/100
Will not run
References files that are not bundled: scripts/quant/seasonality.py, references/trade-plan.md, scripts/export_kline.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: 0. 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 missing-ref reference to a missing file: scripts/quant/seasonality.py
  • warning missing-ref reference to a missing file: references/trade-plan.md
  • warning missing-ref reference to a missing file: scripts/export_kline.py
  • warning missing-ref reference to a missing file: scripts/common.py
  • warning missing-ref reference to a missing file: scripts/neodata_verify.py
  • warning missing-ref reference to a missing file: references/lessons.md
  • warning missing-ref reference to a missing file: scripts/seasonal_analysis.py
  • warning missing-ref reference to a missing file: scripts/gen_report.py
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 41/100

Will not run. References files that are not bundled: scripts/quant/seasonality.py, references/trade-plan.md, scripts/export_kline.py
  • 0Tools and files. 8 referenced file(s) missing: scripts/quant/seasonality.py, references/trade-plan.md, scripts/export_kline.py
  • 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
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1243 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 425: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

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

External checks

ClawHub: clean
This skill is a disclosed financial-analysis workflow that runs local helper scripts and writes user-requested reports, with no evidence of hidden or destructive behavior.
LLM: benign (high) · VirusTotal: · 20 Jul 2026