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BD toc-stock

TOC 股票助手 - AI股神挑战 + 股票分析 + 交易模拟 功能： - AI股神挑战：AI自主选股决策，每日同步操作和收益 - 四大行业分析：AI/消费品/汽车/医疗板块涨跌 - 市场热点：热门概念、板块资金流向 - 股票池：自选股管理 - 持仓模拟：买入/卖出/盈亏计算 - 演练模式：假设交易收益计算 触发场景： - "开启挑战" / "挑战状态" / "挑战统计" - "四大行业" / "市场概况" / "有什么消息" - "加一只 XXX" / "持仓" / "买 100 手 @ 15.6" - "推荐一只股票" / "如果昨天开盘买入 XXX"

ClawHub Agent Skills author: wuritu v1.0.0 MIT-0 18 files body ≈ 474 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 0

✓ No critical or high findings

Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "title"
  • note frontmatter-key unknown frontmatter key "owner"
  • note frontmatter-key unknown frontmatter key "requirements"

Process rating: all ten parameters 49/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (toc-stock) differs from the folder (toc-trading)
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Execution cost. Instruction body is 474 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 283: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (3 code blocks)

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

External checks

ClawHub: clean
This is a coherent simulated stock-trading assistant that stores local portfolio records and fetches market data, with no evidence of real brokerage trading, exfiltration, or destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026