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BC super-stock-trading

超级股票操盘 Skill —— 面向 A 股个人投资者的全流程操盘智能体 Skill 包。整合 117 个细粒度 Skills 与 26 位数字人专家，覆盖盘前分析、盘中决策、盘后复盘、选股、持仓体检、月度组合管理等完整投资闭环。 何时使用本 Skill：当用户提到「股票操盘」「盘前分析」「盘后复盘」「选股」「持仓体检」「月度组合管理」「仓位管理」「止损止盈」「板块轮动」「题材挖掘」「龙头战法」「财报季」「公告解读」「资金面」「北向资金」「龙虎榜」「风险体检」「组合再平衡」「定投」「指数基金」「期权对冲」「产业链」「护城河」「估值」「黑马」「打板」「异动」等关键词，或需要系统性地完成 A 股投研—交易—风控—复盘工作时，应启用本 Skill。 能力边界：主要服务于 A 股（沪深京）及与之相关的港股通、ETF、可转债、期权等品种的研究与决策辅助；不提供直接下单通道，所有交易建议均需用户人工二次确认；不构成任何投资建议，最终决策与盈亏由用户自行承担。

ClawHub Agent Skills author: WANG DONG JIE v1.0.0 MIT-0 26 files · 1 script body ≈ 1 856 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

超级股票操盘 Skill —— 面向 A 股个人投资者的全流程操盘智能体 Skill 包。整合 117 个细粒度 Skills 与 26 位数字人专家，覆盖盘前分析、盘中决策、盘后复盘、选股、持仓体检、月度组合管理等完整投资闭环。 何时使用本…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticsFinanceWriting and documentstype 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%
78
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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 · 6

✓ No critical or high findings

Medium and low: 6
  • medium Dangerous commands cmd-pipe-to-shell scripts/install.sh:185
    Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed)
    log_warn "未找到 npx,Wind AIFin Market Skill 将跳过。安装: curl -fsSL https://fnm.vercel.app/install | bash"
    code literal
  • medium Dangerous commands cmd-shell-rc scripts/install.sh:446
    Writes to a shell startup file
    echo 'export WIND_API_KEY="$cfg_wind"'           >> ~/.bashrc
  • medium Dangerous commands cmd-shell-rc scripts/install.sh:447
    Writes to a shell startup file
    echo 'export TUSHARE_TOKEN="$cfg_tushare"'       >> ~/.bashrc
  • medium Dangerous commands cmd-shell-rc scripts/install.sh:448
    Writes to a shell startup file
    echo 'export ALPHAVANTAGE_API_KEY="$cfg_alpha"'  >> ~/.bashrc
  • low Secrets in code secret-high-entropy-token references/risk_rules.md:58
    High-entropy token-like string (may be an id, hash or a credential)
    | **规则 ID** | R04-…mit |
  • low Secrets in code secret-high-entropy-token references/risk_rules.md:73
    High-entropy token-like string (may be an id, hash or a credential)
    | **规则 ID** | R05-…ker |

Files scanned: 26. 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 "language"
  • note frontmatter-key unknown frontmatter key "market"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 29 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1856 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
  • -34 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 431: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This stock-trading skill is not malicious, but it has high-impact installation and trading-execution ambiguity that users should review before installing.
LLM: suspicious (high) · 4 Jul 2026