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BC hekouwang-stock-data-reader-skill

会勇禾口王 · 个股公开数据速读 Skill。输入一个 A 股代码，自动用 akshare 拉取「资金流 / 龙虎榜 / 估值band / 财务三表 / 主营构成 / 北向 / 日K」等公开数据，算出固定指标体系 （价格轨迹·主力进出·席位结构·利润质量·利润结构·估值·风险线索），并产出「中立事后复盘」 形态的财经贴图 / 公众号长文 / 头条三件套（V2 米白或 V3 财经风）。内置金融合规护栏： 不荐股、不预测涨跌、不给买卖点，自动套风险提示条。 触发词：个股数据速读 / 扒公司公开数据 / 资金面复盘 / 出个股报告 / 3天一只 / stock-data-reader / 给我一只股的数据体检 / 用 akshare 拉某股数据。 ⚠️ 个股走公众号/头条为主；小红书仅限"中立复盘/算账/名词科普"形态且守合规护栏（不荐股/不预测/零站外导流），别发以资金流·席位为钩子的机构观感版。

ClawHub Agent Skills author: 会勇禾口王的AI笔记 v1.2.2 MIT-0 15 files · 1 script body ≈ 599 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

会勇禾口王 · 个股公开数据速读 Skill。输入一个 A 股代码，自动用 akshare 拉取「资金流 / 龙虎榜 / 估值band / 财务三表 / 主营构成 / 北向 / 日K」等公开数据，算出固定指标体系 （价格轨迹·主力进出·席位结构·利润质量·利润结构·估值·风险线索），并产出「中立事后复盘」…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticsSecuritytype 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%
95
Quality 40%
77
Run on models
none yet
Process rating
C
51/100
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.

Broad scope 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 asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash Read Write Edit Glob Grep AskUserQuestion

Files scanned: 15. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 51/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 24 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 599 tokens

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
  • +2Single-language instructions
  • +3Description length 402: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 4 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
This skill is a disclosed stock-data reporting pipeline; users should expect third-party market-data lookups and local report files, including cleanup of generated HTML after screenshots.
LLM: benign (high) · VirusTotal: · 12 Aug 2026