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AI链条机构建仓探测算法。基于Tushare结构化数据，通过5维信号评分体系（资金流+量价+筹码+北向+事件） 识别A股AI链条（算力/芯片/大模型/应用）机构建仓行为，生成买入信号（10日持有期、-15%止损）。 含市场环境判断（11指标）、外部情绪聚合（5源）、日内分钟线分析、大规模回测框架。 回测结果：544次买入信号，10日胜率55.3%，P=0.007（统计显著），样本外59.0%（P=0.006）。 当用户提到以下意图时触发此 skill： "机构建仓检测"、"AI链条选股"、"资金流分析"、"量价信号"、"建仓探测"、 "机构行为识别"、"吸筹信号"、"A股机构跟踪"、"AI赛道筛选"、"智能选股"、 "帮我跑一遍建仓探测"、"今天有哪些AI股票在吸筹"、"跑回测"、"评估算法可信度"。

ClawHub Agent Skills author: casparzhong-cloud v0.1.0 MIT-0 16 files · 1 script body ≈ 1 075 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubInfrastructuretype 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
87/100
safety, quality, tests
Safety 60%
95
Quality 40%
75
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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

Files scanned: 16. 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 "type"
  • note frontmatter-key unknown frontmatter key "best_for"
  • note frontmatter-key unknown frontmatter key "scenarios"
  • note frontmatter-key unknown frontmatter key "estimated_time"
  • note frontmatter-key unknown frontmatter key "references"

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 38 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1075 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill

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
  • -36 of 9 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 8 example trigger phrases
  • +3Description length 356: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This financial-analysis skill is mostly coherent, but it under-discloses credential handling and local cross-skill script execution that users should review before installing.
LLM: suspicious (high) · VirusTotal: · 29 May 2026