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CD 产业链潜力股挖掘

一句话：输入一条产业链（人形机器人、AI 算力、固态电池、光伏……），Cue自动画出上中下游结构，找出每个环节里市占率高但还没被市场充分关注的"隐形冠军"，分析景气从哪个环节来、往哪个环节传导、谁的弹性最大。 对个人投资者的场景化价值： - 追热点不追在高位：一个新概念出来（比如人形机器人），所有人都在追那两家龙头。你用本搭子 5分钟拿到整条链的图谱，发现做谐波减速器、六维力传感器的那几家小公司，才是真正有业绩弹性的。 - 找龙头替代品：宁德时代太贵、比亚迪买不起。输入"新能源车产业链"，Cue帮你找到电池结构件、热管理、高压连接器这些细分环节里，基本面扎实、估值只有龙头一半的标的。 - 建立自己的研究框架：每次投一个新赛道，先用本搭子跑一遍产业链全景，形成自己的认知地图。长期积累下来，你对不同产业链的理解会比 90% 的散户深。 输出什么样： 产业链图谱（上中下游完整结构）→ 传导逻辑（景气从哪来、往哪去）→ 隐形冠军清单（细分龙头、高市占率、低关注度）→ 弹性评估（业绩弹性、估值弹性、事件弹性）→风险提示 → 每条结论附带公告/来源链接。 关联搭子使用： 找到一个"隐形冠军"只是第一步，验证它值不值得投还需要以下搭子配合：个股分析与基本面评估、上市公司财报分析、个股短线走势与技术面分析、企业全景画像等。

ClawHub Agent Skills author: panting09266-ai v1.1.5 MIT-0 2 files body ≈ 2 510 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

一句话：输入一条产业链（人形机器人、AI 算力、固态电池、光伏……），Cue自动画出上中下游结构，找出每个环节里市占率高但还没被市场充分关注的"隐形冠军"，分析景气从哪个环节来、往哪个环节传导、谁的弹性最大。 对个人投资者的场景化价值： -…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWordAI and agentstype 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
C
80/100
safety, quality, tests
Safety 60%
90
Quality 40%
65
Run on models
none yet
Process rating
D
46/100
Unfinished process
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.

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Exfiltration net-credential-use SKILL.md:254
    Credential used in a network call (verify the destination is the intended service)
    echo "=== 2/3 Cue 服务 ===" && curl -sS --max-time 10 "https://cuecue.cn/api/health" -H "Authorization: Bearer $CUE_KEY"
  • medium Exfiltration net-credential-use SKILL.md:255
    Credential used in a network call (verify the destination is the intended service)
    echo "=== 3/3 搭子 ===" && curl -sS --max-time 10 "https://cuecue.cn/api/playbook" -H "Authorization: Bearer $CUE_KEY" | python3 -c "import sys,json;scenes=json.load(sys.stdin).get('data',{}).get('scene

Files scanned: 2. 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 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 "description_zh"

Process rating: all ten parameters 46/100

  • 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
  • 40Consistency. Frontmatter name (产业链潜力股挖掘) differs from the folder (cue-supply-chain-mining)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 23 steps
  • 100Execution cost. Instruction body is 2510 tokens
  • 100Running it twice. No mutating operations
  • low 18 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

  • +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 562: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: suspicious
The skill is mostly a disclosed Cue-based stock research workflow, but it requires default Cue share-link insertion and proactive referral/payment promotion that go beyond the core research task.
LLM: suspicious (high) · 15 Sept 2026