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全球12亿文献知识库（8千万中文期刊可下载）——通过 SmartLib 开放平台 API 提供中外文学术文献检索与下载能力，覆盖 8000 万篇授权中文期刊全文 + 12.28 亿条全球文献元数据（期刊 7.19 亿 / 专利 2.15 亿 / 会议 7155 万 / 学位论文 2473 万 / 标准 268 万）。 能力：中英文关键词检索、文献详情、中文期刊 PDF 全文下载、外文 OA 文献十级渠道免费下载（不消耗配额）、智能关键词扩展、核心期刊优先排序、相关性重排、引文追溯、分类号检索。 配额：首次使用自动注册，免费 100 次检索 + 10 次下载 / 月；耗尽自动弹出套餐（体验卡 / 个人版月 / 专业版月 / 单篇下载 / 下载包），可说「升级 / 充值」唤起；企业 / 机构定制联系 vipsmart@vipslib.com。 触发：用户表达"查论文""找文献""检索学术""搜期刊""查专利""找标准""下论文""写文献综述""找参考文献""查 SCI/EI"等意图时启用；也适用"帮我找关于 XX 的论文""写文献综述""找几篇引用支撑论点"。英文："find papers" "search literature" "write literature review" "find supporting citations"。 调用前必须先用 /consume 获取 consume_token，再凭 token 调 /search（每次计费接口调用都需一次 /consume）。

Blockedguard blocked the skill: signs of malicious behaviour
ClawHub Agent Skills author: J-levee v3.10.2 MIT-0 8 files body ≈ 8 766 tokens Open the sourceclawhub.ai↗ analyzed 36 h ago

全球12亿文献知识库（8千万中文期刊可下载）——通过 SmartLib 开放平台 API 提供中外文学术文献检索与下载能力，覆盖 8000 万篇授权中文期刊全文 + 12.28 亿条全球文献元数据（期刊 7.19 亿 / 专利 2.15 亿 / 会议 7155 万 / 学位论文 2473 万 / 标准 268…

As a process F 29/100 · Will not run — References files that are not bundled: {url}

IntegrationSales and CRMResearchtype 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
F
30/100
safety, quality, tests
Safety 60%
10
Quality 40%
60
Run on models
none yet
Process rating
F
29/100
Will not run
References files that are not bundled: {url}
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten
Guard blocked this skill: critical findings below. Do not install it until the author fixes them.

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Secrets in code
If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

How to improve

  1. Remove the critical guard findings (secrets, dangerous commands, hidden instructions): while they stand the skill is blocked and cannot grade above F.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  4. The text references files that are not there: add them or drop the references.
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

  • critical Secrets in code secret-openai-key config.json:3
    OpenAI-style API key (quoted — discussed, not commanded)
    "SMARTLIB_GATEWAY_SECRET": "sk-O…mi2",
    quoted

Files scanned: 8. 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")
  • warning body-long SKILL.md body ≈ 8766 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: {url}
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 29/100

Will not run. References files that are not bundled: {url}
  • 0Tools and files. 1 referenced file(s) missing: {url}
  • 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. 3 mutating operations with no state check
  • 40Execution cost. Instruction body is 8766 tokens: crowds the task out of the window
  • 100Steps. 106 steps
  • 100Consistency. Name and required fields are in place
  • 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -224 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 657: enough signal without eating the budget
  • +4Structure: 65 headings
  • +3Step-by-step instructions: 106 items
  • +4Has examples (26 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: suspicious
The skill is a real literature-search integration, but it asks the agent to register users, handle payments, store identifiers, and try broad PDF retrieval methods including anti-hotlink workarounds.
LLM: suspicious (high) · 30 Aug 2026