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FF smartlib-citation-checker

核查用户提交的论文稿件或AI生成参考文献是否真实，防止AI幻觉。基于SmartLib API，输出HTML核查报告（含差异标记、验证链接、统计分析）。支持GB/T 7714-2025（2026年7月1日起实施）/APA/MLA/Chicago/BibTeX多格式解析与输出，并行检索（8条/批）+ Token缓存复用 + 智能提前终止回退。✨ 亮点：核查结果附带原始数据库来源链接（覆盖300+数据库，如Scopus/WoS/EI/PubMed等，覆盖率100%），可交叉验证文献真实性。全程自动化：首次使用自动注册开通（免费100次/月），配额自动消耗，用尽后引导充值续费。触发词：核查引用、验证参考文献、检查引用、查引用、论文引用检查、AI引用核查、参考文献真假、文献核实、引用验证、引用格式检查、AI论文引用检查、参考文献审计、论文参考文献是真的吗、AI写的论文引用靠谱吗、ChatGPT引用核查、verify citation、check references。

Blockedguard blocked the skill: signs of malicious behaviour
ClawHub Agent Skills author: J-levee v3.6.3 MIT-0 7 files body ≈ 5 761 tokens Open the sourceclawhub.ai↗ analyzed 33 h ago

核查用户提交的论文稿件或AI生成参考文献是否真实，防止AI幻觉。基于SmartLib API，输出HTML核查报告（含差异标记、验证链接、统计分析）。支持GB/T 7714-2025（2026年7月1日起实施）/APA/MLA/Chicago/BibTeX多格式解析与输出，并行检索（8条/批）+ Token缓存复用…

As a process F 31/100 · Will not run — References files that are not bundled: references/account.md

IntegrationResearchSales and CRMtype 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
23/100
safety, quality, tests
Safety 60%
10
Quality 40%
43
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: references/account.md
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 If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail.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: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 5, column 14: description: 核查用户提交的论文稿件或AI生成参考文献是否真实，防止AI幻觉。基于SmartLib API，输出HTML核查报告（含差异标记、验证… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5761 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/account.md
  • 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 31/100

Will not run. References files that are not bundled: references/account.md
  • 0Tools and files. 1 referenced file(s) missing: references/account.md
  • 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
  • 70Execution cost. Instruction body is 5761 tokens
  • 100Steps. 40 steps
  • 100Consistency. Name and required fields are in place
  • low 13 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 435: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (17 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 43.

External checks

ClawHub: suspicious
This citation-checking skill is mostly coherent, but it handles email, manuscript/reference content, quota-bearing API calls, and third-party links with under-scoped disclosure and controls.
LLM: suspicious (medium) · 21 Jul 2026