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CD HuggingFace Papers

获取 HuggingFace Papers 热门 AI 论文，自动翻译摘要为中文，生成带趋势分析的双语报告。 支持 DeepLX 和 OpenAI 兼容 LLM 两种翻译后端，自动降级。可保存为 Markdown 文件，方便导入 Obsidian 等笔记工具。 使用场景: 1. 用户想查看 HuggingFace 上的热门论文 2. 用户问"最近有什么 AI 论文推荐"或"有什么新论文" 3. 用户需要了解某个领域的最新研究动态 4. 用户想要 AI/ML 论文的摘要和趋势分析 **注意事项：** - 脚本默认通过 DeepLX API 将论文摘要翻译为中文 - 论文标题保留英文原文 - **必须保留完整摘要**，不得自行缩写或概括 - 中文摘要和英文原文摘要同时显示 - 使用 `--no-translate` 参数可输出纯英文报告 - upvotes 数值和论文链接保持原样 **输出规则：** - 运行脚本后，将报告同时 **保存为本地 .md 文件**（存入 skill 目录下的 `reports/` 文件夹） - 文件命名格式：`HuggingFace_本周热门论文_YYYY-MM-DD.md` - 保存完成后，告知用户文件路径，方便放入 Obsidian **工作流程:** 1. 运行 `scripts/get_papers.py` 获取论文数据 2. 运行 `scripts/generate_report.py --output reports/<文件名>.md` 生成报告并保存到文件 3. 将报告内容输出给用户，并告知文件路径方便放入 Obsidian

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Song Xiaowei v1.0.3 MIT-0 6 files body ≈ 538 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationObsidianData and analyticsAI and agentsWriting and documentstype 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
73/100
safety, quality, tests
Safety 60%
75
Quality 40%
70
Run on models
none yet
Process rating
D
41/100
Unfinished process
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

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

Broad scope
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.

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. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 4

  • high Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: OPENAI_API_KEY — OpenAI 兼容 API 密钥（使用 OpenAI 后端时必需） (high-value: OPENAI_API_KEY — OpenAI 兼容 API 密钥（使用 OpenAI 后端时必需）) — verify each one is needed for the stated purpose
    required_environment_variables: OPENAI_API_KEY — OpenAI 兼容 API 密钥（使用 OpenAI 后端时必需）
Medium and low: 3
  • medium Exfiltration net-redirectable-api-key scripts/generate_report.py:41
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment
  • low Exfiltration read-dotenv README.md:135
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv SKILL.md:69
    Reads a .env file
    cp .env.example .env

Files scanned: 6. 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 "required_environment_variables"

Process rating: all ten parameters 41/100

  • 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
  • 40Consistency. Frontmatter name (HuggingFace Papers) differs from the folder (hf-paper-skill)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Execution cost. Instruction body is 538 tokens
  • 100Running it twice. No mutating operations

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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 698: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (5 code blocks)
  • +3All 2 scripts are documented

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

External checks

ClawHub: clean
This skill coherently fetches public HuggingFace paper listings, optionally translates abstracts through configured services, and writes a local Markdown report.
LLM: benign (high) · VirusTotal: · 29 May 2026