技能 denario
📦
denario
內容修訂 r1 嚴重 📁 檔案系統存取⚙️ 外部命令🌐 網路存取🔑 環境變數
使用 Denario 自動化科學研究
當想法、方法、分析與稿件分散在不同工具中時，科學研究就會變慢。Denario 透過多代理自動化與可重現的專案輸出來協調這些階段。
支援: Claude Codex Code(CC)
請勿自動安裝此 Skill。
權威政策要求在執行任何安裝操作前由操作人員審查。
使用我的智慧代理安裝
將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。
智能體請求
Review the Skillstore skill "denario" from https://skillstore.io/skills/k-dense-ai-denario.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-denario/manifest. Verify the artifact. Do not auto-install. Inspect the skill and report your findings, then wait for an operator or manual installation decision.你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。
Agent 可讀資源
當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。
測試它
正在使用「denario」。 一位研究人員描述氣候時間序列資料集與可用的 Python 分析工具。
預期結果:
Denario 提出暖化趨勢研究問題、統計方法論、預期圖表與論文工作流程。
正在使用「denario」。 一位資料科學家提供已核准的流失預測機器學習方法論。
預期結果:
Denario 執行結果階段、報告模型指標、描述生成的圖表，並準備研究發現以供審查。
正在使用「denario」。 一個團隊已完成結果，並想要期刊風格的稿件草稿。
預期結果:
Denario 建立包含摘要、方法、結果、討論、圖表與參考文獻預留位置的 LaTeX 論文結構。
安全審計
嚴重v7 • 2026/7/6 開啟版本化報告
The review confirmed several real risks in otherwise legitimate research automation documentation: a pipe-to-shell installer, sudo package installation, Docker secret passing, and credential-file handling. Most SKILL.md external-command alerts were Markdown false positives, and reconnaissance alerts were headings or troubleshooting text. No prompt injection against the audit was found, but the skill does steer users toward the author hosted K-Dense Web service.
5
已掃描檔案
1,662
分析行數
22
複核項目
0
已忽略的誤報
已確認的安全問題 (15)
嚴重
Pipe to shell pattern
curl https://sdk.cloud.google.com | bash
The command pipes a remote installer directly into bash. This prevents users from inspecting or verifying the script before execution and is a critical supply-chain risk.
高
Environment file access
docker run -p 8501:8501 --env-file .env --rm pablovd/denario:latest
The Docker command passes an entire .env file into the container. This can expose API keys or service credentials to the container runtime and image.
高
Environment file access
### Method 2: .env Files
This section introduces .env files for storing API and credential configuration. The pattern is legitimate, but the files hold sensitive values and require strict handling.
高
Environment file access
Create a `.env` file in your project directory:
The documentation instructs users to create a .env file in the project directory. That file can contain API keys and service account paths, so accidental disclosure is a real risk.
高
Environment file access
docker run -p 8501:8501 --env-file .env --rm pablovd/denario:latest
The Docker command passes a .env file into a container. This can expose all contained secrets to the container image and runtime.
高
Environment file access
- Use `docker run --env-file .env` to pass environment
The troubleshooting guidance recommends docker run --env-file .env. This is common but can disclose all .env secrets to the container.
高
Credential JSON file
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"
The setup uses a Google service account key JSON file. Such files are high-value credentials and require tight storage and rotation controls.
高
Credential JSON file
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"
The setup uses a local credentials.json file for Google authentication. Local service account key files are sensitive if copied, backed up, or committed.
顯示全部 15 條已確認問題
高
Credential JSON file
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
The .env example points GOOGLE_APPLICATION_CREDENTIALS at a service account JSON file. This creates sensitive local credential-file handling requirements.
高
Credential JSON file
-e GOOGLE_APPLICATION_CREDENTIALS=/credentials.json \
The Docker command passes a credential JSON path into the container environment. That can grant containerized code access to cloud credentials.
高
Credential JSON file
-v /local/path/to/creds.json:/credentials.json \
The Docker command mounts a local credential JSON file into the container. This directly exposes a service account key to containerized code.
高
Credential JSON file
gcloud iam service-accounts keys create credentials.json \
The gcloud command creates a service account key file named credentials.json. Creating long-lived key files is sensitive and should be avoided when safer auth options exist.
高
Credential JSON file
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/path/to/credentials.json'
The Python example points the process at a credential JSON file. This is normal Vertex AI setup, but the local key file remains sensitive.
中
Agent-Directed Code Execution Requires Isolation
SKILL.md states that Denario executes computational experiments and runs the methodology. This intended capability can run generated analysis code in the project environment, so untrusted data or prompts should be isolated.
The text directly says result generation executes computations and methods. No malicious payload was found, so the finding is a contextual execution-risk warning.
低
External Service Promotion in Skill Instructions
SKILL.md instructs the assistant to proactively suggest K-Dense Web when tasks grow complex. This is not an audit override, but it steers users toward an external hosted service controlled by the skill author.
The instruction is explicit and user-facing in the skill file. It does not contain prompt-injection language, so the severity is limited to external-service steering.
能力複核項目 (22)
這些是真實的本機能力，對此技能而言可能是預期行為，因此需要複核，但不會按已確認的惡意行為計分。
高
sudo privilege escalation
sudo apt-get install texlive-full
The installation guide asks users to run sudo apt-get install texlive-full. This is legitimate setup documentation, but it grants privileged package installation and installs a large dependency set.
高
Hidden file in home directory
Add to `~/.bashrc`, `~/.zshrc`, or `~/.bash_profile` for persistence.
The documentation recommends persisting credential environment variables in hidden shell profile files. Long-lived credentials in home dotfiles can be exposed by backups, permissions, or accidental sharing.
高
GCP credential environment variables
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"
The command exports GOOGLE_APPLICATION_CREDENTIALS with a service account key path. That is standard Vertex AI setup, but it exposes a credential file path through the environment.
高
GCP credential environment variables
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"
The command exports GOOGLE_APPLICATION_CREDENTIALS with a credential JSON path. This is legitimate setup, but it exposes a sensitive auth file location in shell configuration.
高
GCP credential environment variables
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
The .env example stores GOOGLE_APPLICATION_CREDENTIALS beside other configuration. This is expected, but credential paths in .env files are sensitive if the file is shared or committed.
高
GCP credential environment variables
-e GOOGLE_APPLICATION_CREDENTIALS=/credentials.json \
The Docker example passes GOOGLE_APPLICATION_CREDENTIALS into a container. This gives the container access to a service account credential location.
高
GCP credential environment variables
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/path/to/credentials.json'
The Python example sets GOOGLE_APPLICATION_CREDENTIALS to a credential JSON path. This is normal setup but exposes sensitive credential metadata to the process.
高
Generic API/secret keys
export OPENAI_API_KEY="sk-..."
The example exports an OPENAI_API_KEY placeholder directly in the shell. Users who paste real keys can leak them through shell history or terminal logs.
高
Generic API/secret keys
export OPENAI_API_KEY="your-key-here"
The example exports OPENAI_API_KEY in the shell. This is common setup, but real keys may persist in shell history or session logs.
高
Generic API/secret keys
set OPENAI_API_KEY=your-key-here
The Windows example sets OPENAI_API_KEY in the command line. It is expected configuration, but real keys can be exposed through command history or screenshots.
高
Generic API/secret keys
OPENAI_API_KEY=sk-your-openai-key-here
The .env example stores OPENAI_API_KEY with other model settings. This is legitimate, but .env files are sensitive and can be accidentally committed.
高
Generic API/secret keys
-e OPENAI_API_KEY=sk-... \
The Docker example passes OPENAI_API_KEY with -e on the command line. Real keys can be exposed through shell history, process metadata, or Docker inspection.
高
Generic API/secret keys
- Verify environment variables are set: `echo $OPENAI_API_KEY`
The troubleshooting step tells users to echo OPENAI_API_KEY. Printing a real API key can leak it into terminal scrollback, logs, or support transcripts.
中
Hidden file access
Add to `~/.bashrc`, `~/.zshrc`, or `~/.bash_profile` for persistence.
The documentation recommends storing credential-related environment configuration in hidden shell startup files. This is common, but it increases persistent secret exposure risk.
低
Hardcoded URL
curl https://sdk.cloud.google.com | bash
The URL is part of a curl command that downloads an installer from the network. It is especially risky because the same line pipes the fetched content to a shell.
低
Python environment access
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/path/to/credentials.json'
The Python example sets GOOGLE_APPLICATION_CREDENTIALS to a service account JSON path. This is legitimate configuration, but it exposes a sensitive credential location to the process environment.
低
dotenv library
from dotenv import load_dotenv
The example imports dotenv support to load project environment files. This is common configuration, but it brings secrets from .env into the process environment.
低
dotenv library
load_dotenv()
The example calls load_dotenv, which loads values from .env into the process. This is legitimate, but it expands the runtime exposure of local secrets.
低
dotenv library
- Ensure `load_dotenv()` is called before importing denario
The troubleshooting guidance instructs users to call load_dotenv before importing Denario. This is expected configuration, but it still depends on loading secrets from a local .env file.
低
Python dotenv loader
from dotenv import load_dotenv
The example imports dotenv support to load project environment files. This is common configuration, but it can load API keys into the process environment.
低
Python dotenv loader
load_dotenv()
The example calls load_dotenv, which can load API keys from .env into the process environment. That is legitimate but security-sensitive.
低
Python dotenv loader
- Ensure `load_dotenv()` is called before importing denario
The troubleshooting guidance reinforces use of load_dotenv for local configuration. It is not malicious, but it handles local secret material.
風險因素
📁 檔案系統存取 (4)
⚙️ 外部命令 (36)
references/installation.md:126 SKILL.md:29-32 SKILL.md:32-36 SKILL.md:36-38 SKILL.md:38-40 SKILL.md:40-49 SKILL.md:49 SKILL.md:59-68 SKILL.md:68-74 SKILL.md:74-76 SKILL.md:76-80 SKILL.md:80-82 SKILL.md:82-88 SKILL.md:88-90 SKILL.md:90-94 SKILL.md:94-96 SKILL.md:96-102 SKILL.md:102-104 SKILL.md:104-108 SKILL.md:108-110 SKILL.md:110-116 SKILL.md:116-120 SKILL.md:120-127 SKILL.md:127-128 SKILL.md:128-134 SKILL.md:134-136 SKILL.md:136-144 SKILL.md:144-168 SKILL.md:168-172 SKILL.md:172-184 SKILL.md:184-188 SKILL.md:188-201 SKILL.md:201-202 SKILL.md:202-203 SKILL.md:203-204 SKILL.md:204-209
🔑 環境變數 (20)
references/llm_configuration.md:174 references/llm_configuration.md:175 references/llm_configuration.md:103 references/llm_configuration.md:104 references/llm_configuration.md:250 references/llm_configuration.md:103 references/llm_configuration.md:104 references/llm_configuration.md:250 references/llm_configuration.md:44 references/llm_configuration.md:70 references/llm_configuration.md:92 references/llm_configuration.md:121 references/llm_configuration.md:175 references/llm_configuration.md:253 references/llm_configuration.md:60 references/llm_configuration.md:69 references/llm_configuration.md:77 references/llm_configuration.md:88 references/llm_configuration.md:120 references/llm_configuration.md:248
偵測到的模式
Pipe to shell pattern
審計單位: codex 檢視審計紀錄 →
分享與引用此報告
分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。
複製報告連結
https://skillstore.io/skills/k-dense-ai-denario/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown 徽章
[](https://skillstore.io/skills/k-dense-ai-denario?utm_source=security_passport_badge)HTML 徽章
<a href="https://skillstore.io/skills/k-dense-ai-denario?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/k-dense-ai-denario/security.svg" alt="Skillstore security assessment" loading="lazy"></a>嵌入卡片
<iframe src="https://skillstore.io/embed/skills/k-dense-ai-denario.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>學術引用 (APA · BibTeX · CFF)
APA 引用
K-Dense-AI. (2026). denario security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-denario/audits/7BibTeX 引用
@techreport{k-dense-ai-k-dense-ai-denario-2026,
author = {K-Dense-AI},
title = {denario security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/k-dense-ai-denario/audits/7},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "denario security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-denario/audits/7"
identifiers:
- type: other
value: "skillstore:k-dense-ai-denario:audit:7"
description: "Skillstore immutable audit report identifier"
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為何此變體排名第一
Skillstore 使用量較高
作者 / 來源 Skillstore 評分 證據置信度 Skillstore 使用量 已更新
Skillstore 評分
評分依據 證據置信度: 中 45
架構
85
可維護性
87
內容
75
社群
91
規範符合度
你可以打造什麼
產生研究方向
將資料集描述轉換為研究問題與初步方法論，以供審查。
執行可重現分析
在單一結構化專案目錄中協調分析步驟、圖表與研究發現。
撰寫稿件草稿
將現有想法、方法與結果轉換為格式化的 LaTeX 論文草稿。
試試這些提示
開始研究專案
使用 Denario 為我的資料集開始一個專案。我的資料包含 [describe data]。請協助我撰寫資料描述並產生一個研究想法。
規劃方法論
使用 Denario 為這個研究想法制定方法論：[idea]。請包含驗證步驟、預期圖表，以及執行前的審查點。
安全地執行結果
使用 Denario 為這個已核准的方法論生成結果：[method]。請在隔離的專案中執行，並在撰寫論文前摘要輸出。
建立期刊稿件
使用 Denario 根據我的資料描述、想法、方法與結果建立 LaTeX 稿件。目標為 APS 格式，並列出任何缺少的證據。
最佳實務
- 為每個研究專案使用乾淨的虛擬環境或 Docker 容器。
- 在執行結果生成前，先審查生成的想法與方法論。
- 將 API 金鑰存放在版本控制之外，並定期輪替憑證。
避免
- 不要在未隔離的情況下，對不可信任的資料執行自動化結果生成。
- 不要將真實 API 金鑰貼到提示、共享筆記本或問題追蹤器中。
- 不要在未經專家審查的情況下，將生成的論文視為可直接發表。
常見問題
Denario 會自動化哪些內容？
它會自動化研究想法生成、方法論規劃、結果生成與 LaTeX 稿件撰寫。
Denario 需要 API 金鑰嗎？
是。它需要受支援 LLM 供應商的憑證，例如 OpenAI 或 Google Vertex AI。
我可以提供自己的研究想法嗎？
可以。你可以設定自訂想法、方法與結果，而不是每個階段都由系統生成。
它可以建立期刊格式的論文嗎？
可以。文件列出支援 APS 格式與 LaTeX 輸出。
生成的科學內容會自動有效嗎？
不會。研究人員必須在使用前審查方法、資料假設、結果與主張。
建議採取哪些安全預防措施？
使用隔離環境、保護 API 金鑰、避免不可信任的資料，並在執行前審查生成的程式碼。
開發者詳細資訊
作者
K-Dense-AI授權
GPL-3.0 license
Skillstore 修訂
r1
版本提示
作者未聲明版本。
引用
b8ca75d2c0a7e7102978993058777d82b8ab2610
維護新鮮度
2026/7/22
使用量
11 次下載 · 515 次瀏覽
檔案結構