📦
venue-templates
85Prepare Academic Venue Submissions
Academic submissions often fail because formatting rules vary by venue. This skill provides templates, requirements, and examples for faster preparation.
Research & Learning View
Skills denario
📦
denario
Content revision r1 Critical 📁 Filesystem access⚙️ External commands🌐 Network access🔑 Env variables
Automate Scientific Research with Denario
Scientific research slows down when ideas, methods, analysis, and manuscripts live in separate tools. Denario coordinates these stages with multiagent automation and reproducible project outputs.
Supports: Claude Codex Code(CC)
Do not auto-install this skill.
The canonical policy requires operator review before any installation action.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
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.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "denario". A researcher describes a climate time-series dataset and available Python analysis tools.
Expected outcome:
Denario proposes a warming-trend research question, a statistical methodology, expected figures, and a paper workflow.
Using "denario". A data scientist provides an approved machine learning methodology for churn prediction.
Expected outcome:
Denario runs the results stage, reports model metrics, describes generated figures, and prepares findings for review.
Using "denario". A team has completed results and wants a journal-style manuscript draft.
Expected outcome:
Denario builds a LaTeX paper structure with abstract, methods, results, discussion, figures, and references placeholders.
Security Audit
Criticalv7 • 7/6/2026 Open versioned report
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
Files scanned
1,662
Lines analyzed
22
Review items
0
False positives ignored
Confirmed security concerns (15)
Critical
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
Show all 15 confirmed findings
High
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.
High
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.
High
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.
High
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.
High
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.
Medium
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.
Low
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.
Capability review items (22)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
High
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.
Medium
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.
Low
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.
Low
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.
Low
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.
Low
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.
Low
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.
Low
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.
Low
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.
Low
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.
Risk Factors
📁 Filesystem access (4)
⚙️ External commands (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
🌐 Network access (2)
🔑 Env variables (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
Detected Patterns
Pipe to shell pattern
Audited by: codex View Audit History →
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APA citation
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 citation
@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"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Recently maintained
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
k-dense-ai-denario
Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 13
Updated
2026-09-09
davila7-denario
Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 13
Updated
2026-09-09
Skillstore Score
Why this score Evidence Confidence: Medium 45
Architecture
85
Maintainability
87
Content
75
Community
91
Spec Compliance
What You Can Build
Generate a Research Direction
Turn a dataset description into a research question and initial methodology for review.
Run Reproducible Analysis
Coordinate analysis steps, figures, and findings inside one structured project directory.
Draft a Manuscript
Convert existing ideas, methods, and results into a formatted LaTeX paper draft.
Try These Prompts
Start a Research Project
Use Denario to start a project for my dataset. My data includes [describe data]. Help me write a data description and generate one research idea.
Plan a Methodology
Use Denario to develop a methodology for this research idea: [idea]. Include validation steps, expected figures, and review points before execution.
Run Results Safely
Use Denario to generate results for this approved methodology: [method]. Run in an isolated project and summarize outputs before paper drafting.
Create a Journal Manuscript
Use Denario to create a LaTeX manuscript from my data description, idea, method, and results. Target APS format and list any missing evidence.
Best Practices
- Use a clean virtual environment or Docker container for each research project.
- Review generated ideas and methodologies before running result generation.
- Store API keys outside version control and rotate credentials regularly.
Avoid
- Do not run automated results on untrusted data without isolation.
- Do not paste real API keys into prompts, shared notebooks, or issue trackers.
- Do not treat generated papers as publication-ready without expert review.
Frequently Asked Questions
What does Denario automate?
It automates research idea generation, methodology planning, result generation, and LaTeX manuscript drafting.
Does Denario need API keys?
Yes. It requires credentials for supported LLM providers such as OpenAI or Google Vertex AI.
Can I provide my own research idea?
Yes. You can set custom ideas, methods, and results instead of generating every stage.
Can it create journal-formatted papers?
Yes. The documentation lists APS formatting and LaTeX output support.
Is the generated science automatically valid?
No. Researchers must review methods, data assumptions, results, and claims before use.
What security precautions are recommended?
Use isolated environments, protect API keys, avoid untrusted data, and review generated code before execution.
Developer Details
Author
K-Dense-AILicense
GPL-3.0 license
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/denarioRef
b8ca75d2c0a7e7102978993058777d82b8ab2610
Maintenance freshness
7/18/2026
Usage
11 downloads · 515 views
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