open-notebook

ISSUES FOUND max severity CRITICALSkillSpector Do not install 51 registry K-Dense-AI last scanned 2026-10-21 1 scan

7 findings (3 critical, 2 high, 2 medium), mainly data exfiltration, social engineering, resource abuse. Most recent scan 2026-10-21.

Findings7

Critical3

High2

Medium2

Low0

Analyzersstatic bytecode pipeline correlation behavioral trigger

Scanner version2.3.1

Second scanner: NVIDIA SkillSpector

VerdictDo not install 51

Score51 / 100

Issues12

CategoriesTool Misuse Data Exfiltration MCP Least Privilege

Version2.12.0 (static)

Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.

What this skill does

Organizes research with the self-hosted Open Notebook alternative to NotebookLM. Supports source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts. Use when automating Open Notebook through its REST API or configuring its local or cloud AI providers, including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral.

Package: 9 files, 66 KB
SKILL.md: 1234 words, 12+ sections
Code: Python (4)
License: MIT
Compatibility: Requires a running Open Notebook backend and worker; Python 3.11+ with requests for bundled helpers. Docker Compose is the documented deployment option. Network access to the instance and any configured providers is required.

SKILL.md covers: Open Notebook · Overview · Quick Start · Installation · Configure AI providers · Use the bundled helpers · Core Features and Workflow · Notebooks and notes · Source ingestion · Context-aware chat · Search and Ask · Transformations and podcasts

Findings

CRITICAL Cross-file env var exfiltration: 1 files Data Exfiltrationbehavioral

Environment variable access with network calls in scripts/_common.py

Remediation: Review data flow across files: scripts/_common.py

CRITICAL Cross-file exfiltration chain: 1 files Data Exfiltrationbehavioral

Multi-file exfiltration chain detected: scripts/_common.py collect data → encode → scripts/_common.py transmit to network

Remediation: Review data flow across files: scripts/_common.py

CRITICAL Environment variable access with network calls detected Data Exfiltrationbehavioral

Script accesses environment variables and makes network calls in /home/runner/work/skill-scanner-test/skill-scanner-test/skills/K-Dense-AI-scientific-agent-skills/skills/open-notebook/scripts/_common.py

/home/runner/work/skill-scanner-test/skill-scanner-test/skills/K-Dense-AI-scientific-agent-skills/skills/open-notebook/scripts/_common.py

Remediation: Remove environment variable harvesting or network transmission

HIGH Sensitive source reaches a network sink Data Exfiltrationcorrelation

Correlated sensitive_environment → network behavior across scripts/_common.py.

scripts/_common.py:29

Remediation: Remove the transmission or constrain it to explicitly declared, trusted destinations.

HIGH Infinite loop without clear exit condition Resource Abusestatic

Pattern detected: while True:

scripts/source_ingestion.py:56

while True:

Remediation: Add proper exit conditions or limits to loops

MEDIUM Potential description-behavior mismatch Social Engineeringstatic

Skill performs actions not reflected in its description

SKILL.md

Remediation: Ensure description accurately reflects all skill capabilities

MEDIUM Outbound network request primitives that can transmit data externally Data Exfiltrationstatic

Pattern detected: requests.request(

scripts/_common.py:29

response = requests.request(

Remediation: Ensure network operations are necessary and document allowed destinations

What is in the package

File	Kind	Size
`SKILL.md`	doc	11 KB
`references/api_reference.md`	doc	19 KB
`references/architecture.md`	doc	4.9 KB
`references/configuration.md`	doc	9.2 KB
`references/examples.md`	doc	8.2 KB
`scripts/_common.py`	script	1.5 KB
`scripts/chat_interaction.py`	script	3.5 KB
`scripts/notebook_management.py`	script	2.6 KB
`scripts/source_ingestion.py`	script	6.0 KB

External hosts referenced

github.com arxiv.org raw.githubusercontent.com doi.org export.arxiv.org

Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.

Environment variables referenced

OPEN_NOTEBOOK_PASSWORD OPEN_NOTEBOOK_ENCRYPTION_KEY OPEN_NOTEBOOK_URL API_URL SURREAL_PASS SURREAL_PASSWORD ANTHROPIC_API_KEY GOOGLE_API_KEY GROQ_API_KEY INTERNAL_API_URL OPENAI_API_KEY SURREAL_DATABASE SURREAL_URL

Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.

What we checked

Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
Bytecode analyzer.
Pipeline analyzer.
Correlation analyzer.
Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.

What we did not check

Runtime behaviour. The skill was not executed; findings come from reading its files.
Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
Content the analyzers cannot read: images, binaries, and some non-English text.
Known scanner bypass classes. See the methodology and limitations page.

Sources

Repository: https://github.com/K-Dense-AI/scientific-agent-skills
Raw scan result: skills-open-notebook-scan.json
Badge for your README: ![skillscan.io](https://skillscan.io/badge/k-dense-ai-open-notebook.svg)
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