hypogenic
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
The two scanners disagree: Cisco found nothing and SkillSpector says do not install. One of them has a blind spot here; read the skill before trusting either. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.
- Package
- 18 files, 150 KB
- SKILL.md
- 1777 words, 12+ sections
- Code
- Python (7)
- Allowed tools
ReadWriteEditBashGlobGrep- License
- MIT
- Compatibility
- Requires Python 3.10+ and uv for the pinned upstream package. Bundled local audit tools use only the Python standard library for JSON; YAML input requires exactly PyYAML 6.0.3. Actual HypoGeniC runs may require a separately approved LLM provider, credentials, Redis, local model resources, and network access.
SKILL.md covers: HypoGeniC · Scope and scientific boundary · Default workflow: local review first · Reproducible installation · Safe configuration · Dataset and prompt-text safety · Run and cost planning · Upstream CLI and API facts · Local output inspection · Evaluation without model calls · Provider privacy gate · References
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 14 KB |
assets/dataset_manifest.example.json | config | 874 B |
assets/result.example.json | config | 448 B |
assets/run_config.example.json | config | 1.2 KB |
assets/task_config.example.yaml | config | 1.3 KB |
references/configuration.md | doc | 5.6 KB |
references/datasets.md | doc | 6.6 KB |
references/evaluation.md | doc | 5.3 KB |
references/security.md | doc | 7.5 KB |
references/sources.md | doc | 8.1 KB |
references/upstream.md | doc | 13 KB |
scripts/__init__.py | script | 64 B |
scripts/_common.py | script | 45 KB |
scripts/audit_dataset.py | script | 13 KB |
scripts/evaluate_local.py | script | 7.7 KB |
scripts/inspect_outputs.py | script | 5.6 KB |
scripts/plan_run.py | script | 8.3 KB |
scripts/validate_config.py | script | 5.7 KB |
External hosts referenced
github.com platform.claude.com developers.openai.com huggingface.co arxiv.org pypi.org api.github.com privacy.claude.com aclanthology.org api.anthropic.com api.openai.com docs.vllm.ai doi.org export.arxiv.org openai.com openreview.net pyyaml.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
ANTHROPIC_API_KEY OPENAI_API_KEY ANTHROPIC_BASE_URL OPENAI_BASE_URL MODEL_COSTS
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-hypogenic-scan.json
- Badge for your README:
- Request a re-scan