13c-metabolic-flux
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
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
Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.
- Package
- 16 files, 81 KB
- SKILL.md
- 1432 words, 10 sections
- Code
- Python (3)
- License
- MIT
- Compatibility
- Python 3.12 with uv and Git for installation. Tested with mfapy 0.6.3 at a10433af16682386548b360297e2476152d46ede, NumPy 2.5.3, SciPy 1.18.1, and NLopt 2.11.0. Network access is needed only to install public dependencies. Inference runs locally without credentials; inputs are JSON.
SKILL.md covers: Carbon-13 metabolic flux inference · Scope and required evidence · Install the tested engine · Workflow · Worked examples · Recover a pathway split; then remove the informative measurement · Reproduce a published cyclic-network calculation · Interpretation boundaries · Implementation and validation · Sources
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 12 KB |
assets/branch-fluxes.json | config | 69 B |
assets/branch-identifiable.json | config | 823 B |
assets/branch-model.json | config | 2.1 KB |
assets/branch-unresolved.json | config | 978 B |
assets/mfapy-license.txt | doc | 1.0 KB |
assets/requirements.txt | doc | 136 B |
assets/tca-fluxes.json | config | 114 B |
assets/tca-model.json | config | 4.1 KB |
assets/tca-reference-mdv.json | config | 2.4 KB |
assets/tca-tracer.json | config | 570 B |
references/inference.md | doc | 9.9 KB |
references/input-contract.md | doc | 8.0 KB |
scripts/_mfa_fit.py | script | 13 KB |
scripts/_mfa_model.py | script | 20 KB |
scripts/mfa.py | script | 4.3 KB |
External hosts referenced
github.com docs.scipy.org doi.org fumiomatsuda.github.io
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
SKILL_DIR
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-13c-metabolic-flux-scan.json
- Badge for your README:
- Request a re-scan