13C Metabolic Flux Inference
Estimates cell metabolic fluxes from carbon-13 isotope tracing data with validated fitting and diagnostics
- Repository
- K-Dense-AI/scientific-agent-skills
- Stars
- 55,780
- Licence
- MIT
- Files
- 15 files
What it does
This skill teaches an AI agent to run 13C metabolic flux analysis, a lab method where cells are fed carbon-13 labeled nutrients and the resulting labeling patterns in metabolites reveal how fast each biochemical pathway runs. Once installed, the agent checks your carbon atom maps and measurement files for consistency, runs a bundled Python solver (built on mfapy and SciPy) to fit reaction fluxes, and reports which fluxes the data actually constrain and which it does not. It is strict about its assumptions: it refuses to invent atom maps or measurement errors and warns when an experiment needs a different (nonstationary) method.
Great for
- Researchers preparing or reviewing a steady-state 13C-MFA experiment who want help structuring inputs and checking feasibility
- Computational biology students learning how isotope labeling constrains metabolic fluxes
- Developers building analysis pipelines around mfapy-style flux models
Skip it if
- Time-course labeling experiments, which need INST-MFA with pool sizes; the skill explicitly says not to average those into this solver
- Raw mass spectrometer peak data or MS/MS transitions without prior validated preprocessing and natural-abundance correction, since the skill requires corrected fractional labeling as input
- Clinical or safety-critical decisions based on the bundled demonstration models, which the skill itself says are not validated organism-specific reconstructions
Try asking
- “Check my 13C tracing input files for atom count and mass balance errors before fitting”
- “Fit fluxes for my glycolysis and TCA model with 12 multistart runs and profile reactions v3 and v7”
- “Explain whether my labeling data can constrain the flux through the pentose phosphate pathway”
- “Summarize the fit results with model and data hashes and residual diagnostics for my lab notebook”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 15 original files, the MIT licence and an attribution note.
13c-metabolic-flux.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip 13c-metabolic-flux.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip 13c-metabolic-flux.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Provide your own reviewed atom maps and corrected labeling data; the bundled models are demonstrations only.
- Ask the agent to rerun with more starts and a different seed before trusting any flux profile, since nonlinear networks can have multiple local optima.
- Install dependencies in a separate virtual environment outside the skill folder, and review the pinned mfapy commit, because installation executes build code.
What's inside
- assets/branch-fluxes.json
- assets/branch-identifiable.json
- assets/branch-model.json
- assets/branch-unresolved.json
- assets/mfapy-license.txt
- assets/requirements.txt
- assets/tca-fluxes.json
- assets/tca-model.json
- assets/tca-reference-mdv.json
- assets/tca-tracer.json
- references/inference.md
- references/input-contract.md
- scripts/_mfa_fit.py
- scripts/_mfa_model.py
- SKILL.md
Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.