New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Bio Systems Biology Metabolic Reconstruction

by GPTomics in GPTomics/bioSkills

Builds draft genome-scale metabolic models from an annotated genome using CarveMe (top-down carving of a BiGG universal model) or gapseq (bottom-up pathway-evidence reconstruction), then loads and…

MITAuto-check passedResearch & Science

Install Bio Systems Biology Metabolic Reconstruction

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-systems-biology-metabolic-reconstruction -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-systems-biology-metabolic-reconstruction --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/systems-biology/metabolic-reconstruction .claude/skills/bio-systems-biology-metabolic-reconstruction && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-systems-biology-metabolic-reconstruction
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.6k tokens
SKILL.md length
912 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Builds draft genome-scale metabolic models from an annotated genome using CarveMe (top-down carving of a BiGG universal model) or gapseq (bottom-up pathway-evidence reconstruction), then loads and…

  • Creating a model for an organism without one
  • SKILL.md covers Version Compatibility, The governing principle: a…, Decision: CarveMe vs gapseq vs… and CarveMe (top-down), plus 6 more sections
  • Runs Python scripts from its folder; calls pip and git; reaches github.com
  • Choosing between CarveMe and gapseq

What it does

Bio Systems Biology Metabolic Reconstruction is an agent skill from GPTomics/bioSkills. Builds draft genome-scale metabolic models from an annotated genome using CarveMe (top-down carving of a BiGG universal model) or gapseq (bottom-up pathway-evidence reconstruction), then loads and sanity-checks the draft in COBRApy. Use when creating a model for an organism without one, choosing between CarveMe and gapseq, gap-filling to a target medium, understanding why a draft that grows is still only a hypothesis, handling BiGG-vs-ModelSEED namespace mismatch, or preparing a draft for curation and community…

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/model_reconstruction.py` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Creating a model for an organism without one
  • Choosing between CarveMe and gapseq
  • Gap-filling to a target medium
  • Understanding why a draft that grows is still only a hypothesis

Example prompts

  • “Use the bio-systems-biology-metabolic-reconstruction skill to build draft genome-scale metabolic models from an annotated genome using CarveMe…”
  • “/bio-systems-biology-metabolic-reconstruction”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Systems Biology Metabolic Reconstruction loads about 2.6k tokens when it runs. Until then it costs about 143 tokens; SKILL.md has 912 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~143
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 912 words, ~2,613 tokens.

Download SKILL.mdSave it as .claude/skills/bio-systems-biology-metabolic-reconstruction/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-systems-biology-metabolic-reconstruction
description
Builds draft genome-scale metabolic models from an annotated genome using CarveMe (top-down carving of a BiGG universal model) or gapseq (bottom-up pathway-evidence reconstruction), then loads and sanity-checks the draft in COBRApy. Use when creating a model for an organism without one, choosing between CarveMe and gapseq, gap-filling to a target medium, understanding why a draft that grows is still only a hypothesis, handling BiGG-vs-ModelSEED namespace mismatch, or preparing a draft for curation and community modeling.
tool_type
cli
primary_tool
CarveMe

Version Compatibility

Reference examples tested with: CarveMe 1.6+, gapseq 1.2+, COBRApy 0.29+, DIAMOND 2.1+, Python 3.10+

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures
  • CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Note: CarveMe needs an LP solver (academic CPLEX/Gurobi; SCIP is a slow open-source fallback) and a DIAMOND install, and its universal model is BiGG-derived so the BiGG-model release matters. gapseq is cloned from GitHub (not pip-installable), emits ModelSEED-namespace models, and its reference DB version matters. Model predictions are only comparable within the same tool, DB, and namespace.

Metabolic Reconstruction

"Build a metabolic model from my organism's genome" -> Map the annotated proteome/genome to reactions in a reference database, assemble a draft network with a biomass reaction, and gap-fill so it can grow on a chosen medium.

  • CLI: carve genome.faa -o model.xml (CarveMe, top-down); gapseq doall genome.fna (gapseq, bottom-up)

The governing principle: a draft is a hypothesis, and "it grows" is guaranteed by construction

Automated reconstruction produces a DRAFT, not a finished model. The single most misleading signal is growth: CarveMe and gapseq GAP-FILL specifically to force biomass production on a chosen medium, so a draft that grows proves nothing biological - it was made to grow. Consequences:

  • Gap-filled reactions are the least-evidenced part of the model (added to close a hole, not because homology supports them), and the gap-fill medium determines what gets added. Gap-filling on the wrong medium bakes in the wrong reactions. Flag gap-filled reactions as low-confidence and record the medium.
  • Draft quality is bounded by the annotation and the reference database. CarveMe can only ever include reactions in the BiGG universe (biased toward well-studied organisms); gapseq's homology thresholds and pathway logic set its floor. Peripheral/novel metabolism is systematically underrepresented.
  • The draft is the START of curation, not the end. Different tools produce markedly different models from the same genome, and successive models of the same organism (iJR904 -> iJO1366 -> iML1515) give different predictions. Reconstruction feeds model-curation, never bypasses it.

Decision: CarveMe vs gapseq vs ModelSEED

GoalToolWhy / trade-off
Fast draft(s) for well-studied bacteria; batch/communityCarveMe (carve)top-down MILP carving of a curated BiGG universe; minutes; universe is simulation-ready but BiGG-centric; universal biomass; weak transporters
Non-model/environmental clade; carbon-source & fermentation phenotypesgapseqbottom-up homology + pathway-completeness; slower, more transparent; better SCFA/carbon-use recovery; ModelSEED namespace complicates merging
Fully-automated web pipeline (RAST annotation)ModelSEED/KBasetemplate-based; convenient; template biomass and aggressive gap-fill can force implausible reactions
Eukaryotes / fungi / actinomycetesRAVEN (MATLAB)KEGG/MetaCyc-based, template or de novo; MATLAB license; the eukaryote-capable option

Do NOT treat "CarveMe and gapseq do the same thing, pick the faster one" as true: different philosophies, namespaces (BiGG vs ModelSEED), and failure modes. The choice is scientific. No single tool dominates - which is why consensus/ensemble reconstruction exists.

CarveMe (top-down)

bash
pip install carveme          # also needs DIAMOND and an LP solver (CPLEX/Gurobi; SCIP fallback)

# Draft from a PROTEIN FASTA (default input). Raw/GenBank genomes are NOT accepted.
carve genome.faa -o model.xml

# Gram type and universe are VALUES of -u/--universe, NOT --grampos/--gramneg flags.
carve genome.faa -o model.xml -u grampos    # {bacteria (default), grampos, gramneg, archaea, cyanobacteria}

# Gap-fill to force growth on a medium (opt-in; records what was added for that medium).
carve genome.faa -o model.xml --gapfill M9
carve genome.faa -o model.xml -u gramneg --gapfill M9,LB   # multiple media

# Nucleotide input instead of protein, or download by accession:
carve genome.fna --dna -o model.xml

Community reconstruction uses a SEPARATE merge_community command (not carve); see systems-biology/community-metabolic-modeling.

gapseq (bottom-up, pathway-evidence)

bash
git clone https://github.com/jotech/gapseq && cd gapseq && ./gapseq test   # cloned, not pip; check deps

# One-shot: find + find-transport + draft + fill
./gapseq doall genome.fna

# Or the explicit steps (note find-transport is its OWN subcommand, not `find -t`):
./gapseq find -p all genome.fna          # -> genome-all-Reactions.tbl, genome-all-Pathways.tbl
./gapseq find-transport genome.fna       # -> genome-Transporter.tbl  (singular)
./gapseq draft -r genome-all-Reactions.tbl -t genome-Transporter.tbl \
               -p genome-all-Pathways.tbl -c genome.fna   # -> genome-draft.RDS, genome-rxnWeights.RDS
./gapseq fill -m genome-draft.RDS -n dat/media/M9.csv \
              -c genome-rxnWeights.RDS -g genome-rxnXgenes.RDS   # -> genome.xml / genome.RDS

Load and Sanity-Check the Draft

Goal: Read the draft, confirm it grows on the gap-fill medium, and inventory the parts most likely to be wrong.

Approach: Load the SBML into COBRApy, report network size and gene coverage, test growth, and count orphan (gene-less) reactions and exchanges - the draft's soft spots before curation.

python
import cobra

model = cobra.io.read_sbml_model('model.xml')
print(f'reactions={len(model.reactions)} metabolites={len(model.metabolites)} genes={len(model.genes)}')
print(f'grows on gap-fill medium: {model.slim_optimize() > 1e-3}')   # true by construction if gap-filled
orphans = [r for r in model.reactions if not r.genes]   # no GPR: gap-filled, spontaneous, or transport
print(f'orphan (gene-less) reactions: {len(orphans)}  exchanges: {len(model.exchanges)}')
# Typical bacterial draft: ~1000-2500 reactions. Far outside that range flags an annotation problem.
Show full SKILL.md (361 more words)Show less

Namespaces (the silent killer of model comparison)

python
# Reaction/metabolite IDs come from the tool's reference DB: CarveMe = BiGG, gapseq/ModelSEED =
# ModelSEED (seed.*), RAVEN = KEGG/MetaCyc. Two models in different namespaces cannot be merged or
# compared directly. Reconcile through MetaNetX/MNXref (MNXM* metabolites, MNXR* reactions) BEFORE
# any cross-tool merge or community build. This BiGG-vs-ModelSEED split is exactly why community
# modeling of CarveMe + gapseq outputs breaks without reconciliation.

Common Errors

SymptomCauseFix
carve errors on a genome fileGenBank/nucleotide passed where protein FASTA expectedsupply a protein FASTA, or add --dna for nucleotide
--grampos/--gramneg not recognizedthose are -u/--universe VALUES, not flagscarve ... -u grampos
Draft cannot grow at allno gap-filling requested, or wrong mediumadd --gapfill <medium>; confirm the medium supplies biomass precursors
Draft grows on everything / implausiblygap-fill forced reactions for the chosen mediumflag gap-filled reactions low-confidence; re-gap-fill on the correct medium; curate
Two models will not merge / IDs mismatchdifferent namespaces (BiGG vs ModelSEED)reconcile via MetaNetX/MNXref before merging
gapseq find -t failstransport is the find-transport subcommanduse ./gapseq find-transport genome.fna
Very few genes / tiny networkpoor annotation or wrong input filecheck the proteome/annotation; verify gene IDs
  • systems-biology/model-curation - Curate, gap-fill deliberately, and validate the draft (the required next step)
  • systems-biology/flux-balance-analysis - Predict growth/flux once the model is trustworthy
  • systems-biology/community-metabolic-modeling - Combine reconstructions into a community model
  • genome-annotation/prokaryotic-annotation - Produce the annotated protein FASTA CarveMe/gapseq consume
  • database-access/ncbi-datasets-cli - Fetch genome/proteome inputs

References

  • Machado D, Andrejev S, Tramontano M, Patil KR. 2018. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 46(15):7542-7553. (CarveMe)
  • Zimmermann J, Kaleta C, Waschina S. 2021. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 22(1):81.
  • Henry CS, DeJongh M, Best AA, et al. 2010. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28(9):977-982. (ModelSEED)
  • Wang H, Marcisauskas S, Sanchez BJ, et al. 2018. RAVEN 2.0: a versatile toolbox for metabolic network reconstruction. PLoS Comput Biol 14(10):e1006541.
  • Thiele I, Palsson BO. 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5(1):93-121.
  • Mendoza SN, Olivier BG, Molenaar D, Teusink B. 2019. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol 20(1):158.
  • Moretti S, Tran VDT, Mehl F, et al. 2021. MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions. Nucleic Acids Res 49(D1):D570-D574.
  • Feist AM, Palsson BO. 2010. The biomass objective function. Curr Opin Microbiol 13(3):344-349.
  • Monk JM, Lloyd CJ, Brunk E, et al. 2017. iML1515, a knowledgebase that computes Escherichia coli traits. Nat Biotechnol 35(10):904-908.

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in systems-biology/metabolic-reconstruction of GPTomics/bioSkills.

  • SKILL.md
  • examples/model_reconstruction.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Bio Systems Biology Metabolic Reconstruction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Bio Systems Biology Metabolic Reconstruction compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bio Systems Biology Metabolic Reconstruction this skillGPTomics/bioSkills1.2k1 repos~2.6kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

Similar skills

  • Alphagenome Single Variant Analysis

    google-deepmind/science-skills

    Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.

    3.2k GitHub starsUsed in 2 repos~3k tokens
    Research & ScienceAuto-check: notes

  • 13C Metabolic Flux Analysis

    K-Dense-AI/scientific-agent-skills

    Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.

    48k GitHub starsUsed in 1 repo~3.2k tokens
    Research & ScienceAuto-check passed

  • Clinvar Database

    google-deepmind/science-skills

    A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…

    3.2k GitHub starsUsed in 2 repos~3.9k tokens
    Research & ScienceAuto-check: notes

  • Metabolic Study Planner

    aiming-lab/AutoResearchClaw

    Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.

    15k GitHub stars~1.9k tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed

  • Dbsnp Database

    google-deepmind/science-skills

    A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.

    3.2k GitHub starsUsed in 2 repos~3.4k tokens
    Research & ScienceAuto-check: notes

  • MFA Pipeline Orchestrator

    aiming-lab/AutoResearchClaw

    Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.

    15k GitHub stars~923 tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed

More from GPTomics/bioSkills

All 559 skills in this repo

  • Bio Alignment Io

    GPTomics/bioSkills

    Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.

    1.2k GitHub starsUsed in 3 repos~4.9k tokens
    Auto-check passed

  • bioSkills Installer

    GPTomics/bioSkills

    Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.

    1.2k GitHub starsUsed in 1 repo~789 tokens
    Auto-check passed

  • Bio Write Sequences

    GPTomics/bioSkills

    Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.

    1.2k GitHub starsUsed in 3 repos~2.1k tokens
    Auto-check passed

  • Amplicon Primer Clipping

    GPTomics/bioSkills

    Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.

    1.2k GitHub starsUsed in 2 repos~2.2k tokens
    Auto-check passed

  • Alignment Filtering with samtools and pysam

    GPTomics/bioSkills

    Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.

    1.2k GitHub starsUsed in 2 repos~3.6k tokens
    Auto-check passed

  • Bio Alignment Indexing

    GPTomics/bioSkills

    Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.

    1.2k GitHub starsUsed in 2 repos~2.4k tokens
    Auto-check passed

Categories

Questions about Bio Systems Biology Metabolic Reconstruction

What does Bio Systems Biology Metabolic Reconstruction do?

Builds draft genome-scale metabolic models from an annotated genome using CarveMe (top-down carving of a BiGG universal model) or gapseq (bottom-up pathway-evidence reconstruction), then loads and…. Bio Systems Biology Metabolic Reconstruction is an agent skill from GPTomics/bioSkills. Builds draft genome-scale metabolic models from an annotated genome using CarveMe (top-down carving of a BiGG universal model) or gapseq (bottom-up pathway-evidence reconstruction), then loads and sanity-checks the draft in COBRApy.

When should I use Bio Systems Biology Metabolic Reconstruction?

Bio Systems Biology Metabolic Reconstruction fits situations like: creating a model for an organism without one; choosing between CarveMe and gapseq; gap-filling to a target medium; understanding why a draft that grows is still only a hypothesis.

How do I install Bio Systems Biology Metabolic Reconstruction in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-metabolic-reconstruction -a claude-code`. Or copy the skill folder (systems-biology/metabolic-reconstruction in GPTomics/bioSkills) into .claude/skills/bio-systems-biology-metabolic-reconstruction in your project. Claude Code loads it when a task matches its description.

How do I install Bio Systems Biology Metabolic Reconstruction in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-metabolic-reconstruction -a codex`. Or copy the skill folder (systems-biology/metabolic-reconstruction in GPTomics/bioSkills) into .agents/skills/bio-systems-biology-metabolic-reconstruction in your project. Codex loads it when a task matches its description.

Can I use Bio Systems Biology Metabolic Reconstruction in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-metabolic-reconstruction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-systems-biology-metabolic-reconstruction, .gemini/skills/bio-systems-biology-metabolic-reconstruction, .github/skills/bio-systems-biology-metabolic-reconstruction and .opencode/skills/bio-systems-biology-metabolic-reconstruction in your project.

What does Bio Systems Biology Metabolic Reconstruction need to run?

Going by SKILL.md and its folder, Bio Systems Biology Metabolic Reconstruction needs Python for the scripts in its folder and the command-line tools its instructions call (pip and git). Our summary lists: Python 3.

Does Bio Systems Biology Metabolic Reconstruction access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Bio Systems Biology Metabolic Reconstruction safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Systems Biology Metabolic Reconstruction use?

Bio Systems Biology Metabolic Reconstruction is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Systems Biology Metabolic Reconstruction use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Systems Biology Metabolic Reconstruction?

Skills that share tags, products or a category with Bio Systems Biology Metabolic Reconstruction: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Systems Biology Metabolic Reconstruction?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.