Genomas Guide
wentorai/research-plugins
Automate gene expression analysis with the GenoMAS multi-agent system
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
Install MFA Pipeline Orchestrator
$ npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiming-lab/AutoResearchClaw mfa-pipeline-orchestrator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator .claude/skills/mfa-pipeline-orchestrator && rm -rf skills-srcUse ~/.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/
Install the "mfa-pipeline-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator into .claude/skills/mfa-pipeline-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfa-pipeline-orchestrator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestratorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiming-lab/AutoResearchClaw mfa-pipeline-orchestrator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator .agents/skills/mfa-pipeline-orchestrator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mfa-pipeline-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator into .agents/skills/mfa-pipeline-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfa-pipeline-orchestrator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiming-lab/AutoResearchClaw mfa-pipeline-orchestrator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator .cursor/skills/mfa-pipeline-orchestrator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mfa-pipeline-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator into .cursor/skills/mfa-pipeline-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfa-pipeline-orchestrator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aiming-lab/AutoResearchClaw.git --path external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiming-lab/AutoResearchClaw mfa-pipeline-orchestrator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator .gemini/skills/mfa-pipeline-orchestrator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mfa-pipeline-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator into .gemini/skills/mfa-pipeline-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfa-pipeline-orchestrator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aiming-lab/AutoResearchClaw mfa-pipeline-orchestratorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator .github/skills/mfa-pipeline-orchestrator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mfa-pipeline-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator into .github/skills/mfa-pipeline-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfa-pipeline-orchestrator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aiming-lab/AutoResearchClaw mfa-pipeline-orchestrator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator .opencode/skills/mfa-pipeline-orchestrator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mfa-pipeline-orchestrator" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator into .opencode/skills/mfa-pipeline-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfa-pipeline-orchestrator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mfa-pipeline-orchestrator- GitHub stars
- 15k
- Token cost
- ~923 tokens
- SKILL.md length
- 231 words
- Files
- 1
- Skills in repo
- 34
- Repo updated
- First seen
- Licence
- MIT
At a glance
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
- Works in 5 steps: Parse User Request → Invoke model-builder → Invoke fba-runner → …
- Running an end-to-end metabolic model analysis from an organism name or BiGG model ID
- SKILL.md covers Overview, Workflow, Progress File Specification and Key Conventions
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
The orchestrator begins by recording your request in progress/step0_inputs.md: the model source (a BiGG model ID or custom reactions), organism and growth condition, objective reaction, gene knockouts, analysis goals and target product. It then calls four sub-agents in order. The model-builder produces a model JSON and a validation report, the fba-runner runs FBA, pFBA, FVA or knockout screens and writes flux CSV files, the flux-analyzer covers essentiality, phase plane and sampling, and the metabolic-pheno-analyzer reports maximum theoretical yield and publication figures.
Each step writes a markdown file in a progress folder with a PASS or FAIL status and the results the next step reads, so a failed step can be rerun without repeating finished ones. The skill says never to re-run completed steps and caps total sub-agent retries at 10 across the whole pipeline. The excerpt is truncated, so conventions after that point are not described here.
When your agent uses it
- Running an end-to-end metabolic model analysis from an organism name or BiGG model ID
- Screening gene knockouts for essentiality and growth effects
- Estimating the maximum theoretical yield of a target product
- Resuming a multi-step modelling run after one step failed
Example prompts
- “Run the full flux analysis for E. coli under anaerobic growth and give me the essential genes.”
- “Load the BiGG model iML1515, knock out pgi, and compare wild-type and mutant growth.”
- “Step 2 of the metabolic pipeline failed; pick up from the progress files without redoing step 1.”
Requirements
- The model-builder, fba-runner, flux-analyzer and metabolic-pheno-analyzer sub-agents
- A BiGG model ID, organism name or custom reaction list
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit be4ba47. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
MFA Pipeline Orchestrator loads about 923 tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 231 words of instructions outside code blocks.
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 aiming-lab/AutoResearchClaw at commit be4ba47, republished under its MIT licence (© aiming-lab). 231 words, ~923 tokens.
Download SKILL.mdSave it as
.claude/skills/mfa-pipeline-orchestrator/SKILL.md (or your agent's skills folder).- name
- mfa-pipeline-orchestrator
- description
- Orchestrate the full metabolic flux analysis pipeline from model loading to phenotype prediction and publication figures. Triggers when the user provides an organism name, BIGG model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.
- metadata.category
- domain
- metadata.trigger-keywords
- metabolic flux analysis,MFA,FBA,COBRApy,BIGG,metabolic engineering,genome-scale metabolic model,knockout,yield,phenotype prediction
- metadata.applicable-stages
- 8,9,10,11,12,13,14,15,16,17
- metadata.priority
- 1
MFA Pipeline Orchestrator
Overview
Coordinates all mfa-agent sub-agents in sequence, tracking progress via progress/ markdown files so any failed step can be resumed independently.
Full pipeline:
Model source (BIGG ID / custom reactions)
→ [model-builder] models/<Model>.json + validation report
→ [fba-runner] simulations/fba_fluxes.csv + scan_summary.json
→ [flux-analyzer] analysis/essentiality.csv + phase_plane.png
→ [metabolic-pheno-analyzer] output/figures/*.pdf + yield tableWorkflow
Step 0: Parse User Request
Extract and record in progress/step0_inputs.md:
- Model source (BIGG ID or custom)
- Organism and condition (aerobic/anaerobic, carbon source, concentration)
- Objective reaction (biomass or product)
- Gene knockouts to apply
- Analysis goals (essentiality, phase plane, yield optimisation, WT vs. mutant comparison)
- Target product (if yield analysis requested)
Step 1: Invoke model-builder
Provide: model source, medium constraints, objective, knockouts.
Wait for progress/step1_metabolic_model.md.
Read: model file path, WT growth rate, model statistics.
Step 2: Invoke fba-runner
Provide: model path, simulation types requested (FBA, pFBA, FVA, knockout screen), carbon source sweep if requested.
Wait for progress/step2_fba_simulation.md.
Read: flux CSV paths, essential gene count, secretion fluxes.
Step 3: Invoke flux-analyzer
Provide: model path, FBA results, analysis goals (essentiality, phase plane, sampling), nutrient pair for phase plane.
Wait for progress/step3_flux_analysis.md.
Read: essential genes, phase plane optimum, engineering targets.
Step 4: Invoke metabolic-pheno-analyzer
Provide: model path, all previous results, target product, publication requirements.
Wait for progress/step4_metabolic_phenotype.md.
Read: max theoretical yield, figure paths.
Progress File Specification
progress/step1_metabolic_model.md
# Step 1: Metabolic Model
## Status: PASS / FAIL
## Model: <BIGG_ID>.json
## Reactions: N Metabolites: M Genes: G
## WT growth rate: X h⁻¹
## Validation: mass balance errors=0, dead-ends=Nprogress/step2_fba_simulation.md
# Step 2: FBA Simulation
## Status: PASS / FAIL
## Runs: FBA, pFBA, FVA, knockout screen
## WT growth rate: X h⁻¹ (pFBA: Y h⁻¹)
## Essential genes: N
## Key secretion products: [ethanol: X mmol/gDW/h, ...]
## Files: simulations/fba_fluxes.csv, simulations/gene_essentiality.csvprogress/step3_flux_analysis.md
# Step 3: Flux Analysis
## Status: PASS / FAIL
## Essential gene count: N
## Phase plane optimum: glucose=X, O2=Y → growth=Z h⁻¹
## Top engineering targets: [gene1, gene2, gene3]
## Files: analysis/phase_plane.png, analysis/essentiality.csvKey Conventions
- Never re-run completed steps — check progress file status before invoking sub-agents
- Maximum total sub-agent retries: 10 across all steps
- All file paths relative to working directory
- The orchestrator does not run FBA itself — all computation delegated to sub-agents
© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator of aiming-lab/AutoResearchClaw.
Open the folder on GitHubat commit be4ba47
Compare with similar skills
MFA Pipeline Orchestrator 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| MFA Pipeline Orchestrator this skillaiming-lab/AutoResearchClaw | 15k | — | ~923 | Automated safety check: Pass | MIT | |
| Genomas Guidewentorai/research-plugins | 298 | 1 repos | ~958 | Automated safety check: Pass | MIT | |
| ULW Deep Researchcode-yeongyu/oh-my-openagent | 70k | — | ~14k | Automated safety check: Pass | Custom licence | |
| Deep ResearchXiaomiMiMo/MiMo-Code | 14k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Mcpmed Bioinformatics ServerFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 1 repos | ~353 | Automated safety check: Pass | MIT | |
| Spatial TrajectoryTianGzlab/OmicsClaw | 161 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 |
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ULW Deep Research
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Deep Research
XiaomiMiMo/MiMo-Code
Runs a multi-source investigation with parallel sub-agents and built-in web tools, then writes one cited report. Meant for open-ended topics, not quick lookups.
Mcpmed Bioinformatics Server
FreedomIntelligence/OpenClaw-Medical-Skills
Model Context Protocol (MCP) server for bioinformatics web services like GEO, STRING, and UCSC Cell Browser.
Spatial Trajectory
TianGzlab/OmicsClaw
Load when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint…
Kegg Analysis
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Multi-step KEGG bioinformatics workflows — pathway enrichment from gene lists, drug-target investigation, cross-species metabolic comparison, and compound-reaction network exploration.
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Builds or loads a genome-scale metabolic model in COBRApy, sets its growth medium and objective, and exports it as a validated JSON file for flux analysis.
Biopython Bioinformatics
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Quick reference for Biopython work: sequence operations, SeqIO file parsing, BLAST searches, Entrez queries, phylogenetic trees and PDB structure analysis.
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Related
Categories
Questions about MFA Pipeline Orchestrator
What does MFA Pipeline Orchestrator do?
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence. md: the model source (a BiGG model ID or custom reactions), organism and growth condition, objective reaction, gene knockouts, analysis goals and target product. It then calls four sub-agents in order.
When should I use MFA Pipeline Orchestrator?
MFA Pipeline Orchestrator fits situations like: running an end-to-end metabolic model analysis from an organism name or BiGG model ID; screening gene knockouts for essentiality and growth effects; estimating the maximum theoretical yield of a target product; resuming a multi-step modelling run after one step failed.
How do I install MFA Pipeline Orchestrator in Claude Code?
Run `npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a claude-code`. Or copy the skill folder (external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator in aiming-lab/AutoResearchClaw) into .claude/skills/mfa-pipeline-orchestrator in your project. Claude Code loads it when a task matches its description.
How do I install MFA Pipeline Orchestrator in Codex?
Run `npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a codex`. Or copy the skill folder (external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator in aiming-lab/AutoResearchClaw) into .agents/skills/mfa-pipeline-orchestrator in your project. Codex loads it when a task matches its description.
Can I use MFA Pipeline Orchestrator 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 aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mfa-pipeline-orchestrator, .gemini/skills/mfa-pipeline-orchestrator, .github/skills/mfa-pipeline-orchestrator and .opencode/skills/mfa-pipeline-orchestrator in your project.
What does MFA Pipeline Orchestrator need to run?
SKILL.md names no scripts, command-line tools or credentials: MFA Pipeline Orchestrator is instructions for the agent only. Our summary lists: The model-builder, fba-runner, flux-analyzer and metabolic-pheno-analyzer sub-agents; A BiGG model ID, organism name or custom reaction list.
Does MFA Pipeline Orchestrator access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is MFA Pipeline Orchestrator 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 MFA Pipeline Orchestrator use?
MFA Pipeline Orchestrator 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 MFA Pipeline Orchestrator use?
About 923 tokens (SKILL.md is roughly 3.7k 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 MFA Pipeline Orchestrator?
Skills that share tags, products or a category with MFA Pipeline Orchestrator: Genomas Guide (wentorai/research-plugins, 298 stars), ULW Deep Research (code-yeongyu/oh-my-openagent, 70k stars), Deep Research (XiaomiMiMo/MiMo-Code, 14k stars) and Mcpmed Bioinformatics Server (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains MFA Pipeline Orchestrator?
aiming-lab (a GitHub organization) maintains it in aiming-lab/AutoResearchClaw, which has 14,607 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on August 19, 2026.
Source: aiming-lab/AutoResearchClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.