Mfa Pipeline Orchestrator logo

Mfa Pipeline Orchestrator

OrganizationPopular
aiming-lab
mfa-pipeline-orchestrator

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.

Overview

Publisheraiming-lab
RepositoryAutoResearchClaw
Skill namemfa-pipeline-orchestrator
Stars
14.4K
Forks
1.7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aiming-lab on GitHub. Read the source before you install it.

Installation

Install the Mfa Pipeline Orchestrator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git /tmp/AutoResearchClaw
mkdir -p .claude/skills
cp -r /tmp/AutoResearchClaw/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator .claude/skills/mfa-pipeline-orchestrator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mfa Pipeline Orchestrator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mfa Pipeline Orchestrator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mfa Pipeline Orchestrator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 table

Workflow

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

markdown
# 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=N

progress/step2_fba_simulation.md

markdown
# 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.csv

progress/step3_flux_analysis.md

markdown
# 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.csv

Key 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

Frequently asked questions

What does the Mfa Pipeline Orchestrator AI skill do?

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.

Why use Mfa Pipeline Orchestrator on TypingMind?

Because you install it once and use it with any model. Mfa Pipeline Orchestrator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mfa Pipeline Orchestrator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mfa Pipeline Orchestrator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mfa Pipeline Orchestrator?

As many as you like. As long as a model supports skills, you can use Mfa Pipeline Orchestrator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mfa Pipeline Orchestrator AI skill free?

Yes. It is published on GitHub by aiming-lab under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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