Agent Skillsaiming-lab/AutoResearchClaw › mfa-pipeline-orchestrator

mfa-pipeline-orchestrator

GitHub

编排代谢通量分析全流程,协调模型构建、FBA模拟、通量分析及表型预测等子代理,通过进度文件实现断点续跑与自动化执行。

external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator/SKILL.md aiming-lab/AutoResearchClaw

Trigger Scenarios

用户提供生物体名称或BIGG模型ID请求端到端代谢建模 用户指定自定义反应列表并需要自动运行代谢模拟流程

Install

npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -g -y
More Options

Non-standard path

npx skills add https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator -g -y

Use without installing

npx skills use aiming-lab/AutoResearchClaw@mfa-pipeline-orchestrator

指定 Agent (Claude Code)

npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator -a claude-code -g -y

安装 repo 全部 skill

npx skills add aiming-lab/AutoResearchClaw --all -g -y

预览 repo 内 skill

npx skills add aiming-lab/AutoResearchClaw --list

SKILL.md

Frontmatter
{
    "name": "mfa-pipeline-orchestrator",
    "metadata": {
        "category": "domain",
        "priority": "1",
        "trigger-keywords": "metabolic flux analysis,MFA,FBA,COBRApy,BIGG,metabolic engineering,genome-scale metabolic model,knockout,yield,phenotype prediction",
        "applicable-stages": "8,9,10,11,12,13,14,15,16,17"
    },
    "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."
}

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

# 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

# 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

# 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

Version History

  • e2e23c9 Current 2026-07-25 07:47

Same Skill Collection

.claude/skills/a-evolve/SKILL.md
.claude/skills/biology-biopython/SKILL.md
.claude/skills/chemistry-rdkit/SKILL.md
.claude/skills/hypothesis-formulation/SKILL.md
.claude/skills/literature-search/SKILL.md
.claude/skills/researchclaw/SKILL.md
.claude/skills/scientific-visualization/SKILL.md
.claude/skills/scientific-writing/SKILL.md
.claude/skills/statistical-reporting/SKILL.md
external/agents/Biology-Agent/skills/fba-simulator/SKILL.md
external/agents/Biology-Agent/skills/flux-analyzer/SKILL.md
external/agents/Biology-Agent/skills/gsmm-builder/SKILL.md
external/agents/Biology-Agent/skills/gsmm-validator/SKILL.md
external/agents/Biology-Agent/skills/metabolic-study-planner/SKILL.md
external/agents/stat_research_agent/skills/stat-research-orchestrator/SKILL.md
external/agents/stat_research_agent/skills/stat-result-validator/SKILL.md
external/agents/stat_research_agent/skills/statistical-experimental-evaluation/SKILL.md
external/agents/stat_research_agent/skills/statistical-method-design/SKILL.md
external/agents/stat_research_agent/skills/statistical-problem-formulation/SKILL.md
external/agents/stat_research_agent/skills/statistical-theory-analysis/SKILL.md
researchclaw/skills/builtin/domain/biology-biopython/SKILL.md
researchclaw/skills/builtin/domain/chemistry-rdkit/SKILL.md
researchclaw/skills/builtin/domain/cv-classification/SKILL.md
researchclaw/skills/builtin/domain/cv-detection/SKILL.md
researchclaw/skills/builtin/domain/nlp-alignment/SKILL.md
researchclaw/skills/builtin/domain/nlp-pretraining/SKILL.md
researchclaw/skills/builtin/domain/quantum-qiskit/SKILL.md
researchclaw/skills/builtin/domain/rl-policy-optimization/SKILL.md
researchclaw/skills/builtin/experiment/experimental-design/SKILL.md
researchclaw/skills/builtin/experiment/hypothesis-formulation/SKILL.md
researchclaw/skills/builtin/experiment/literature-search/SKILL.md
researchclaw/skills/builtin/experiment/meta-analysis/SKILL.md
researchclaw/skills/builtin/experiment/scientific-visualization/SKILL.md
researchclaw/skills/builtin/experiment/scientific-writing/SKILL.md
researchclaw/skills/builtin/experiment/statistical-reporting/SKILL.md
researchclaw/skills/builtin/experiment/systematic-review/SKILL.md
researchclaw/skills/builtin/tooling/data-loading/SKILL.md
researchclaw/skills/builtin/tooling/distributed-training/SKILL.md
researchclaw/skills/builtin/tooling/mixed-precision/SKILL.md
researchclaw/skills/builtin/tooling/pytorch-training/SKILL.md

Metadata

Files
0
Version
be4ba47
Hash
b244b759
Indexed
2026-07-25 07:47

inicio - Wiki
Copyright © 2011-2026 iteam. Current version is 2.155.2. UTC+08:00, 2026-08-20 05:20
浙ICP备14020137号-1 $mapa de visitantes$