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mfa-pipeline-orchestrator

An AI agent skill from aiming-lab/AutoResearchClaw, scored 56 out of 100 by code.

56/ 100C

Code score by group

  • Spec20/20
  • Description7/20
  • Procedure7/15
  • Done criteria0/15
  • Resources10/10
  • Scripts2/8
  • Safety7/7
  • Upkeep3/5

It loses the most points in Done criteria (15), the group that checks whether there is a checklist and a step that verifies the work.

What the author says it does

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.

This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.

Repository
aiming-lab/AutoResearchClaw
Folder
external/agents/Biology-Agent/skills/mfa-pipeline-orchestrator
License
MIT
GitHub stars
14,862
SKILL.md lines
101
Other files
0

Add mfa-pipeline-orchestrator to your agent

npx skills add aiming-lab/AutoResearchClaw --skill mfa-pipeline-orchestrator

The command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored.

More skills from aiming-lab/AutoResearchClaw

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  3. scientific-visualization

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  4. flux-analyzer

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  5. gsmm-validator

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  6. researchclaw

    Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.

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  7. metabolic-study-planner

    Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypo…

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  8. statistical-reporting

    Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.

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All skills in aiming-lab/AutoResearchClaw

Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.