Menú
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
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 hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.
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.
| name | rl-policy-optimization |
| description | Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design. |
| metadata | {"category":"domain","trigger-keywords":"reinforcement learning,rl,policy,reward,agent,environment,ppo,sac","applicable-stages":"9,10","priority":"3","version":"1.0","author":"researchclaw","references":"Schulman et al., Proximal Policy Optimization, 2017; Haarnoja et al., Soft Actor-Critic, ICML 2018"} |
Algorithm selection:
Training recipe:
Evaluation:
Common pitfalls:
