Agent skills by aiming-lab

Diagnoses where an agent failed across runs and turns the findings into new skills, system prompt patches and knowledge entries, using the A-Evolve loop.

aiming-lab/AutoResearchClaw

Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.

aiming-lab/AutoResearchClaw

Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.

aiming-lab/AutoResearchClaw

Reference patterns for writing qiskit 2.x code for variational quantum machine learning: feature maps, VQC training, VQE for chemistry, MPS circuits and noise models.

aiming-lab/AutoResearchClaw

Use this skill before any data analysis, transformation, or modeling.

aiming-lab/MetaClaw

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.

aiming-lab/AutoResearchClaw

A skill your agent uses when implementing any endpoint, form handler, CLI tool, or function that accepts external input.

aiming-lab/MetaClaw

A skill your agent uses when writing shell scripts, Python automation, or any unattended batch job.

aiming-lab/MetaClaw

Quick reference for Biopython work: sequence operations, SeqIO file parsing, BLAST searches, Entrez queries, phylogenetic trees and PDB structure analysis.

aiming-lab/AutoResearchClaw

A skill your agent uses when building production services, pipelines, or automation that needs to be debugged, monitored, or audited.

aiming-lab/MetaClaw

Reference guide for working with molecules in RDKit: reading SMILES and SDF files, computing descriptors and fingerprints, and searching substructures.

aiming-lab/AutoResearchClaw

Runs flux balance analysis and related constraint-based simulations on a COBRApy metabolic model, from standard FBA to gene knockouts and carbon source swaps.

aiming-lab/AutoResearchClaw

Turns raw flux balance analysis output and a COBRApy model into gene essentiality maps, phenotypic phase planes, flux sampling results, pathway summaries and secretion predictions.

aiming-lab/AutoResearchClaw

A skill your agent uses when delegating a subtask to a sub-agent, spawning a parallel worker, or handing off work across sessions.

aiming-lab/MetaClaw

Runs quality control on a COBRApy genome-scale metabolic model before flux analysis, checking mass and charge balance, biomass feasibility, dead ends, thermodynamic loops and GPR rules.

aiming-lab/AutoResearchClaw

A skill your agent uses when writing messages in async channels (Slack, GitHub issues, email threads) where the reader may not have context and cannot ask follow-up questions immediately.

aiming-lab/MetaClaw

Guides turning an observation into testable, falsifiable hypotheses with null and alternative statements, competing explanations and predictions tied to experimental design.

aiming-lab/AutoResearchClaw

A skill your agent uses when writing any explanation, documentation, or response that will be read by someone else.

aiming-lab/MetaClaw

Lays out a systematic literature review method: PICO-based search strategy, inclusion criteria, PRISMA screening, quality assessment tools and synthesis approaches.

aiming-lab/AutoResearchClaw

A skill your agent uses when implementing authentication (login, token issuance) or authorization (access control, permissions).

aiming-lab/MetaClaw

Runs the ResearchClaw 23-stage autonomous pipeline from a topic, config file and output directory, from literature review through experiments to a written and reviewed paper.

aiming-lab/AutoResearchClaw

Publication-ready scientific figure design with matplotlib and seaborn.

aiming-lab/AutoResearchClaw

Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines.

aiming-lab/AutoResearchClaw

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

aiming-lab/AutoResearchClaw

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim…

aiming-lab/AutoResearchClaw

Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

aiming-lab/AutoResearchClaw

Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.

aiming-lab/AutoResearchClaw

Statistical test selection, assumption checking, and APA-formatted reporting.

aiming-lab/AutoResearchClaw

A skill your agent uses when exploring an unfamiliar codebase, tracing code paths, or answering questions about how the system works.

aiming-lab/MetaClaw

Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.

aiming-lab/AutoResearchClaw

Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.

aiming-lab/AutoResearchClaw

Use this skill in long conversations or multi-turn agentic sessions where context may be lost or the conversation is approaching token limits.

aiming-lab/MetaClaw

Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning.

aiming-lab/AutoResearchClaw

Best practices for language model pretraining and fine-tuning.

aiming-lab/AutoResearchClaw

Best practices for reinforcement learning policy optimization.

aiming-lab/AutoResearchClaw

A skill your agent uses when diagnosing a bug, unexpected behavior, test failure, or any situation where code does not behave as expected.

aiming-lab/MetaClaw

Best practices for object detection tasks. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Optimize data loading pipeline to prevent GPU starvation. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

Multi-GPU and distributed training patterns with PyTorch DDP.

aiming-lab/AutoResearchClaw

Statistical methods for combining results across multiple studies.

aiming-lab/AutoResearchClaw

Use FP16/BF16 mixed precision to accelerate training and reduce memory.

aiming-lab/AutoResearchClaw

Best practices for building robust PyTorch training loops. An agent skill from aiming-lab/AutoResearchClaw.

aiming-lab/AutoResearchClaw

A skill your agent uses when working with git — making commits, creating branches, resolving merge conflicts, opening pull requests, or reviewing diffs.

aiming-lab/MetaClaw

Structured methodology for comprehensive literature review following PRISMA guidelines.

aiming-lab/AutoResearchClaw

A skill your agent uses when writing scripts, cron jobs, data pipelines, or any automated process that may be run multiple times.

aiming-lab/MetaClaw

Use this skill before executing a sequence of 3 or more steps, especially when steps are irreversible or depend on each other.

aiming-lab/MetaClaw