Repository
aiming-lab/AutoResearchClaw agent skills
- skills
- 34
- GitHub stars
- 15k
GitHub description: “Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞”
- Stars
- 14,587 (1,699 forks)
- Licence
- MIT
- Last push
- Aug 2026
- Created
- Mar 2026
- autonomous-research
- citation-verification
- llm-agents
- multi-agent-debate
- openclaw
- paper-generation
- scientific-discovery
- self-evolving
- metaclaw
Install all skills
npx skills add aiming-lab/AutoResearchClawAdd --skill <name> for a single skill and -a <agent> to choose the agent (see the agent guides).
Skills in aiming-lab/AutoResearchClaw, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | 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/ | 15k | — | ~1.8k | Automated safety check: Pass | MIT | 1 mo ago |
| 2 | 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/ | 15k | — | ~1.9k | Automated safety check: Pass | MIT | 1 mo ago |
| 3 | Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence. | aiming-lab/ | 15k | — | ~923 | Automated safety check: Pass | MIT | 1 mo ago |
| 4 | 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/ | 15k | — | ~4.7k | Automated safety check: Pass | MIT | 1 mo ago |
| 5 | 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/ | 15k | — | ~1.4k | Automated safety check: Pass | MIT | 1 mo ago |
| 6 | Quick reference for Biopython work: sequence operations, SeqIO file parsing, BLAST searches, Entrez queries, phylogenetic trees and PDB structure analysis. | aiming-lab/ | 15k | — | ~810 | Automated safety check: Pass | MIT | 1 mo ago |
| 7 | Reference guide for working with molecules in RDKit: reading SMILES and SDF files, computing descriptors and fingerprints, and searching substructures. | aiming-lab/ | 15k | — | ~708 | Automated safety check: Pass | MIT | 1 mo ago |
| 8 | 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/ | 15k | — | ~2.1k | Automated safety check: Pass | MIT | 1 mo ago |
| 9 | 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/ | 15k | — | ~2.3k | Automated safety check: Pass | MIT | 1 mo ago |
| 10 | 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/ | 15k | — | ~2k | Automated safety check: Pass | MIT | 1 mo ago |
| 11 | Guides turning an observation into testable, falsifiable hypotheses with null and alternative statements, competing explanations and predictions tied to experimental design. | aiming-lab/ | 15k | — | ~628 | Automated safety check: Pass | MIT | 1 mo ago |
| 12 | Lays out a systematic literature review method: PICO-based search strategy, inclusion criteria, PRISMA screening, quality assessment tools and synthesis approaches. | aiming-lab/ | 15k | — | ~709 | Automated safety check: Pass | MIT | 1 mo ago |
| 13 | 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/ | 15k | — | ~1k | Automated safety check: Pass | MIT | 1 mo ago |
| 14 | Publication-ready scientific figure design with matplotlib and seaborn. | aiming-lab/ | 15k | — | ~709 | Automated safety check: Pass | MIT | 1 mo ago |
| 15 | Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. | aiming-lab/ | 15k | — | ~703 | Automated safety check: Pass | MIT | 1 mo ago |
| 16 | Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis. | aiming-lab/ | 15k | — | ~1.5k | Automated safety check: Pass | MIT | 1 mo ago |
| 17 | Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim… | aiming-lab/ | 15k | — | ~951 | Automated safety check: Pass | MIT | 1 mo ago |
| 18 | Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations. | aiming-lab/ | 15k | — | ~553 | Automated safety check: Pass | MIT | 1 mo ago |
| 19 | Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets. | aiming-lab/ | 15k | — | ~671 | Automated safety check: Pass | MIT | 1 mo ago |
| 20 | Statistical test selection, assumption checking, and APA-formatted reporting. | aiming-lab/ | 15k | — | ~756 | Automated safety check: Pass | MIT | 1 mo ago |
| 21 | Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation. | aiming-lab/ | 15k | — | ~330 | Automated safety check: Pass | MIT | 1 mo ago |
| 22 | Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations. | aiming-lab/ | 15k | — | ~375 | Automated safety check: Pass | MIT | 1 mo ago |
| 23 | Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~304 | Automated safety check: Pass | MIT | 1 mo ago |
| 24 | Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~286 | Automated safety check: Pass | MIT | 1 mo ago |
| 25 | Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. | aiming-lab/ | 15k | — | ~300 | Automated safety check: Pass | MIT | 1 mo ago |
| 26 | Best practices for language model pretraining and fine-tuning. | aiming-lab/ | 15k | — | ~280 | Automated safety check: Pass | MIT | 1 mo ago |
| 27 | Best practices for reinforcement learning policy optimization. | aiming-lab/ | 15k | — | ~329 | Automated safety check: Pass | MIT | 1 mo ago |
| 28 | 28.Cv Detection Best practices for object detection tasks. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~257 | Automated safety check: Pass | MIT | 1 mo ago |
| 29 | 29.Data Loading Optimize data loading pipeline to prevent GPU starvation. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~210 | Automated safety check: Pass | MIT | 1 mo ago |
| 30 | Multi-GPU and distributed training patterns with PyTorch DDP. | aiming-lab/ | 15k | — | ~216 | Automated safety check: Pass | MIT | 1 mo ago |
| 31 | Statistical methods for combining results across multiple studies. | aiming-lab/ | 15k | — | ~230 | Automated safety check: Pass | MIT | 1 mo ago |
| 32 | Use FP16/BF16 mixed precision to accelerate training and reduce memory. | aiming-lab/ | 15k | — | ~275 | Automated safety check: Pass | MIT | 1 mo ago |
| 33 | Best practices for building robust PyTorch training loops. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~391 | Automated safety check: Pass | MIT | 1 mo ago |
| 34 | Structured methodology for comprehensive literature review following PRISMA guidelines. | aiming-lab/ | 15k | — | ~246 | Automated safety check: Pass | MIT | 1 mo ago |
Questions, answered from the data.
What is the best skill in aiming-lab/AutoResearchClaw?
A-Evolve Agent Improvement from aiming-lab/AutoResearchClaw ranks first of the 34 skills in aiming-lab/AutoResearchClaw listed here, with the highest score: its repository has 15k GitHub stars, its SKILL.md loads about 1.8k tokens and it passes the automated safety check with no findings. Next come Metabolic Study Planner and MFA Pipeline Orchestrator.
Are the skills in aiming-lab/AutoResearchClaw official?
None yet. All 34 skills in aiming-lab/AutoResearchClaw listed here come from community repositories; a skill counts as official when the product's own GitHub organization publishes it.
How do I install all skills from aiming-lab/AutoResearchClaw?
Run npx skills add aiming-lab/AutoResearchClaw in your project: the open-source skills CLI installs the repository's skills into your coding agent's skills folder. To install a single skill, open its page here for the exact command.
How are these skills ranked?
By Skill Navigator score, which combines the GitHub stars of the skill's repository (shared across that repo's skills and discounted for large collections), how many other GitHub owners carry a copy of the skill, and automated SKILL.md quality checks, minus penalties for safety-check warnings and for each further skill from the same repository. Skills that fail the safety check are not listed.