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Add skill marketplace registry and in-loop skill acquisition
Three connected features: 1. characters/index.json — centralized registry with tags field for indexing. Website JS now fetches this dynamically instead of hardcoding the CHARACTERS array. New characters added by PR to this file or via GitHub topic discovery. 2. autolab_acquire_skill MCP tool — searches the published registry and GitHub (topic:autolab-character) for existing characters that have the needed skill. Downloads the SKILL.md directly if found, or returns instructions to create it via the Character Builder. Acquired skills are cached in .autolab/acquired_skills/. 3. PI prompt update — new "Skill Acquisition" section tells the PI to call autolab_acquire_skill when stuck, before spending a full turn struggling with an unfamiliar tool. v0.5.6
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‎docs/characters/index.json‎

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{
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"version": 1,
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"updated": "2026-02-14",
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"characters": [
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{
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"id": "maria-chen",
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"name": "Dr. Maria Chen",
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"role": "pi",
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"avatar": "neuroscientist",
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"title": "Computational Biology PI",
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"expertise": "Single-cell genomics, machine learning, and multi-modal data integration",
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"goal": "Discover cell-type-specific gene regulatory programs using multi-modal single-cell data",
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"skills": ["scanpy", "scvi-tools", "pytorch-lightning", "scientific-writing", "scientific-visualization", "statistical-analysis"],
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"tags": ["biology", "genomics", "single-cell", "machine-learning", "scRNA-seq"],
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"personality": [
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"Visionary: identifies novel biological questions from data patterns",
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"Rigorous: demands reproducible computational pipelines with version control",
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"Collaborative: bridges wet lab and dry lab teams effectively"
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],
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"github": "albert-ying/autolab-char-compbio-pi",
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"stars": 342,
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"official": true
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},
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{
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"id": "alex-kumar",
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"name": "Alex Kumar",
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"role": "trainee",
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"avatar": "bioinformatician",
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"title": "Bioinformatics Postdoc",
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"expertise": "NGS data analysis, pipeline development, and statistical genomics",
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"goal": "Build clean, reproducible analysis pipelines and generate publication-quality figures",
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"skills": ["scanpy", "pydeseq2", "pysam", "matplotlib", "seaborn", "scikit-learn", "deeptools"],
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"tags": ["biology", "bioinformatics", "NGS", "RNA-seq", "pipelines"],
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"personality": [
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"Dedicated: completes tasks thoroughly with comprehensive documentation",
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"Technical: writes self-contained, reproducible code with proper testing",
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"Proactive: identifies additional analyses that strengthen the narrative"
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],
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"github": "albert-ying/autolab-char-bioinfo-trainee",
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"stars": 256,
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"official": true
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},
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{
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"id": "sarah-oconnor",
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"name": "Dr. Sarah O'Connor",
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"role": "pi",
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"avatar": "chemist",
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"title": "Medicinal Chemistry PI",
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"expertise": "Drug discovery, QSAR modeling, and lead optimization",
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"goal": "Identify and optimize novel small molecule inhibitors through computational screening",
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"skills": ["rdkit", "datamol", "deepchem", "pytdc", "medchem", "pubchem-database"],
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"tags": ["chemistry", "drug-discovery", "QSAR", "medicinal-chemistry"],
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"personality": [
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"Strategic: prioritizes compounds with drug-like properties early",
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"Data-driven: demands SAR analysis before advancing any lead series",
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"Publication-savvy: structures work for high-impact medicinal chemistry journals"
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],
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"github": "albert-ying/autolab-char-medchem-pi",
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"stars": 189,
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"official": false
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},
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{
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"id": "james-park",
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"name": "James Park",
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"role": "trainee",
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"avatar": "ml_engineer",
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"title": "ML Research Engineer",
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"expertise": "Deep learning, model training, and inference optimization",
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"goal": "Implement and benchmark state-of-the-art models with clean, efficient code",
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"skills": ["pytorch-lightning", "transformers", "accelerate", "weights-and-biases", "vllm", "flash-attention"],
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"tags": ["machine-learning", "deep-learning", "LLM", "inference", "GPU"],
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"personality": [
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"Efficient: writes highly optimized code with proper GPU utilization",
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"Systematic: benchmarks every change with rigorous ablation studies",
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"Clear communicator: documents architecture decisions and trade-offs"
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],
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"github": "albert-ying/autolab-char-ml-engineer",
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"stars": 198,
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"official": false
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},
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{
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"id": "elena-vasquez",
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"name": "Dr. Elena Vasquez",
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"role": "collaborator",
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"avatar": "epidemiologist",
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"title": "Clinical Epidemiologist",
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"expertise": "Clinical trial design, survival analysis, and real-world evidence",
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"goal": "Ensure robust clinical study designs and proper statistical interpretation",
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"skills": ["scikit-survival", "statistical-analysis", "statsmodels", "clinical-reports", "clinicaltrials-database"],
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"tags": ["clinical", "epidemiology", "survival-analysis", "trial-design"],
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"personality": [
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"Methodical: insists on pre-registered analysis plans",
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"Critical: identifies confounders and biases in study designs",
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"Translational: connects statistical findings to clinical implications"
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],
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"github": "albert-ying/autolab-char-clinical-epi",
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"stars": 145,
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"official": false
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},
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{
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"id": "wei-zhang",
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"name": "Dr. Wei Zhang",
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"role": "collaborator",
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"avatar": "statistician",
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"title": "Biostatistician",
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"expertise": "Bayesian modeling, causal inference, and high-dimensional statistics",
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"goal": "Provide rigorous statistical frameworks and validate analytical approaches",
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"skills": ["pymc", "statistical-analysis", "statsmodels", "scikit-learn", "shap", "scientific-visualization"],
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"tags": ["statistics", "Bayesian", "causal-inference", "biostatistics"],
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"personality": [
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"Precise: never allows hand-waving about statistical assumptions",
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"Educational: explains complex methods in accessible terms",
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"Conservative: prefers well-validated methods over trendy approaches"
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],
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"github": "albert-ying/autolab-char-biostatistician",
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"stars": 267,
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"official": true
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}
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]
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}

‎docs/js/main.js‎

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// ============================================================
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// INITIALIZATION — Render all sprites on load
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// ============================================================
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document.addEventListener("DOMContentLoaded", () => {
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document.addEventListener("DOMContentLoaded", async () => {
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const S = window.LabSprites;
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if (!S) return console.error("LabSprites not loaded");
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// Load characters from registry before rendering marketplace
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await fetchCharacters();
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// Hero sprites
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S.renderPI(document.getElementById("hero-pi-sprite"));
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S.renderTrainee(document.getElementById("hero-trainee-sprite"));
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// Users fork the template, create their character,
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// and list their repo. Ranked by GitHub stars.
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// Official characters from albert-ying get a badge.
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// Now loaded dynamically from characters/index.json
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// ============================================================
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const CHARACTERS = [
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{
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id: "maria-chen",
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name: "Dr. Maria Chen",
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role: "pi",
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avatar: "neuroscientist",
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title: "Computational Biology PI",
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expertise: "Single-cell genomics, machine learning, and multi-modal data integration",
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goal: "Discover cell-type-specific gene regulatory programs using multi-modal single-cell data",
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skills: ["scanpy", "scvi-tools", "pytorch-lightning", "scientific-writing", "scientific-visualization", "statistical-analysis"],
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personality: [
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"Visionary: identifies novel biological questions from data patterns",
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"Rigorous: demands reproducible computational pipelines with version control",
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"Collaborative: bridges wet lab and dry lab teams effectively"
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],
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github: "albert-ying/autolab-char-compbio-pi",
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stars: 342,
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official: true
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},
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{
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id: "alex-kumar",
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name: "Alex Kumar",
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role: "trainee",
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avatar: "bioinformatician",
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title: "Bioinformatics Postdoc",
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expertise: "NGS data analysis, pipeline development, and statistical genomics",
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goal: "Build clean, reproducible analysis pipelines and generate publication-quality figures",
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skills: ["scanpy", "pydeseq2", "pysam", "matplotlib", "seaborn", "scikit-learn", "deeptools"],
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personality: [
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"Dedicated: completes tasks thoroughly with comprehensive documentation",
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"Technical: writes self-contained, reproducible code with proper testing",
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"Proactive: identifies additional analyses that strengthen the narrative"
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],
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github: "albert-ying/autolab-char-bioinfo-trainee",
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stars: 256,
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official: true
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},
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{
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id: "sarah-oconnor",
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name: "Dr. Sarah O'Connor",
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role: "pi",
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avatar: "chemist",
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title: "Medicinal Chemistry PI",
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expertise: "Drug discovery, QSAR modeling, and lead optimization",
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goal: "Identify and optimize novel small molecule inhibitors through computational screening",
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skills: ["rdkit", "datamol", "deepchem", "pytdc", "medchem", "pubchem-database"],
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personality: [
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"Strategic: prioritizes compounds with drug-like properties early",
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"Data-driven: demands SAR analysis before advancing any lead series",
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"Publication-savvy: structures work for high-impact medicinal chemistry journals"
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],
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github: "albert-ying/autolab-char-medchem-pi",
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stars: 189,
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official: false
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},
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{
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id: "james-park",
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name: "James Park",
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role: "trainee",
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avatar: "ml_engineer",
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title: "ML Research Engineer",
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expertise: "Deep learning, model training, and inference optimization",
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goal: "Implement and benchmark state-of-the-art models with clean, efficient code",
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skills: ["pytorch-lightning", "transformers", "accelerate", "weights-and-biases", "vllm", "flash-attention"],
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personality: [
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"Efficient: writes highly optimized code with proper GPU utilization",
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"Systematic: benchmarks every change with rigorous ablation studies",
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"Clear communicator: documents architecture decisions and trade-offs"
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],
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github: "albert-ying/autolab-char-ml-engineer",
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stars: 198,
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official: false
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},
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{
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id: "elena-vasquez",
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name: "Dr. Elena Vasquez",
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role: "collaborator",
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avatar: "epidemiologist",
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title: "Clinical Epidemiologist",
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expertise: "Clinical trial design, survival analysis, and real-world evidence",
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goal: "Ensure robust clinical study designs and proper statistical interpretation",
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skills: ["scikit-survival", "statistical-analysis", "statsmodels", "clinical-reports", "clinicaltrials-database"],
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personality: [
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"Methodical: insists on pre-registered analysis plans",
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"Critical: identifies confounders and biases in study designs",
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"Translational: connects statistical findings to clinical implications"
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],
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github: "albert-ying/autolab-char-clinical-epi",
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stars: 145,
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official: false
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},
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{
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id: "wei-zhang",
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name: "Dr. Wei Zhang",
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role: "collaborator",
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avatar: "statistician",
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title: "Biostatistician",
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expertise: "Bayesian modeling, causal inference, and high-dimensional statistics",
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goal: "Provide rigorous statistical frameworks and validate analytical approaches",
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skills: ["pymc", "statistical-analysis", "statsmodels", "scikit-learn", "shap", "scientific-visualization"],
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personality: [
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"Precise: never allows hand-waving about statistical assumptions",
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"Educational: explains complex methods in accessible terms",
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"Conservative: prefers well-validated methods over trendy approaches"
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],
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github: "albert-ying/autolab-char-biostatistician",
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stars: 267,
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official: true
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},
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];
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let CHARACTERS = []; // populated by fetchCharacters()
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async function fetchCharacters() {
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try {
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const resp = await fetch("characters/index.json");
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const data = await resp.json();
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CHARACTERS = data.characters || [];
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} catch (e) {
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console.warn("Failed to load characters/index.json, using empty list:", e);
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CHARACTERS = [];
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}
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}
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function initMarketplace() {
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const grid = document.getElementById("mp-grid");

‎pyproject.toml‎

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[project]
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name = "autonomous-lab"
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version = "0.5.5"
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version = "0.5.6"
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description = "MCP server that turns any Senior-Junior workflow into an autonomous loop with a human decision maker. Seamless integration with Cursor, Claude Code, Codex, and any MCP client."
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readme = "README.md"
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requires-python = ">=3.11"

‎src/autonomous_lab/__init__.py‎

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Fork of mcp-feedback-enhanced with virtual lab extensions.
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"""
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__version__ = "0.5.5"
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__version__ = "0.5.6"
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__author__ = "Albert Ying"
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from .server import main as run_server

‎src/autonomous_lab/lab/prompts.py‎

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If you don't need expert consultation this turn, skip this section.
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### 8. PROGRESS
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### 8. SKILL ACQUISITION (When Stuck)
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If you or the {junior_label} tried something and failed because a specific technical skill is missing, call `autolab_acquire_skill` with the skill name (e.g., 'scanpy', 'survival-analysis', 'pytorch-lightning').
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This tool:
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1. **Searches the character marketplace** for published characters that already have this skill
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2. **Downloads the SKILL.md** if found — ready to use immediately
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3. **Returns creation instructions** if no match exists — you can train the skill via the Character Builder
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Use this BEFORE spending a full turn struggling with an unfamiliar tool. The marketplace has hundreds of community-contributed skills. Only create from scratch when nothing exists.
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### 9. PROGRESS
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Assess overall project progress from 0-100. This drives the progress bar in the monitoring UI. Consider:
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- 0-10: Project planning / initial research
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- 10-30: Core work running, preliminary results

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