Research Agent Skills

13c-metabolic-flux

skills/13c-metabolic-flux/SKILL.md

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

abstract-and-title

skills/abstract-and-title/SKILL.md

Write and sharpen research paper titles, abstracts (structured and unstructured), keywords, highlights, significance statements, graphical-abstract text and lay summaries. Use when drafting or revising an abstract to a word limit, turning results into a one-sentence contribution, choosing between candidate titles, writing Cell/Elsevier highlights, a PNAS significance statement or a plain-language summary, or optimizing discoverability (keywords, search terms).

academic-plotting

skills/academic-plotting/SKILL.md

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

acm-sigconf

skills/acm-sigconf/SKILL.md

Format ACM conference papers and journal articles with the acmart LaTeX class (sigconf, sigplan, acmsmall, acmlarge, acmtog, manuscript/review/anonymous modes) or the ACM Word template, covering CCS concepts, keywords, ACM Reference Format, rights management and copyright blocks, anonymization for double-blind review, accessibility (alt text), the TAPS production workflow and common acmart errors. Use when writing for CHI, SIGGRAPH, KDD, SIGMOD, CCS, FAccT, WWW, SIGIR, CSCW, PLDI or any ACM venue or journal.

adaptyv

skills/adaptyv/SKILL.md

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

aeon

skills/aeon/SKILL.md

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

skills/alphagenome/SKILL.md

Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

analytical-method-validation

skills/analytical-method-validation/SKILL.md

Plans and evaluates analytical method validation, verification, and transfer using Python scripts (plan_validation, check_response, check_accuracy_precision, check_detection_limits, check_bioanalytical_run, compare_methods) under ICH Q2(R2)/Q14, ICH M10, USP 1220/1225/1226, CLSI EP, or ISO/IEC 17025. Covers protocols, linearity, accuracy and precision, DL/QL, M10 run checks, TOST equivalence, Deming and Passing-Bablok. Use when designing a validation protocol with pre-stated acceptance criteria. Use when evaluating linearity, precision, or detection limit data. Use when checking an ICH M10 bioanalytical run or ISR. Use when comparing or transferring a method between labs. Does not decide a method is validated; USP, CLSI, ISO text is not supplied.

anndata

skills/anndata/SKILL.md

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

apa7

skills/apa7/SKILL.md

Format papers, theses and references in APA Style 7th edition for psychology, education, social sciences, nursing and business, covering student vs professional papers, title page, abstract and keywords, heading levels, in-text citations, reference list entries (journal articles, books, chapters, datasets, software, webpages, AI tools), tables and figures, bias-free language, and APA via LaTeX (apa7 class + biblatex-apa), Word or Pandoc/CSL. Use when a user needs APA format, APA citations or references, or an APA-compliant manuscript or thesis.

ara-compiler

skills/ara-compiler/SKILL.md

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

ara-research-manager

skills/ara-research-manager/SKILL.md

Records research provenance at the end of a coding or research session by scanning the conversation and writing decisions, experiments, dead ends, pivots, claims, and heuristics into the ara/ directory (exploration_tree.yaml, claims.md, heuristics.md, observations.yaml, session records), each tagged user, ai-suggested, ai-executed, or user-revised. Use when a task is finished and you want a session epilogue. Use when you need an auditable trace of how a project evolved. Use when decisions and dead ends need logging with provenance. Use when initializing an ara/ directory. Do not use during task execution, since it must run only after the request is complete.

ara-rigor-reviewer

skills/ara-rigor-reviewer/SKILL.md

Performs ARA Seal Level 2 semantic epistemic review of an Agent-Native Research Artifact directory, reading PAPER.md, logic/claims.md, logic/experiments.md, and trace/exploration_tree.yaml. Scores six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) from 1 to 5 and writes a severity-ranked level2_report.json with a Strong Accept to Reject grade. Use when Level 1 structural validation has passed and an ARA needs a critique before release. Use when checking whether claims are supported by their cited experiments. Use when auditing falsification criteria or scope over-claiming. Use when judging whether an exploration tree documents real dead ends. Not for structural or reference validation, which is Level 1.

arbor

skills/arbor/SKILL.md

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

arboreto

skills/arboreto/SKILL.md

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

arxiv-submission

skills/arxiv-submission/SKILL.md

Prepare and post preprints to arXiv without processing failures or leaks, covering TeX source packaging (.bbl, figures, case-sensitive paths), stripping private comments, choosing categories and license, endorsement, metadata (title, abstract, comments field), versioning and replacements, and linking the journal DOI later. Use when posting a paper to arXiv, when arXiv's TeX compilation fails, before uploading source, or when updating a preprint after acceptance. Includes a zero-dependency preflight checker.

astropy

skills/astropy/SKILL.md

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

audiocraft-audio-generation

skills/audiocraft-audio-generation/SKILL.md

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

autogpt-agents

skills/autogpt-agents/SKILL.md

Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

autoresearch

skills/autoresearch/SKILL.md

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

autoskill

skills/autoskill/SKILL.md

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing research-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

awq-quantization

skills/awq-quantization/SKILL.md

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

axolotl

skills/axolotl/SKILL.md

Provides guidance for fine-tuning large language models with Axolotl, covering YAML training configs, LoRA and QLoRA, preference training with DPO, KTO, ORPO and GRPO, multimodal models, FSDP and multi-GPU setups, sequence (context) parallelism, NCCL bandwidth tests, dataset formats, compressed model saving for vLLM and llmcompressor, and custom integrations. Use when writing or debugging an Axolotl YAML config, choosing a dataset format for a fine-tuning run, setting up FSDP or context_parallel_size across GPUs, or running DPO, KTO, ORPO or GRPO training. Use when saving a compressed model for vLLM inference, or when writing a custom Axolotl plugin or integration. Not for inference serving or general Hugging Face Trainer scripts outside Axolotl.

benchling-integration

skills/benchling-integration/SKILL.md

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

bgpt-paper-search

skills/bgpt-paper-search/SKILL.md

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

bibtex-hygiene

skills/bibtex-hygiene/SKILL.md

Clean, deduplicate and validate BibTeX/BibLaTeX bibliographies before submission. Use when a .bib file has duplicate keys or works, missing fields, broken DOIs, lost acronym capitalization (e.g. "bert" instead of "BERT"), inconsistent page dashes, arXiv preprints that now have published versions, or when LaTeX/biber emits bibliography warnings. Includes a zero-dependency linter and normalizer.

bids

skills/bids/SKILL.md

Organizes, queries, validates, and converts neuroscience and biomedical datasets using the Brain Imaging Data Structure (BIDS) standard, covering MRI, PET, EEG, MEG, iEEG, EMG, NIRS, microscopy, motion capture, and behavioral data. Covers PyBIDS (BIDSLayout), the bids-validator, HeuDiConv, dcm2bids, BIDScoin, JSON sidecars, dataset_description.json, events.tsv, participants.tsv, .bidsignore, and derivatives. Use when arranging raw data into BIDS directories and filenames. Use when converting DICOM scans to BIDS. Use when validating a dataset before OpenNeuro or DANDI submission. Use when querying files or sidecar metadata with PyBIDS. Use when writing BIDS derivatives. For extracellular Neuropixels analysis, use neuropixels-analysis instead.

biopython

skills/biopython/SKILL.md

Provides Biopython (Bio.Seq, Bio.SeqIO, Bio.Align, Bio.Entrez, Bio.Blast, Bio.PDB, Bio.Phylo, Bio.motifs, Bio.SeqUtils, Bio.Restriction) for sequence handling, file parsing, NCBI access, BLAST, structures, and phylogenetic trees in Python. Reads, writes, and converts FASTA, GenBank, FASTQ, PDB, mmCIF, Newick, and NEXUS files. Use when manipulating or translating DNA, RNA, or protein sequences, converting sequence file formats, fetching records from NCBI via Entrez, running or parsing BLAST searches, doing pairwise or multiple sequence alignment, analyzing PDB structures, or building and editing phylogenetic trees. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

skills/bioservices/SKILL.md

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

blip-2-vision-language

skills/blip-2-vision-language/SKILL.md

Explains how to use Salesforce BLIP-2 (Q-Former bridging a frozen image encoder and an LLM such as OPT or FlanT5) through HuggingFace Transformers and LAVIS for image captioning, visual question answering, image-text matching, and feature extraction. Use when generating captions for images, building a VQA system, doing zero-shot image-text understanding without task-specific training, matching or retrieving images against text, or fitting a BLIP-2 model into limited GPU memory with INT8/INT4 quantization. Prefer LLaVA or InstructBLIP for instruction-following multimodal chat, and CLIP for plain image-text similarity.

brainstorming-research-ideas

skills/brainstorming-research-ideas/SKILL.md

Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.

bulk-rnaseq

skills/bulk-rnaseq/SKILL.md

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.

cell-press

skills/cell-press/SKILL.md

Prepare manuscripts for Cell Press journals (Cell, Molecular Cell, Neuron, Immunity, Cell Reports, Cell Systems, iScience, Cell Metabolism, Current Biology and others), covering Summary, Highlights, eTOC blurb and graphical abstract, STAR Methods with the Key Resources Table, resource availability (lead contact, materials, data and code), figure and statistics requirements, and Cell Press formatting at submission vs revision. Use when targeting a Cell Press journal, writing highlights or a graphical abstract, or converting methods into STAR Methods.

cellxgene-census

skills/cellxgene-census/SKILL.md

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

chroma

skills/chroma/SKILL.md

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

cirq

skills/cirq/SKILL.md

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

skills/citation-management/SKILL.md

Searches OpenAlex, PubMed, and Google Scholar, extracts metadata from DOIs, PMIDs, PMCIDs, arXiv IDs, and URLs via CrossRef, PubMed, and arXiv, then formats, deduplicates, and validates BibTeX files using bundled Python scripts (search_openalex.py, extract_metadata.py, format_bibtex.py, validate_citations.py, doi_to_bibtex.py). Use when converting DOIs or PMIDs to BibTeX, finding papers on a topic, filling missing volume, pages, or DOI fields, cleaning or merging .bib files with duplicate entries, or checking a bibliography for errors before submission. Not for systematic review search methodology or synthesis; use literature-review for that.

citation-verification

skills/citation-verification/SKILL.md

Verify that every reference in a manuscript really exists and matches its metadata, catching hallucinated, corrupted or mismatched citations before submission. Use when checking a .bib file or reference list, after an AI assistant drafted citations, when a DOI may point to the wrong paper, when adding missing DOIs, or before camera-ready. Queries Crossref, OpenAlex and arXiv; zero dependencies.

clinical-decision-support

skills/clinical-decision-support/SKILL.md

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

skills/clinical-reports/SKILL.md

Generates fail-closed draft JSON templates and runs local deterministic structure and consistency checks for clinical reports: CARE case reports, radiology, pathology and lab scaffolds, CONSORT 2025, SPIRIT 2025, ICH E3 CSRs, aggregate adverse-event tables, provenance manifests, terminology schemas, and de-identification process checklists. Use when drafting a case report for publication from a verified source-fact manifest. Use when checking a trial results manuscript, protocol, or CSR draft against its reporting structure. Use when building an aggregate safety table from synthetic or de-identified data. Use when routing an artifact to the right guideline. Not for diagnosis, treatment advice, individual case safety report creation, or filing and submission.

clip

skills/clip/SKILL.md

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

cobrapy

skills/cobrapy/SKILL.md

Runs constraint-based metabolic modeling with COBRApy (Python, import cobra) on genome-scale models in SBML, JSON, YAML, or MATLAB format. Covers FBA, pFBA, geometric FBA, FVA, flux sampling, gene and reaction knockouts, production envelopes, growth media, gapfilling, and building models. Use when loading or exporting a genome-scale metabolic model. Use when predicting growth or flux distributions with FBA or FVA. Use when screening gene or reaction knockouts. Use when tuning growth media or exchange constraints. Use when gap-filling an infeasible model or checking model consistency. Not for kinetic or ODE-based simulation of metabolism.

consciousness-council

skills/consciousness-council/SKILL.md

Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a problem from multiple angles, requests devil's advocate analysis, or says things like "what would different experts think about this", "help me think through this from all sides", "council mode", "mind council", or "deliberate on this". Also trigger when the user faces a dilemma, trade-off, or complex choice with no obvious answer.

constitutional-ai

skills/constitutional-ai/SKILL.md

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

cover-letter-to-editor

skills/cover-letter-to-editor/SKILL.md

Write journal submission cover letters, presubmission inquiries, transfer requests, and reviewer suggestion or exclusion lists that help a manuscript get past editorial triage. Use when submitting a paper to a journal, pitching a manuscript to an editor (presubmission enquiry), transferring after rejection, explaining a resubmission or appeal, or preparing the editor-facing parts of a submission such as the significance pitch, suggested reviewers and conflicts. Not for job or fellowship application letters.

creative-thinking-for-research

skills/creative-thinking-for-research/SKILL.md

Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.

crewai-multi-agent

skills/crewai-multi-agent/SKILL.md

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

dask

skills/dask/SKILL.md

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

database-lookup

skills/database-lookup/SKILL.md

Query documented public database APIs with explicit endpoints, filters, pagination, and provenance. Use when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from general knowledge.

datalad

skills/datalad/SKILL.md

Retrieve, version, and publish scientific datasets with DataLad and git-annex, and capture computational provenance with datalad run, rerun, and containers-run. Use when cloning or fetching data from OpenNeuro, DANDI, datasets.datalad.org, or any DataLad dataset; when a file in a dataset reads as a broken symlink or a small pointer instead of real data; when an analysis needs a machine-readable record of how each output was produced so it can be re-executed; or when publishing a dataset to siblings such as a GitHub repository plus a storage remote. Also use to decide between DataLad and plain Git for a data-carrying repository.

datamol

skills/datamol/SKILL.md

Wraps RDKit through the datamol Python library (import datamol as dm) for molecular cheminformatics, returning native rdkit.Chem.Mol objects. Covers SMILES/SELFIES/InChI conversion, sanitization and standardization, descriptors, fingerprints and similarity, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds, BRICS/RECAP fragmentation, 3D conformers, reactions, SDF/CSV/Excel I/O including cloud paths, and parallel batch processing. Use when parsing or standardizing SMILES from external sources, computing descriptors or fingerprints for a compound library, clustering or selecting diverse molecules, making scaffold-based train/test splits, or generating conformers. Use when running a molecule-processing pipeline with n_jobs. For fine-grained control or custom parameters, use RDKit directly instead.

deepchem

skills/deepchem/SKILL.md

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

deepspeed

skills/deepspeed/SKILL.md

Covers DeepSpeed for distributed deep learning training and I/O: ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8 training, 1-bit Adam, sparse attention, and DeepNVMe (aio_handle, gds_handle, async_io and gds operators, ds_nvme_tune, ds_report) for fast transfers between NVMe storage and host or GPU tensors. Use when configuring ZeRO stages for large-model training, enabling mixed precision or 1-bit Adam, offloading parameters or optimizer state to NVMe with ZeRO-Infinity, writing or reading tensors to files with blocking or non-blocking DeepNVMe calls, or tuning NVMe I/O settings such as block size, queue depth and parallelism. Not for plain PyTorch DistributedDataParallel without DeepSpeed.

deeptools

skills/deeptools/SKILL.md

Runs deepTools command-line programs on NGS alignment data: bamCoverage and bamCompare for BAM to bigWig/bedGraph with RPGC, CPM, RPKM or BPM normalization, multiBamSummary with plotCorrelation and plotPCA, plotFingerprint, computeMatrix with plotHeatmap and plotProfile, and alignmentSieve with --ATACshift. Use when converting BAM files to normalized coverage tracks. Use when checking ChIP-seq quality or comparing replicates. Use when plotting signal around TSS or peak regions. Use when comparing treatment versus control samples. Use when building ChIP-seq, RNA-seq or ATAC-seq coverage workflows. Not for peak calling or differential expression analysis.

depmap

skills/depmap/SKILL.md

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.

dhdna-profiler

skills/dhdna-profiler/SKILL.md

Extract cognitive patterns and thinking fingerprints from any text. Use this skill when the user wants to analyze how someone thinks, understand cognitive style, profile writing or speech patterns, compare thinking styles between people, asks "what's my thinking style", "analyze how this person reasons", "cognitive profile", "thinking pattern", "DHDNA", "digital DNA", or wants to understand the mind behind any text. Also trigger when the user provides text and wants deeper insight into the author's reasoning patterns, decision-making style, or cognitive signature.

diffdock

skills/diffdock/SKILL.md

DiffDock and DiffDock-L molecular docking. Use for protein-small-molecule pose prediction from PDB or sequence plus SMILES/SDF/MOL2, batch docking, virtual screening, and pose-confidence interpretation. Not for binding affinity prediction.

distributed-llm-pretraining-torchtitan

skills/distributed-llm-pretraining-torchtitan/SKILL.md

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

dnanexus-integration

skills/dnanexus-integration/SKILL.md

Build and operate reproducible genomics workloads on DNAnexus with the dx CLI, dxpy, apps/applets, native workflows, dxCompiler, and Nextflow. Use for DNAnexus data transfers, dxapp.json development, execution monitoring, workflow import, and project automation.

dspy

skills/dspy/SKILL.md

Builds and optimizes language model programs with DSPy (Stanford NLP), using Signatures, modules (Predict, ChainOfThought, ReAct, ProgramOfThought) and optimizers (BootstrapFewShot, MIPRO, BootstrapFinetune). Use when replacing hand-written prompts with declarative signatures, when automatically tuning prompts against training data and a metric, when building multi-stage RAG pipelines or ReAct agents, when building classifiers or structured-output programs, or when saving and evaluating optimized LM programs across providers such as Anthropic, OpenAI, or Ollama. Not for quick prototypes or simple chains; use manual prompting or LangChain instead.

elsevier-cas

skills/elsevier-cas/SKILL.md

Prepare submissions to Elsevier journals (including The Lancet family style notes, Cell-independent Elsevier titles, and thousands of society journals) using the elsarticle LaTeX class, the CAS single/double-column templates or Word, covering highlights, graphical abstracts, keywords, CRediT author statements, declarations of interest, generative-AI disclosure, data availability, reference styles per journal, Editorial Manager submission and "Your Paper Your Way" format-free first submission. Use when targeting an Elsevier journal or fixing elsarticle/CAS template issues.

esm

skills/esm/SKILL.md

Covers the EvolutionaryScale/Biohub `esm` Python SDK: ESM3 generative protein design (sequence, structure and function tracks, chain-of-thought), ESMC embeddings, inverse folding, ESMFold2 structure prediction, and hosted Forge/Biohub inference with ESM_API_KEY. Use when generating or completing proteins with ESM3, extracting ESMC embeddings for classification or similarity, folding a sequence or designing sequences from a structure, or choosing between esm3-open, esmc_300m and Forge-hosted models. Use when batching async Forge or Biohub API calls. Not for other protein models such as AlphaFold or generic Hugging Face transformers usage.

etetoolkit

skills/etetoolkit/SKILL.md

Analyze, manipulate, compare, annotate, and visualize phylogenetic or other hierarchical trees with ETE 4. Use for Newick/Nexus tree I/O, topology edits and pattern matching, Robinson-Foulds comparisons, gene-tree evolutionary events and reconciliation, NCBI/GTDB taxonomy, SmartView exploration, and publication rendering. Do not use it to infer trees from raw sequences; align sequences and infer a tree first.

evaluating-code-models

skills/evaluating-code-models/SKILL.md

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

evaluating-cosmos-policy

skills/evaluating-cosmos-policy/SKILL.md

Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.

evaluating-llms-harness

skills/evaluating-llms-harness/SKILL.md

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

evolving-ai-agents

skills/evolving-ai-agents/SKILL.md

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

exa-search

skills/exa-search/SKILL.md

Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch/extract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article.

experiment-tracking-swanlab

skills/experiment-tracking-swanlab/SKILL.md

Tracks ML experiments with SwanLab, an open-source tool covering swanlab.init, config and metric logging, scalar charts, and media logging (images, audio, text, GIFs, point clouds, molecules), in cloud, local (mode="local" with swanlab[dashboard]), or self-hosted setups. Includes integrations for PyTorch, Transformers, PyTorch Lightning, and Fastai. Use when logging metrics and hyperparameters for training runs, comparing runs across seeds, checkpoints, or hyperparameters, running an offline or self-hosted dashboard instead of managed SaaS, logging media alongside scalars, or adding tracking callbacks to a Transformers, Lightning, or Fastai training loop. Not for other trackers such as Weights & Biases or MLflow.

experimental-design

skills/experimental-design/SKILL.md

Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.

exploratory-data-analysis

skills/exploratory-data-analysis/SKILL.md

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain formats are reference-only and unknown formats fail closed.

faiss

skills/faiss/SKILL.md

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

fictiv

skills/fictiv/SKILL.md

Drives the Fictiv on-demand manufacturing web app (app.fictiv.com) in the user's browser, since there is no public API. Covers uploading CAD files (STEP, SLDPRT, STL), configuring process, material, finish, threads and tolerances, reading DFM feedback, instant or manual quotes, lead-time tiers, checkout by card or PO, order tracking and reorders. Use when the user mentions Fictiv. Use when they want a part CNC machined, 3D printed, sheet-metal fabricated, cast, or molded online. Use when they ask to get a quote or order parts from a STEP or STL file. Use when they need to check a Fictiv quote or order status. Use when a Fictiv upload, DFM warning, price or checkout fails. Not for ITAR-controlled parts, which Fictiv does not accept.

fine-tuning-openvla-oft

skills/fine-tuning-openvla-oft/SKILL.md

Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.

fine-tuning-serving-openpi

skills/fine-tuning-serving-openpi/SKILL.md

Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.

fine-tuning-with-trl

skills/fine-tuning-with-trl/SKILL.md

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

firecrawl-research-index

skills/firecrawl-research-index/SKILL.md

Query Firecrawl Research Index paper endpoints for topic discovery, source metadata, question-matched passages, and citation-neighbor expansion. Use when a researcher explicitly asks for Firecrawl Research or its hosted arXiv and biomedical paper records.

flowio

skills/flowio/SKILL.md

Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Use for low-level FCS metadata and channel inspection, NumPy event extraction, multi-dataset files, table export, and FCS 3.1 creation; use FlowKit for compensation, cytometry transforms, gating, or FlowJo workspaces.

fluidsim

skills/fluidsim/SKILL.md

Plan, configure, inspect, restart, and analyze bounded FluidSim computational-fluid-dynamics simulations with explicit numerical-validity and HPC safety checks. Use for FluidSim solver selection, parameter review, FFT/MPI setup, output diagnostics, or restart compatibility.

folklore-variant-evidence

skills/folklore-variant-evidence/SKILL.md

Retrieve ClinGen gene-disease validity assertions for a public gene or disease, and review source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple indel through Folklore Clinical Variant Interpretation MCP. Use when a scientific agent must branch deterministically on resolved, ambiguous, not-found, invalid, unsupported, or unavailable variant outcomes; chain a resolved public variant into related literature or publication details; or preserve evidence provenance without accepting patient, phenotype, family, segregation, or private case data.

generate-image

skills/generate-image/SKILL.md

Generate or edit images with AI models through the OpenRouter Image API (Gemini, Seedream, Recraft, GPT-Image, Riverflow). Use for photos, illustrations, artwork, concept art, visual assets, logos, and image editing or compositing from reference images. For flowcharts, circuits, pathways, and other technical diagrams, use the scientific-schematics skill instead.

geniml

skills/geniml/SKILL.md

Plans and audits local genomic-interval machine learning workflows with Geniml (0.8.4) and Gtars: validates BED files against chromosome sizes and assembly contracts, plans Region2Vec, scEmbed, and BEDspace runs, checks model, tokenizer, and universe.bed compatibility, and assesses consensus universes (CC, CCF, ML, HMM, assess-universe). Bundled scripts only validate or plan; they do not train. Use when checking BED coordinates, contigs, or assembly before analysis. Use when planning Region2Vec or scEmbed training on tokenized Parquet data. Use when verifying that a checkpoint, config.yaml, and universe match. Use when building or assessing a consensus peak universe. Use when reviewing BEDbase or Hugging Face download risks. Not for general BED manipulation or peak calling; use bedtools or similar tools.

genomic-coordinates

skills/genomic-coordinates/SKILL.md

Convert genomic intervals between coordinate conventions, normalise and compare variant representations, and detect assembly or contig-naming mismatches before they corrupt an analysis. Use whenever coordinates cross a format, tool, or assembly boundary - converting between BED, GFF/GTF, VCF, SAM/BAM, WIG, PSL, genePred, Picard interval_list, or region strings; reconciling 0-based half-open with 1-based inclusive; left-aligning or trimming indels; checking whether two variant records describe the same change; mapping genomic to transcript, CDS, or protein positions; auditing a BED/GTF/VCF for convention violations; or diagnosing GRCh37 vs hg19 vs GRCh38 vs T2T, chr-prefix, and liftover problems. Triggers include "off by one", "0-based", "1-based", "half-open", "coordinate system", "left-align", "normalize variant", "bcftools norm", "chr prefix", "wrong genome build", "liftover", "REF mismatch", and "HGVS".

genomic-intelligence

skills/genomic-intelligence/SKILL.md

Predict regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence''s hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene symbol, a genomic region, or a DNA/FASTA sequence and wants any of these predictions, mentions Genomic Intelligence, genomicintelligence.ai, api.genomicintelligence.ai, or mcp.genomicintelligence.ai.

geomaster

skills/geomaster/SKILL.md

Provides geospatial and Earth observation workflows using GeoPandas, Rasterio, GDAL, Xarray, Shapely, Laspy, PDAL, Google Earth Engine, and STAC/Planetary Computer, covering Sentinel, Landsat, MODIS, SAR and hyperspectral imagery, spectral indices, terrain and network analysis, point clouds, COGs, CRS handling, and spatial ML, with code in Python, R, Julia, JavaScript, C++, Java, Go, and Rust. Use when computing NDVI or other indices from satellite imagery, when running vector overlays, reprojection, or spatial statistics on shapefiles, GeoJSON, or GeoPackage data, when searching STAC catalogs and reading cloud-optimized GeoTIFFs, when classifying land cover or training ML models on Earth observation data, or when doing terrain, hydrology, or point cloud analysis. Not for general non-spatial data analysis or tabular ML.

geopandas

skills/geopandas/SKILL.md

Guidance and local audit CLIs for Python workflows using GeoPandas 1.1.4 GeoSeries and GeoDataFrame for planar vector data: CRS handling, geometry validity and repair, sjoin, overlay, clip, dissolve, union, GeoPackage, GeoParquet and PostGIS I/O, and plotting. Use when reprojecting or assigning a CRS, auditing invalid or empty geometries, running spatial joins and checking cardinality, exporting vector data with a reproducible contract, or migrating code from GeoPandas 0.14 to 1.x. Use when checking privacy risks in coordinate data. Do not use for raster data or geodesic-only calculations.

get-available-resources

skills/get-available-resources/SKILL.md

Detect host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.

gget

skills/gget/SKILL.md

Queries 20+ bioinformatics databases and analysis services through the gget CLI and Python package, covering Ensembl gene search, info and sequences (ref, search, info, seq), BLAST, BLAT, MUSCLE, DIAMOND, PDB, AlphaFold, ELM, ARCHS4, CELLxGENE, Enrichr, Bgee, OpenTargets, cBioPortal, COSMIC, viral sequence downloads (virus), and 8cube mouse specificity and expression data. Use when looking up gene or transcript details, running a quick BLAST/BLAT search, fetching AlphaFold or PDB structures, running enrichment analysis on a gene list, downloading viral sequences with filters, or exploring disease and drug associations interactively. Not for batch processing or fine-grained BLAST control (use biopython) or multi-database Python pipelines (use bioservices).

gguf-quantization

skills/gguf-quantization/SKILL.md

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

ginkgo-cloud-lab

skills/ginkgo-cloud-lab/SKILL.md

Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to run protein expression and purification (cell-free, E. coli, or Pichia), HiBiT or A280 or LabChip quantification, IVT mRNA/circRNA synthesis, thermal shift / developability assays, Echo-MS enzyme or analyte methods, SPR target onboarding, fluorescent pixel art, or otherwise interact with Ginkgo Cloud Lab services. Covers protocol selection, input preparation, pricing, and ordering workflows.

gptq

skills/gptq/SKILL.md

Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers. Covers loading pre-quantized GPTQ models, quantizing your own model, choosing group size, selecting ExLlamaV2, Marlin, or Triton kernels, and QLoRA fine-tuning with PEFT. Use when fitting 70B-class models onto limited or consumer GPUs, cutting memory about 4x versus FP16, speeding up inference, finding pre-quantized checkpoints on HuggingFace, or fine-tuning a quantized model with LoRA. For slightly better accuracy on newer GPUs use AWQ instead, and for simple 8-bit or on-the-fly quantization use bitsandbytes.

grpo-rl-training

skills/grpo-rl-training/SKILL.md

Guides GRPO (Group Relative Policy Optimization) fine-tuning of language models with the TRL library, including GRPOTrainer configuration, composing multiple reward functions (correctness, format, length, style), dataset prep in chat format, Unsloth setup, LoRA merging, and monitoring reward, reward_std and KL. Use when training a model to follow a strict output format such as XML or JSON. Use when teaching verifiable tasks like math or code with objective correctness rewards. Use when improving chain-of-thought reasoning with custom reward functions. Use when debugging flat rewards, mode collapse, or OOM in GRPO runs. Do not use for plain supervised fine-tuning (use SFT) or when high-quality preference pairs exist (use DPO or PPO).

gtars

skills/gtars/SKILL.md

Inspects and plans work with Gtars, the Rust/Python/CLI toolkit for genomic intervals: BED RegionSet set algebra (reduce, setdiff, intersect, closest, cluster, gaps), overlap counts, consensus peak sets, coverage, tokenizers, fragment processing, and refget/BEDbase stores. Use when merging or comparing BED interval sets, building a consensus or universe from peaks, tokenizing regions for ML models, working with fragment files, or planning refget sequence-collection and BEDbase cache use. Use when checking BED coordinates, contigs, or strand before running gtars. Not for general BAM/VCF processing; use samtools or bedtools for that.

guidance

skills/guidance/SKILL.md

Constrains LLM output during generation with Guidance (Microsoft Research), using regex, select() choices, context-free grammars, token healing, and @guidance functions, with Anthropic, OpenAI, Transformers, and llama.cpp backends. Use when you need generated text to match a regex or fixed format such as dates, emails, or IDs. Use when you need guaranteed valid JSON, XML, or code from a model. Use when building multi-step generation workflows or ReAct-style agents with Python control flow. Use when classifying text into fixed categories with select(). Use when running local models and wanting grammar constraints. Not for Pydantic validation with automatic retries; use Instructor for that.

histolab

skills/histolab/SKILL.md

Extracts tiles and preprocesses H&E whole slide images with the histolab Python library (OpenSlide), covering slide inspection, tissue masks (TissueMask, BiggestTissueBoxMask), RandomTiler, GridTiler and ScoreTiler extraction, image and morphological filters, and Macenko or Reinhard stain normalization. Use when building a tile dataset from WSI files for deep learning. Use when detecting tissue and excluding background or pen annotations. Use when previewing tile locations and exporting tile CSV reports. Use when normalizing stain variation across slides. Not for spatial proteomics, multiplexed imaging, or deep learning pipelines; use pathml for those.

hqq-quantization

skills/hqq-quantization/SKILL.md

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

hugging-science

skills/hugging-science/SKILL.md

Use when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering, math, drug discovery, protein design, weather modeling, theorem proving, single-cell, or PDE solving. Hugging Science is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces. This skill helps discover and use resources via `datasets`, `transformers`, the HF Inference API, `gradio_client`, and methodology citations.

huggingface-accelerate

skills/huggingface-accelerate/SKILL.md

Wraps existing PyTorch training scripts with HuggingFace Accelerate (Accelerator class, accelerate config, accelerate launch) so the same code runs on CPU, single GPU, multi-GPU, multi-node, TPU, or Apple MPS. Covers device placement, FP16/BF16/FP8 mixed precision, gradient accumulation, distributed checkpointing, and switching between DDP, DeepSpeed ZeRO, FSDP, and Megatron backends. Use when converting a single-GPU script to multi-GPU, enabling mixed precision, configuring DeepSpeed ZeRO or FSDP, or setting up gradient accumulation. Use when one script must run on different hardware. Not for callback-heavy training loops (use PyTorch Lightning) or multi-node orchestration with hyperparameter tuning (use Ray Train).

huggingface-tokenizers

skills/huggingface-tokenizers/SKILL.md

Provides the HuggingFace Tokenizers library (Rust core with Python and Node.js bindings) for training and using BPE, WordPiece, and Unigram tokenizers. Covers the pipeline of normalizers, pre-tokenizers, models, post-processors and decoders, padding and truncation, batch encoding, alignment tracking, and conversion to transformers PreTrainedTokenizerFast. Use when training a custom tokenizer or vocabulary on a new corpus, tokenizing large text corpora quickly, mapping tokens back to character offsets for NER or question answering, or configuring normalization and special tokens. Use when wrapping a custom tokenizer for transformers. For SentencePiece models or tiktoken, use those tools instead; for loading a pretrained tokenizer only, AutoTokenizer is enough.

hypogenic

skills/hypogenic/SKILL.md

Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.