Explore AI Agent Skills & Claude Prompts

Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

ai-scientist-evaluator

Critically review, score, compare, and rank one or more AI scientist outputs for biology, bioinformatics, computational life science, or adjacent research tasks. Trigger when the user asks to evaluate notebooks, code, figures, analyses, manuscripts, software, or final reports produced by AI scientists; compare multiple AI scientists on the same task; judge publication readiness; or audit rigor, reproducibility, novelty, and task completion. Do not use this skill to perform the original research task itself unless the user is explicitly asking for a reviewer-style audit of already produced outputs.

seed-iv-skill

Use this skill whenever the user wants an end-to-end workflow for the SEED-IV (SJTU Emotion EEG Dataset - 4 emotions) dataset, including EEG validation, preprocessing, feature extraction, and emotion classification. Triggers include: 'SEED-IV', 'SEED4', 'emotion EEG', 'EEG emotion recognition', 'process SEED-IV', or any request to run the SEED-IV pipeline.

evo-memory

Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after research-ideation), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing research-ideation cycles or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use research-ideation).

bio-comparative-genomics-ortholog-inference

Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

cbioportal-database

Cancer genomics (TCGA et al.) via cBioPortal REST API. Retrieve somatic mutations, CNAs, expression, clinical data (survival/stage/treatment) across thousands of studies. Use for TMB, oncoprints, survival analysis. For population frequencies use gnomad-database; for drug-gene interactions use dgidb-database.

bnt

Use this model doc whenever the user wants to run BrainNetworkTransformer for fMRI phenotype prediction, including data loading, training, and evaluation. BNT uses dense FC matrices (no PyG dependency) with DEC pooling + interpretable transformer encoder.

dmt-har-med-skill

Use this skill whenever the user wants an end-to-end workflow for the DMT-HAR-MED dataset (ds006644), including download, BIDS organization, and processing of rs-fMRI data from a psychedelic intervention study. Triggers include: 'DMT-HAR-MED', 'DMT HAR MED', 'ds006644', 'process DMT data', 'psychedelic fMRI', or any request to run the DMT-HAR-MED pipeline. This is the NeuroClaw dataset-orchestration layer for DMT-HAR-MED.

bio-clinical-biostatistics-power-sample-size

Computes sample size and power for clinical trials including continuous, binary, and time-to-event endpoints; superiority, non-inferiority, and equivalence designs; FDA 2016 non-inferiority margin selection with M1/M2 framework; Schoenfeld 1981 and Lakatos 1988 for survival; Schuirmann TOST and 80-125% bioequivalence; minimum clinically important difference (MCID) vs δ distinction. Use when justifying trial size in protocol or SAP per CONSORT 2025 item 7.

gnomad-database

gnomAD v4 population variant frequencies via GraphQL API. Allele counts and frequencies stratified by ancestry (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID), gene-level constraint (pLI, LOEUF, missense z), and coverage. Identify rare or constrained variants. For clinical pathogenicity use clinvar-database; for GWAS use gwas-database.

hbn-skill

Use this skill whenever the user wants an end-to-end workflow for the Healthy Brain Network (HBN) dataset, including download, BIDS organization, and multimodal processing of sMRI, dMRI, rs-fMRI, task-fMRI, and EEG data. Triggers include: 'HBN', 'Healthy Brain Network', 'process HBN', 'HBN fMRI', 'HBN EEG', or any request to run the HBN multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for HBN.

hcpd-skill

Use this skill whenever the user wants an end-to-end workflow for the HCP Development (HCP-D) dataset, including dataset download, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'HCP Development', 'HCP-D', 'process HCP Development data', 'HCP Development sMRI fMRI', or any request to run the HCP-D multimodal pipeline.