Browse Skills — Page 297

tooluniverse-precision-medicine-stratification

mims-harvard/ToolUniverse

Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic, and personalized therapeutic strategy reports per patient subgroup.

tooluniverse-precision-oncology

mims-harvard/ToolUniverse

Cancer treatment recommendations from molecular profile (mutations + cancer type + biomarkers) — FDA-approved + investigational therapies, resistance mechanisms, matching clinical trials, prognosis. Uses CIViC, ClinVar, OpenTargets, ClinicalTrials.gov. Use for tumor-board treatment recommendations, evidence-tiered actionability assessment, and FDA-precedent-driven therapy selection.

tooluniverse-primer-design

mims-harvard/ToolUniverse

PCR / qPCR primer and oligo design — design forward/reverse primers for a target region (SantaLucia nearest-neighbor thermodynamics), compute melting temperature (Tm) and annealing temperature (Ta), check GC content, and screen an oligo for hairpins and primer-dimers. Use when you need primers for a sequence, want to QC an existing primer pair, or need the Tm of an oligo. Covers the primer-design rules (Tm matching, GC clamp, 3'-end, length) and the tools' constraint quirks.

tooluniverse-product-safety-surveillance

mims-harvard/ToolUniverse

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tooluniverse-protein-interactions

mims-harvard/ToolUniverse

Protein-protein interaction (PPI) network analysis — STRING (predicted + experimental), BioGRID (curated), SASBDB (small-angle scattering). Distinguishes physical interactions (binding) from functional associations (co-expression, co-regulation). Use for interactome queries, complex partner identification, and pathway-level interaction analysis.

tooluniverse-protein-lof-mechanism

mims-harvard/ToolUniverse

Propose the mechanism by which a missense variant causes loss-of-function (LoF), synthesizing evidence from 5 independent layers: AlphaMissense pathogenicity, AlphaFold structural context, ESMC sequence likelihood, SAE feature disruption, and DynaMut2 stability ΔΔG. Distinguishes 'structural stability LoF' (mis-folding) from 'direct functional disruption' (catalytic / binding / PTM site damage). Use for coding missense variants where you need a mechanistic causal model, not just a pathogenicity score.

tooluniverse-protein-modification-analysis

mims-harvard/ToolUniverse

Post-translational modification (PTM) analysis — phosphorylation, ubiquitination, acetylation, glycosylation, methylation. Uses iPTMnet (sites + enzymes), ProtVar (functional consequences), UniProt (baseline), STRING, ELM (linear motifs), MassIVE/ProteomeXchange (experimental). Use for PTM site annotation, kinase-substrate identification, and PTM-disease associations.

tooluniverse-protein-sae-variant-interpretation

mims-harvard/ToolUniverse

Interpret a missense variant via ESMC-6B Sparse Autoencoder (SAE) feature activations. For a given protein + variant, computes which interpretable SAE features (catalytic, ligand-binding, PTM, structural motif, domain, etc.) are lost or gained at the mutation site. Use when standard pathogenicity scores (AlphaMissense, ClinVar) say a variant is damaging but you need a MECHANISTIC explanation — e.g. 'why is this variant LoF?' Complements (does not replace) variant-interpretation and variant-to-mechanism skills, which focus on ACMG classification or regulatory mechanism.

tooluniverse-protein-structural-annotation-pdb

mims-harvard/ToolUniverse

Given a PDB structure, produce a per-residue annotation table: which residues sit at a binding interface (vs a partner chain), which line a ligand pocket, which are buried (core) vs solvent-exposed (surface), and optionally secondary structure. This is the structural track drawn under a DMS heatmap and the structural prior SAE feature drops are read against. Use when you need to anchor a variant-interpretation or DMS analysis to the protein's actual physical context.

tooluniverse-protein-structure-prediction

mims-harvard/ToolUniverse

Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.

tooluniverse-protein-structure-retrieval

mims-harvard/ToolUniverse

Protein structure retrieval from RCSB PDB, PDBe, and AlphaFold with disambiguation, quality assessment (resolution, R-factor, pLDDT), and metadata. Distinguishes high-quality experimental (X-ray under 2 Angstrom) vs predicted vs medium-quality structures. Use for fetching protein structures, structure-quality comparison, and selecting structures for drug design or modeling.

tooluniverse-protein-therapeutic-design

mims-harvard/ToolUniverse

AI-guided de novo protein design — RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation (pLDDT, pTM, MPNN scores). Use for designing therapeutic protein binders, novel scaffolds, enzyme variants, and miniprotein/protein-interface design before experimental validation.

tooluniverse-proteomics-analysis

mims-harvard/ToolUniverse

Mass-spec proteomics analysis — protein identification, quantification (LFQ, TMT, iTRAQ), differential expression (tumor vs normal, treatment vs control), PTM identification, and pathway enrichment on protein lists. Use when you have proteomics MS output, asking about protein abundance differences, or doing systems-level proteomic interpretation.

tooluniverse-proteomics-data-retrieval

mims-harvard/ToolUniverse

Find and retrieve proteomics datasets from MassIVE and ProteomeXchange. Search by species, keyword, or accession; retrieve detailed metadata (instruments, publications, species, PTMs studied). Use for locating public proteomics datasets to reanalyze, comparing instrument/protocol coverage across studies, and pre-download dataset evaluation.

tooluniverse-rare-disease-diagnosis

mims-harvard/ToolUniverse

Rare disease differential diagnosis from patient phenotype — HPO term matching to candidate diseases (Orphanet, OMIM), gene panel prioritization, ACMG variant interpretation, and structure-based variant analysis. Use for diagnostic odyssey assistance, phenotype-to-disease ranking, and genetic-counseling differential generation.

tooluniverse-rare-disease-genomics

mims-harvard/ToolUniverse

Rare disease genomics — disease identification (Orphanet), causative gene discovery, gene-disease validity (GenCC), variant interpretation (ClinVar), and translational research (ClinicalTrials.gov, drug repurposing for orphans). Use for rare-disease-gene curation, novel-gene-discovery analysis, and rare-disease drug-development support.

tooluniverse-regulatory-genomics

mims-harvard/ToolUniverse

Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.

tooluniverse-regulatory-variant-analysis

mims-harvard/ToolUniverse

Non-coding/regulatory variant interpretation — GWAS association lookup, eQTL evidence (GTEx), chromatin state (ENCODE), regulatory variant scoring (RegulomeDB, CADD), and TF-binding disruption. Use for non-coding GWAS hit interpretation, eQTL-based gene assignment, and regulatory mechanism reasoning. Distinct from coding-variant tools.

tooluniverse-residue-functional-mechanism-interpretation

mims-harvard/ToolUniverse

Given a set of residues in a protein, explain WHY they are functionally critical by combining structural context (binding interface, ligand pocket, core, secondary structure), UniProt features (active sites, binding sites, PTM sites, disulfides), optional SAE feature evidence, and optional DMS data. Accepts residues from any source: DMS hotspots (top-K by max effect), ClinVar recurrent variants, literature-reported hot regions, evolutionarily conserved positions, or user-curated lists. Returns a per-cluster mechanism call: catalytic / ligand-binding / interface / structural-core / PTM / regulatory / unknown.

tooluniverse-rnaseq-deseq2

mims-harvard/ToolUniverse

RNA-seq differential expression analysis with DESeq2, edgeR, and limma-voom — DEG lists, fold changes, dispersion estimation, design formulas including covariates, multi-condition contrasts, and Venn-set operations across groups. Routes across DESeq2 (default), edgeR (QL-F / exact test for small replicate counts), and limma-voom (large n / complex designs). Use when you have a count matrix + metadata, want to find DEGs, or need dispersion/PCA/clustering analysis. Includes RULE ZERO precedence (read executed.ipynb if present).

tooluniverse-sdk

mims-harvard/ToolUniverse

Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.

tooluniverse-self-review

mims-harvard/ToolUniverse

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tooluniverse-sequence-analysis

mims-harvard/ToolUniverse

Biological sequence analysis — gene/protein sequence retrieval (NCBI, Ensembl, UniProt), nucleotide/protein search, ortholog discovery, and FASTQ QC + alignment workflows (Trimmomatic, BWA, samtools, coverage depth). Use for sequence retrieval, sequence comparison, FASTQ QC analysis, and read alignment pre-processing.

tooluniverse-sequence-retrieval

mims-harvard/ToolUniverse

Retrieve DNA/RNA/protein sequences from NCBI and ENA with disambiguation. Quality hierarchy: RefSeq (NM_/NP_) > RefSeq predicted (XM_/XP_) > GenBank submissions. Use for fetching specific sequences by accession, gene-symbol-to-sequence lookup, transcript-isoform retrieval, and curated-vs-raw-submission preference.

tooluniverse-single-cell

mims-harvard/ToolUniverse

Single-cell RNA-seq analysis with scanpy/anndata — h5ad data loading, scRNA-seq quality control and QC gating (n_genes_by_counts, total_counts, mitochondrial percent / pct_counts_mt, pct_counts_ribo, doublet detection with Scrublet/scDblFinder, ambient RNA / SoupX awareness, empty-droplet filtering, MAD-based thresholds), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering (Leiden, Louvain), marker gene identification, cell-type annotation, pseudotime/trajectory analysis. Use for any scRNA-seq workflow, including deciding which cells to filter, flag, or investigate before downstream analysis.

tooluniverse-small-molecule-discovery

mims-harvard/ToolUniverse

Small molecule identification, characterization, and procurement — PubChem, ChEMBL, BindingDB, ADMET-AI, SwissADME, eMolecules, Enamine. Covers compound name to structure to activity to ADMET properties to commercial sourcing. Use for chemical biology, lead identification, probe selection, and the full small-molecule discovery pipeline.

tooluniverse-spatial-omics-analysis

mims-harvard/ToolUniverse

Spatial multi-omics interpretation pipeline. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into biological insights via domain-by-domain characterization, cell-type composition, spatial gene expression patterns, RNA+protein+metabolite integration. Use for Visium, MERFISH, seqFISH, Slide-seq, spatial proteomics, and spatial multi-omics interpretation. Goes beyond statistics to disease mechanisms and therapeutic opportunities.

tooluniverse-spatial-transcriptomics

mims-harvard/ToolUniverse

Spatial transcriptomics analysis — Visium, MERFISH, seqFISH, Slide-seq. Maps gene expression to tissue architecture, identifies spatially variable genes (SVGs), tissue-domain segmentation, and cell-cell interaction inference. Use for spatial gene-expression questions, tissue architecture analysis, and SVG identification.

tooluniverse-statistical-modeling

mims-harvard/ToolUniverse

Statistical modeling — linear/logistic/ordinal/Poisson regression, ANOVA, Kruskal-Wallis, chi-square, Mann-Whitney, Cox survival, spline fits (R `ns()`), odds ratios, Cohen's d, F-statistic, p-value computation. Specializes in clinical-trial AE analysis (SDTM DM/AE), severity ordinal regression, and per-feature stat workflows.

tooluniverse-stem-cell-organoid

mims-harvard/ToolUniverse

Stem cell, iPSC, and organoid research — pluripotency markers, differentiation protocol pathways, lineage commitment factors, organoid model selection. Use for iPSC characterization, differentiation protocol design via developmental-pathway recapitulation, and organoid-model selection for disease modeling.

tooluniverse-structural-proteomics

mims-harvard/ToolUniverse

Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, and BindingDB ligand-affinity data. Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), and antibody-target interface analysis.

tooluniverse-structural-variant-analysis

mims-harvard/ToolUniverse

Structural variant (SV) clinical interpretation: deletions, duplications, inversions, translocations, complex rearrangements. Applies ACMG-adapted criteria with ClinGen HI/TS dosage scores, gnomAD frequencies, and ClinVar evidence. Produces 5-tier classification with explicit per-criterion evidence. Use for clinical genomics SV review, dosage-sensitivity assessment, breakpoint analysis, and CNV pathogenicity calls. Gene-dosage-driven reasoning.

tooluniverse-systems-biology

mims-harvard/ToolUniverse

Systems biology and pathway analysis integrating Reactome, KEGG, WikiPathways, BioCarta, NCI-Nature Pathway Interaction Database. Multi-database pathway enrichment, protein-pathway relationships, network reasoning. Use for pathway analysis on a gene list, multi-source pathway concordance, and systems-level interpretation across databases.

tooluniverse-target-research

mims-harvard/ToolUniverse

Comprehensive drug-target intelligence — tissue expression (GTEx, HPA), pathways, protein interactions (STRING), variant landscape (ClinVar, gnomAD), druggability (DGIdb, ChEMBL approved drugs). 9 parallel research paths with citations. Use for full target profile reports, target characterization for drug discovery, and 'tell me about target X' queries.

tooluniverse-toxicology

mims-harvard/ToolUniverse

Drug and chemical toxicity assessment via adverse outcome pathways (AOPs), real-world FAERS adverse event signals, FDA labels, and toxicogenomic associations. Triangulates molecular initiating event to cellular outcome to organ-level toxicity to clinical adverse event. Use for hepatotoxicity/cardiotoxicity/nephrotoxicity prediction and toxicology reports.

tooluniverse-vaccine-design

mims-harvard/ToolUniverse

Computational vaccine candidate design: peptide/subunit vaccines via MHC-I/MHC-II epitope prediction (IEDB), population HLA coverage optimization, B-cell epitope identification, and cross-strain conservation analysis. Use for vaccine epitope prediction, HLA allele coverage, multi-epitope construct design, and immunogenicity assessment. Combines predicted MHC binding with experimentally validated IEDB epitopes for higher-confidence designs.

tooluniverse-variant-analysis

mims-harvard/ToolUniverse

VCF and variant analysis — parsing, annotation, classification (synonymous, missense, frameshift, stop_gained), VAF filtering, coding vs non-coding categorization, multi-condition variant comparison. Use for VCF parsing, variant fraction calculations (denominator = coding subset only, NOT all variants), and per-sample mutation profiling.

tooluniverse-variant-functional-annotation

mims-harvard/ToolUniverse

Functional annotation of protein variants — ProtVar structural/functional context, ClinVar clinical classifications, gnomAD population frequencies, CADD deleteriousness, ClinGen gene-disease validity, plus FAVOR one-call comprehensive GRCh38 annotation. Use for variant annotation pipelines, missense effect prediction, and protein-level variant interpretation with functional context.

tooluniverse-variant-interpretation

mims-harvard/ToolUniverse

Clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Use for VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), non-coding/regulatory variant effect prediction with sequence deep-learning models (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2), and producing clinical-grade variant reports for return of results or molecular tumor boards. Use this whenever a user asks about a variant's significance, an intronic/promoter/enhancer/UTR non-coding variant's functional impact, or needs ACMG classification — even if they don't say "ACMG".

tooluniverse-variant-predictor-dms-validation

mims-harvard/ToolUniverse

Validate a variant-effect predictor (AlphaMissense, ESM-C SAE, ESM logits, EVE, conservation scores, or any per-variant numeric score) against experimental deep mutational scanning (DMS) data. Computes per-variant predictor scores, splits variants into neutral vs disruptive groups by DMS effect, runs a Mann-Whitney U test on the predictor scores, and sweeps the stratification thresholds for robustness. Use when you need to know whether a predictor's scores track real functional disruption on a specific protein.

tooluniverse-variant-to-mechanism

mims-harvard/ToolUniverse

End-to-end variant-to-mechanism analysis — trace a variant (rsID/coordinates) through regulatory context, target gene(s), molecular pathway(s), and phenotypic consequences. Integrates 7+ databases across 3 evidence layers (regulatory, molecular, disease) for a mechanistic model. Use for GWAS-hit-to-mechanism, eQTL-causal-gene tracing, and full causal-chain reports.

toon-format

closedloop-ai/claude-plugins

This skill should be used when writing or parsing TOON (Token-Oriented Object Notation) files. Triggers on .toon file operations, org-patterns.toon generation, or when converting between JSON and TOON formats. Provides syntax rules, quoting conventions, and examples for LLM-optimized data serialization.

top-banner-generator

kostja94/marketing-skills

When the user wants to add, optimize, or audit a top announcement bar or sticky banner. Also use when the user mentions "announcement bar," "top banner," "sticky bar," "promo banner," "discount banner," "student discount banner," "header banner," "announcement bar design," "sticky header," "promo bar," "urgency banner," or "lead capture bar." For promos, use discount-marketing-strategy.

top-design

wondelai/skills

Create award-winning, immersive web experiences at the level of Awwwards-featured agencies. Use when the user mentions "Awwwards quality", "make my site stunning", "scroll animations", "parallax storytelling", "cinematic web design", "portfolio site", or "brand experience". Also trigger when elevating a standard landing page into a memorable digital experience. Covers dramatic typography, purposeful motion, scroll-based composition, and performance-optimized animation. For foundational UI, see refactoring-ui. For type selection, see web-typography.

top-down-typescript

paleo/alignfirst

TypeScript and JavaScript coding style conventions, centered on top-down narrative ordering (caller first, helpers below) and functions over classes. Read before writing or reviewing TypeScript/JavaScript code, including code inside a spec or a plan.

top-web-vulnerabilities

benjaminasterA/antigravity-awesome-skills

This skill should be used when the user asks to \"identify web application vulnerabilities\", \"explain common security flaws\", \"understand vulnerability categories\", \"learn about inject...

topic-brainstormer

notque/vexjoy-agent

Generate blog topic ideas: problem mining, gap analysis, expansion.

topic-brief

genli-ai/market-research-skills

Generate a topic-focused briefing in HTML from public news sources. Use when user asks for a briefing / observation / digest / 简报 / 观察 on any subject — region (Middle East, ASEAN, India), industry (semiconductors, EV supply chain, AI), policy issue (AI regulation, critical minerals, cross-border payments), institution (Fed, ECB, IMF), or theme. Output is a single self-contained HTML file with blue-color "TOPIC BRIEF" branding, optimized for both browser viewing and direct copy-paste into 微信公众号 / WeChat Official Account editor. Walks through 5 steps with one user confirmation midway. Do NOT use this skill for short summaries (<1000 字), single-piece news commentary, or already-defined report templates.

topic-cluster-planning

inhouseseo/superseo-skills

Use when planning a topic cluster (hub + spokes) for a new content area. The agent researches the space, identifies the hub topic, maps the spokes, and produces a specific content plan with internal linking strategy.

topic-evidence-mapper

aipoch/medical-research-skills

Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Always use this skill when a user needs a structured evidence map of a medical topic before deeper reading, gap analysis, or study planning. Do not treat evidence mapping as formal gap identification.

topic-momentum

aaronjmars/aeon

Weekly content-gap scanner — cross-references rising narrative signals (narrative-tracker, tweet-roundup, paper-pick, etc.) against recent article history and surfaces the top 3 uncovered angles to write next.

topical-authority-mapper

gooseworks-ai/goose-skills

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torch-geometric

K-Dense-AI/scientific-agent-skills

Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torch_geometric / PyG. Also trigger when you see imports from torch_geometric, or the user mentions graph convolutions (GCN, GAT, GraphSAGE, GIN), graph data structures, or working with relational/network data. Even if the user just says 'graph learning' or 'geometric deep learning', use this skill.

torch-geometric-graph-neural-networks

jaechang-hits/SciAgent-Skills

PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.

torchdrug

K-Dense-AI/scientific-agent-skills

PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.

torchdrug-english

aipoch/medical-research-skills

PyTorch-native Graph Neural Network framework for molecules and proteins. Suitable for building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, and retrosynthesis. If you need pretrained models and diverse feature extractors, use deepchem; if you need benchmark datasets, use pytdc.

torchforge-rl-training

Orchestra-Research/AI-Research-SKILLs

Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.

toss-securities

NomaDamas/k-skill

토스증권 조회형 질문에 대해 tossinvest-cli의 tossctl을 설치/로그인한 뒤 계좌 요약, 포트폴리오, 시세, 주문내역, 관심종목을 안전한 read-only 흐름으로 조회한다.

total-segmentator

mkurman/zorai

Tool for robust segmentation of 104+ anatomical structures in CT images. Uses nnUNet-based models for whole-body, organ, and bone segmentation. One-line CLI for comprehensive body-part segmentation.

touchdesigner-mcp

NousResearch/hermes-agent

Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.

toxicity_assessment

InternScience/scp

Drug Toxicity Assessment - Comprehensive toxicity assessment: FDA adverse reactions, nonclinical toxicology, carcinogenicity data, and ADMET prediction. Use this skill for toxicology tasks involving get adverse reactions by drug name get nonclinical toxicology info by drug name get carcinogenic mutagenic fertility impairment info by drug name pred molecule admet. Combines 4 tools from 2 SCP server(s).

toxicity-structure-alert

aipoch/medical-research-skills

Analyze data with `toxicity-structure-alert` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

tpd-ternary-complex-agent

swaruplab/operon

AI-powered ternary complex prediction for targeted protein degradation, modeling POI-degrader-E3 ligase assemblies to optimize PROTAC and molecular glue efficacy.

tpmi-phewas-skill

openai/plugins

Fetch compact TPMI PheWAS summaries for single variants by accepting rsID, GRCh37, or GRCh38 input and resolving to the required GRCh38 query. Use when a user wants concise TPMI association results for one variant

tpscheck-automation

ComposioHQ/awesome-claude-skills

"Automate Tpscheck tasks via Rube MCP (Composio). Always search tools first for current schemas."

trace

boshu2/agentops

Trace decisions through artifacts.

trace-and-isolate

rohitg00/skillkit

Applies systematic tracing and isolation techniques to pinpoint exactly where a bug originates in code. Use when a bug is hard to locate, code is not working as expected, an error or crash appears with unclear cause, a regression was introduced between recent commits, or you need to narrow down which component, function, or line is faulty. Covers binary search debugging, git bisect for regressions, strategic logging with [TRACE] patterns, data and control flow tracing, component isolation, minimal reproduction cases, conditional breakpoints, and watch expressions across TypeScript, SQL, and bash.

trace-history

tranhieutt/software_development_department

Reads and filters production/traces/decision_ledger.jsonl to display a timeline of agent decisions. Supports filtering by agent, risk_tier, task_id, outcome, and date range.

trace-to-skill-inducer

Tibsfox/gsd-skill-creator

>

trace-to-training-data

wshobson/agents

Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.

traceability-audit

HoangNguyen0403/agent-skills-standard

Map requirement, acceptance criteria, implementation, tests, PR evidence, and release artifacts into one traceability report.

traceloop

TerminalSkills/skills

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Tracing Knowledge Lineages

obra/superpowers-skills

Understand how ideas evolved over time to find old solutions for new problems and avoid repeating past failures

Tracing Knowledge Lineages

aAAaqwq/AGI-Super-Team

Understand how ideas evolved over time to find old solutions for new problems and avoid repeating past failures

tracing-downstream-lineage

astronomer/agents

Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.

tracing-upstream-lineage

astronomer/agents

Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.

track-findings

openai/codex-security

Track validated Codex Security findings in Linear, Jira, GitHub issues, or draft GitHub security advisories. Supports issue batches, duplicate checks, and reviewed writes. Do not use for scans or fixes.

track-management

benjaminasterA/antigravity-awesome-skills

Use this skill when creating, managing, or working with Conductor

track-management

wshobson/agents

Use this skill when creating, managing, or working with Conductor tracks - the logical work units for features, bugs, and refactors. Applies to spec.md, plan.md, and track lifecycle operations.

track-management-v2

diegosouzapw/awesome-omni-skills

Track Management workflow skill. Use this skill when the user needs creating, managing, or working with Conductor tracks - the logical work units for features, bugs, and refactors. Applies to spec.md, plan.md, and track lifecycle operations and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

track-partner-pipeline

Significant-Gravitas/skills-catalog

Use when the user asks what partners are producing: the sourced and influenced split with its proofs, coverage math, and the leadership report.

trackcn

vercel-labs/trackcn

Use the trackcn CLI to sync files, directories, commits, pull requests, and agent skills from GitHub into a codebase while preserving local edits. Read this before running trackcn commands or modifying trackcn-managed files.

tracking

BuilderIO/agent-native

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tracking-crypto-derivatives

aAAaqwq/AGI-Super-Team

Comprehensive derivatives market analysis across centralized and decentralized exchanges. This skill aggregates funding rates, open interest, liquidat

tracking-crypto-portfolio

aAAaqwq/AGI-Super-Team

This skill provides comprehensive cryptocurrency portfolio tracking with

tracking-crypto-prices

aAAaqwq/AGI-Super-Team

Foundation skill providing real-time and historical cryptocurrency price data. This skill is the data layer for the crypto plugin ecosystem - 10+ othe

tracking-threat-actor-infrastructure

mukul975/Anthropic-Cybersecurity-Skills

Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, a

trackpy-particle-tracking

jaechang-hits/SciAgent-Skills

Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.

traction-eos

wondelai/skills

Implement the Entrepreneurial Operating System (EOS) to align vision and execution across a company. Use when the user mentions "EOS", "Entrepreneurial Operating System", "V/TO", "quarterly rocks", "Level 10 meetings", "accountability chart", "IDS process", "my company feels chaotic", "we keep having the same problems", or "get the whole team aligned". Also trigger when a growing company needs meeting structure, goal-setting frameworks, or a systematic way to solve recurring organizational issues. Covers the six EOS components: Vision, People, Data, Issues, Process, Traction. For team motivation design, see drive-motivation. For lean experimentation, see lean-startup.

trade-gate

kansoku-trade/kansoku

>

trade-prediction-markets

aAAaqwq/AGI-Super-Team

Build and test Polymarket prediction market trading strategies for YES/NO token trading. Provides 6 tools: get_all_prediction_events (browse markets, $0.001), get_prediction_market_data (analyze price history, $0.001), create_prediction_market_strategy (generate code, $1-$4.50), run_prediction_market_backtest (test performance, $0.001). Trade on real-world events (politics, economics, sports, crypto). Currently simulation only (live deployment coming soon).

trademark-assistant

cat-xierluo/legal-skills

面向中国商标申请的类别规划、可注册性初筛及申请材料准备技能。基于尼斯分类（国际商标分类），引用中国法律法规。本技能应在接到商标咨询、需要结构化输出建议与风险分级、或需要准备申请材料时使用。不要用于：替代正式法律意见、承诺注册成功率、处理复杂商标争议案件。

tradeoff-analysis

microsoft/amplifier-bundle-systems-design

Structured tradeoff analysis methodology — the 8-dimension comparison frame, tradeoff matrix template, and common tradeoff patterns. Use when evaluating design alternatives, comparing technology choices, or when the answer is 'it depends.'

trading-agents

TerminalSkills/skills

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trading-discipline

kansoku-trade/kansoku

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trading-signals

roman-rr/trading-skills

Fetches live AI crypto trading signals with entry price, stop-loss, take-profit, leverage, confidence scores, and automated verification. Covers 50+ coins including BTC, ETH, SOL. Use when the user asks for crypto signals, trade ideas, market direction, portfolio analysis, or wants to build a trading bot.

traefik

TerminalSkills/skills

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traffic-acquisition

aAAaqwq/AGI-Super-Team

Use when driving traffic to Xiaohongshu account from external sources, acquiring new followers beyond organic discovery, implementing multi-platform growth strategy, or scaling account growth beyond plateau

traffic-analysis

kostja94/marketing-skills

When the user wants to analyze website traffic sources, attribution, or dark traffic. Also use when the user mentions "traffic sources," "dark traffic," "direct traffic," "UTM parameters," "traffic attribution," "channel attribution," "attribution optimization," "channel analysis," "traffic analysis," "traffic diversification," "natural traffic benchmark," or "organic vs paid traffic." For GA4 setup, use analytics-tracking.

trail

simota/agent-skills

Investigating git history, analyzing regression root causes, and performing code archaeology. Time-travels through commit history to uncover truth. Use when git history investigation or regression analysis is needed.