Processing
Data transformation and parsing
agent-harness
by lightdash
Guide for AI agents running in the isolated agent-harness environment. Use when you need to discover your agent ID, find your ports, manage your stack with agent-cli.sh, run verification, or understand the multi-agent development setup.
ralph-plan
by mastra-ai
Interactive planning assistant that helps create focused, well-structured ralph-loop commands through collaborative conversation
ce-riffrec-feedback-analysis
by EveryInc
Analyze Riffrec feedback captures from bundles or standalone recordings. Always load for riffrec-*.zip, session.json + events.json + recording.webm + voice.webm bundles, .mp4/.mov/.webm videos, .m4a/.mp3/.wav audio, or capture/share requests.
ce-doc-review
by EveryInc
Review requirements, plans, or specs with role-specific lenses. Use when the user wants to improve an existing planning document.
experiments
by langwatch
Create and run LangWatch experiments for pre-deployment batch testing. Use when the user wants to test an agent against a dataset, compare prompts or models, benchmark quality, detect regressions, or add a CI quality gate. Do not use for production monitoring or guardrails.
generate-rag-dataset
by langwatch
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
langwatch-kanban
by langwatch
"Manage the LangWatch Kanban GitHub project board — sync statuses, view your board, find stale items, move issues, assign work."
agent-performance
by langwatch
Deep-dive diagnosis of how your AI agent behaves in production. Explores LangWatch analytics and traces end to end to map failure patterns, dissatisfied users, token cost hotspots, edge cases, behavior changes, and outliers, then delivers an HTML report where every finding links to real example traces. Use when you want to truly understand what your agent is doing in production.
agent-improve
by langwatch
Turns production evidence into tested improvements for your AI agent. Forms hypotheses from real traces and analytics, explains the reasoning behind each one, then executes with the user: scenario tests that reproduce production failures, prompt and code changes as reviewable PRs, new evaluators and monitors that capture production signals, and experiments that settle open questions. Use when you want to know what to do next to improve your agent.
online-evaluations
by langwatch
Configure LangWatch online evaluations and guardrails for production traffic. Use when the user wants to score live traces or threads, monitor production quality, sample incoming traffic, or synchronously block unsafe requests and responses. Do not use for batch experiments.
datasets
by langwatch
Generate realistic synthetic evaluation datasets by analyzing the user's codebase, prompts, production traces, and reference materials. Interactive and consultant-style. Asks clarifying questions, proposes a plan, generates a preview for approval, then delivers a complete dataset uploaded to LangWatch. Use when user asks to generate, create, or build a dataset for evaluation, testing, or benchmarking.
agent-best-practices
by langwatch
Expert AI engineering consultant for your agent development practices. Audits your codebase, traces, evaluations, and scenarios against best practices, then guides you to close the gaps, starting from low-hanging fruit and going deeper. Use when you want to level up your agent's engineering quality.
errors-api-e2e
by triggerdotdev
End-to-end smoke test for the public Errors HTTP API (error groups). Seeds failed runs into ClickHouse so the error materialized views populate, then drives the real endpoints against the running webapp — list (with filters + pagination), retrieve, resolve/ignore/unresolve, the filter[error] runs filter, user attribution via the trigger.dev mint-token -> JWT exchange, and the 401/403/404 negatives. Use for "smoke test the errors API", "test the errors API e2e", "prove the errors endpoints work", or to re-verify after changes.
chart
by Kilo-Org
Use when the user asks to visualize data with charts, graphs, or plots using the chart tool (bar, line, scatter, pie, time series, etc.).
eliza-cloud-buy-domain
by elizaOS
"Use whenever a user wants to register or buy a custom domain for an Eliza Cloud app — including in the same request as building the app (\"build me X and put it on Y.com\"). Uses Cloudflare as registrar after explicit user confirmation, paid from the user's existing cloud credit balance. Pairs with build-monetized-app (build first, then buy domain) and eliza-cloud-manage-domain (post-purchase: list, edit dns records, detach). Skip when the user is fine with the auto-assigned *.apps.eliza.app subdomain."
weather
by elizaOS
Get current weather and forecasts (no API key required). Use when the user asks about the weather, temperature, forecast, wind, humidity, or climate conditions for a city or location. Fetches real-time weather data from free services using curl.
sql-queries
by anthropics
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
instrument-data-to-allotrope
by anthropics
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
statistical-analysis
by anthropics
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
knowledge-synthesis
by anthropics
Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.
scvi-tools
by anthropics
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
data-context-extractor
by anthropics
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
nextflow-development
by anthropics
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
data-validation
by anthropics
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.