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Found 2,772 Skills
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
Redis Search guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR, JSON path), DIALECT 2 query syntax, FT.SEARCH / FT.AGGREGATE / FT.HYBRID command selection, vector similarity with HNSW or FLAT, hybrid retrieval combining lexical and vector ranking, RAG pipelines, zero-downtime index updates via aliases, and debugging with FT.PROFILE and FT.EXPLAIN. Use when defining a search index on Hash or JSON documents, writing FT.SEARCH queries with filters, sorting, aggregation, or vector KNN, tuning HNSW parameters, building a RAG retrieval pipeline, or troubleshooting slow or empty search results.
Reference skill for building production-ready crw integrations. Covers verb selection, call surfaces (CLI/MCP/REST), post-filtering strategies, context-window hygiene, Hybrid RAG patterns, common pitfalls, and crw-specific operational considerations (search backend limits, renderer pool, proxy rotation). Load this when writing application code that embeds crw, designing a multi-step agent workflow, or debugging an integration that isn't behaving as expected.
Produce a risk matrix or heatmap that quantifies what could break by business impact × probability, runs failure mode analysis on the top items, and maps test coverage to risk zones. Includes stakeholder interview frameworks and continuous reassessment. Run this BEFORE test-strategy or test-planning. Use when: "risk assessment," "risk matrix," "risk heatmap," "what could break," "critical paths," "failure modes," "where to focus testing." Not for: multi-quarter QA direction — use test-strategy. Not for: a single sprint/release test plan — use test-planning. Not for: hands-on session-based bug hunting — use exploratory-testing. Related: test-strategy, test-planning, release-readiness, qa-metrics.
BATS shell script testing. Use when writing or running shell script tests. Covers setup/teardown, assertions, mocking, helper patterns, and coverage with kcov.
Run tests with coverage reporting. Auto-detects test frameworks (Vitest, Playwright, RSpec, pytest, Jest, BATS, etc.) and runs appropriate test commands. Use when asked to run tests, check coverage, or validate code.
Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns
Betting analysis — odds conversion, de-vigging, edge detection, Kelly criterion, arbitrage detection, parlay analysis, and line movement. Pure computation, no API calls. Works with odds from any source: ESPN (American odds), Polymarket (decimal probabilities), Kalshi (integer probabilities). Use when: user asks about bet sizing, expected value, edge analysis, Kelly criterion, arbitrage, parlays, line movement, odds conversion, or comparing odds across sources. Also use when you have odds from ESPN and a prediction market price and want to evaluate whether a bet has positive expected value. Don't use when: user asks for live odds or market data — use polymarket, kalshi, or the sport-specific skill to fetch odds first, then use this skill to analyze them.
Complete guide for Marginfi - Solana's decentralized lending protocol for lending, borrowing, leveraged positions(looping) and flash loans. Covers account creation, deposits, borrows, repayments, withdrawals, flash loans, and leveraged positions using the @mrgnlabs/marginfi-client-v2 SDK.
Design and evaluate vaccine candidates using computational immunology tools. Covers epitope prediction (MHC-I/II binding via IEDB), population coverage analysis, antigen selection, adjuvant matching, and immunogenicity assessment. Integrates IEDB for epitope prediction, UniProt for antigen sequences, PDB/AlphaFold for structural epitopes, BVBRC for pathogen proteomes, and literature for clinical precedent. Use when asked about vaccine design, epitope prediction, immunogenicity, MHC binding, T-cell epitopes, B-cell epitopes, or population coverage for vaccine candidates.
Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.
Define, track, and act on QA metrics: test coverage percentage, flakiness rate, defect escape rate, MTTR, test execution time trends, automation ROI, quality gates, and SLAs for test suites. Includes metric formulas, realistic targets by company stage, and the action to take when each metric goes red. Use when: "QA metrics," "test metrics," "quality KPIs," "test health," "flakiness rate," "defect escape rate." Not for: building the dashboard UI (Allure/Grafana) — use qa-dashboard; measuring coverage gaps and mutation score — use coverage-analysis. Related: qa-dashboard, coverage-analysis, ci-cd-integration, release-readiness, quality-postmortem.