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Found 2,225 Skills
Comprehensive Rust coding guidelines covering ownership, error handling, async patterns, traits, testing, performance, clippy, and documentation. Use when writing new Rust code, reviewing or refactoring existing Rust, implementing async systems with Tokio, designing error hierarchies, choosing between borrowing and cloning, setting up tests or benchmarks, configuring linting, or optimizing performance. Do not use for non-Rust languages or general software architecture unrelated to Rust idioms.
Create CodeTour `.tour` files — persona-targeted, step-by-step walkthroughs with real file and line anchors. Use for onboarding tours, architecture walkthroughs, PR tours, RCA tours, and structured "explain how this works" requests.
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.
Challenges AI-generated plans, code, designs, and decisions before you commit. Pairs with any other skill as a review layer. Uses pre-mortem analysis, inversion thinking, and Socratic questioning to find what AI missed — blind spots, hidden assumptions, failure modes, and optimistic shortcuts. The skill that asks "are you sure about that?" so you don't have to. Triggers on: "challenge this", "devils advocate", "stress test this plan", "what could go wrong", "poke holes in this", "review this critically", "second opinion on this design", "what am I missing". Use this skill when you need critical review of any AI-generated output, architecture decision, implementation plan, or code before committing to it.
The foundational knowledge distillation pattern for building and maintaining an AI-powered Obsidian wiki. Based on Andrej Karpathy's LLM Wiki architecture. Use this skill whenever the user wants to understand the wiki pattern, set up a new knowledge base, or needs guidance on the three-layer architecture (raw sources → wiki → schema). Also use when discussing knowledge management strategy, wiki structure decisions, or how to organize distilled knowledge. This is the "theory" skill — other skills handle specific operations (ingesting, querying, linting).
Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of belief-based agent reasoning.
Create professional system architecture diagrams with support for dark/light dual theme switching, output as standalone HTML files (containing SVG graphics). Use this skill when users need system architecture diagrams, infrastructure topology diagrams, cloud architecture visualizations, security architecture diagrams, network topology diagrams, or any technical diagrams that display system components and their relationships. Supports Chinese annotations and descriptions, with a built-in theme switch button.
[Pragmatic DDD Architecture] How to structure **Use Cases** using DDD and Railway-Oriented Programming (neverthrow Result types). Tailored for TypeScript + drizzle-orm + node-postgres stack. **Use whenever creating or modifying any Use Case class — even simple ones like "Exists" or "List" operations — to ensure type-safe error unions, proper transactional boundaries, Value Object-only contracts, auth-first patterns, and Result-based error handling.** Includes references to working examples (Create, List, Exists patterns). Depends on 'repositories' skill.
[Pragmatic DDD Architecture] Guide for creating tests. Use when creating unit tests, integration tests, or understanding test conventions. Covers our tightly coupled stack: Vitest (unit, integration, ui projects), file naming, transactional database integration tests (txTest) with testcontainers/node-postgres/drizzle, mock patterns (createMock*RepoWithAssertions), and neverthrow Result assertions.
VeChainThor node internals — architecture, consensus (PoA/PoS/BFT), built-in contracts, REST API, storage, P2P networking, block production, transaction lifecycle, reward distribution, staking, and contributing to the Go codebase.
Grafana Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, caching, and HTTP API. Use when working with distributed traces, writing TraceQL queries, deploying Tempo, configuring trace pipelines, or setting up Grafana-Tempo integrations (traces-to-logs, traces-to-metrics, traces-to-profiles).