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Found 1,291 Skills
Loads the full ***plain language reference into context: syntax, section types (definitions, implementation reqs, test reqs, functional specs, acceptance tests), concept notation, frontmatter (import/requires/required_concepts/exported_concepts), templates, linked resources, module model, and authoring best practices. Use whenever authoring, editing, reviewing, or debugging .plain files, or before invoking any other skill that reads or writes .plain content.
FastMCP Python framework for MCP servers with tools, resources, storage backends (memory/disk/Redis/DynamoDB). Use for Claude tool exposure, OAuth Proxy, cloud deployment, or encountering storage, lifespan, middleware, circular import, async errors.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
This skill should be used when the user asks for a cryptographer, cryptography review, help to choose a cipher (AES-GCM, ChaCha20-Poly1305, ECDH, RSA tradeoffs), key management, PKI design, TLS configuration, protocol security or handshake review, authenticated encryption, digital signature scheme design, post-quantum migration at architecture level, ProVerif or Tamarin modeling concepts, nonce reuse or IV misuse analysis, HKDF vs password hashing (Argon2), HSM or KMS usage patterns, secure randomness, side-channel and constant-time requirements, or cryptographic agility and algorithm deprecation—not general OWASP web app review only (information-security-engineer), secure coding checklists without crypto depth, Solidity or smart contract audits, blockchain wallet tracing, legal export classification, or shipping custom production crypto without design and review gates.
Analyzes Android apps to identify key user workflows for AppFunctions such as creating a note, playing media, or sending an automated or AI agent triggered message, voice commands, or system shortcuts, without needing to open the app UI. Generates Kotlin code to expose these workflows to the Android system, allowing agents to discover and execute them on-device. Also refines KDoc documentation to ensure AI agents correctly understand and use the provided functionality.
Analyze host/CPU overhead in TensorRT-LLM inference from nsys traces. Detect whether host overhead is the bottleneck using GPU idle ratio, host prep exposed ratio, and per-phase evidence. For regressions, isolate forward steps via allreduce/NVTX patterns, compare host operation breakdowns across versions, and identify scheduling or request-management overhead. Supports optional inter-kernel gap, eager-vs-graph, pattern mapping, and multi-rank straggler drill-down. Use standalone or within perf-analysis. Triggers: host overhead, inter-step gap, scheduling overhead, forward step isolation, nsys iteration analysis, NVTX breakdown, request management overhead, GPU idle, host bottleneck, host prep exposed, inter-kernel gap, bubble analysis, graph coverage, eager kernel, rank imbalance, straggler detection.
Secure browser SSO and OAuth2 authentication proxy that lets AI agents access authenticated APIs without exposing credentials
Enable AI agents to request payment credentials from Link wallets for secure purchases without exposing real card details
Desktop & Tauri app testing for AI agents — Tauri v2 + WebKitGTK in Docker (AppImage extraction, Gemini Computer Use, virtual display, DOCX export verification) plus Electron app automation (VS Code, Slack, Discord, Figma) via `agent-browser skills get electron`. Use when testing a Tauri desktop app (Cicero), Electron app, or any non-browser desktop UI. For web browser testing, see `browser-test-agent`.
Generates Insomnia collection export files from Express, Next.js, Fastify, or other API routes. Creates organized workspaces with request groups, environments, and authentication. Use when users request "generate insomnia collection", "export to insomnia", "create insomnia workspace", or "insomnia import".
OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries. Use when training, evaluating, exporting, quantizing, or running inference for a TAO OneFormer model. Trigger phrases include "train OneFormer", "universal segmentation", "task-conditioned segmentation", "panoptic / instance / semantic in one model".
Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images. Use when training, evaluating, exporting, or running inference for a TAO monocular depth model. Trigger phrases include "train monocular depth", "DepthAnything v2", "metric depth from single image", "monocular depth estimation".