Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.
Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules.
Use when refactoring into deep modules, choosing seams, classifying dependencies, deciding adapter strategy, or planning tests around a refactor.
4-layer recall procedure (durable → semantic → PostgreSQL → daily notes) before claiming "no context".
Deep directory traversal, dependency analysis, env var discovery, and architecture summarization. Returns partial JSON for the parent scanner to assemble.
Builds a rigorous test strategy with multiple test-suite variants and critical review. Use when the user asks what to test, wants adversarial coverage, or needs a stronger test…
Use when user requests deep analysis, thorough thinking, or detailed breakdown of a problem. Triggered by phrases like: 帮我深入思考, 请仔细分析, 帮我详细拆解, 请梳理一下思路, 仔细考虑, 深入理解, 详细分析, or…
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or…
Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop…
Use when growing an already-playable Godot game along a depth axis (systemic / content / run-meta) without regressing proven behavior.
Surface architectural deepening opportunities — find shallow modules and refactor them into deep ones using precise vocabulary (module, seam, adapter, depth, leverage, locality).
Use when discussing or working with DeepEval (the python AI evaluation framework)
Configure Deepgram CI/CD integration for automated testing and deployment. Use when setting up continuous integration pipelines, automated testing, or deployment workflows for…
Collect Deepgram debug evidence for support and troubleshooting. Use when preparing support tickets, investigating issues, or collecting diagnostic information for Deepgram…
Deploy Deepgram integrations to production environments. Use when deploying to cloud platforms, configuring containers, or setting up Deepgram in Docker/Kubernetes/serverless.
Execute Deepgram incident response procedures for production issues. Use when handling Deepgram outages, debugging production failures, or responding to service degradation.
Configure Deepgram local development workflow with testing and mocks. Use when setting up development environment, configuring test fixtures, or establishing rapid iteration…
Transcribes audio using the Deepgram Nova-2 API with diarization, punctuation, and smart formatting. Supports streaming via WebSocket and batch via REST with pre-recorded endpoint…
Execute Deepgram production deployment checklist. Use when preparing for production launch, auditing production readiness, or verifying deployment configurations.
Implement Deepgram rate limiting and backoff strategies. Use when handling API quotas, implementing request throttling, or dealing with 429 rate limit errors.
Implement Deepgram reference architecture for scalable transcription systems. Use when designing transcription pipelines, building production architectures, or planning Deepgram…
NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference…
Essential procedural knowledge and constraints for writing, debugging, and understanding the `at` programming language.
Development workflow for features, bugs, refactoring. Auto-activates for multi-file implementations. — from engineering/code-quality
Development workflow for features, bugs, refactoring. Auto-activates for multi-file implementations. — from engineering/workflow-orchestration
Production-grade defensive Bash scripting for server automation, monitoring, and DevOps tasks. Emphasizes safety, error handling, idempotency, and logging.
Expert knowledge for DeFi/MEV bot development including critical pitfalls, backtesting realities, AMM mechanics, MEV extraction strategies, and production failure modes
Manifest builder. Plan work, scope tasks, spec out requirements, break down complex tasks before implementation.
Generates folder structures, module contracts, middleware pipelines, and frontend/backend boundaries for TypeScript full-stack applications.
Capture deployment characteristics for both production and development — hosting, IaC, CI/CD, secrets, observability, local dev environment, containerization, hot reload, and seed…
Encode test, lint, build, and docs routines as named Python sessions so humans and agents run the same workflow every time.
Use when starting an initiative or when the request names a vague quality ("make it scalable / fast / secure / robust") with no bar — derive and RANK the architecture…
Use as the mandatory front door for ALL planning, brainstorming, design, and "let's build/add/change/plan X" work — including prompts that claim the problem statement is already…
Defines standard TypeScript interfaces for Appwrite Collections. Use when creating new models for Tours, Users, or Bookings to ensure full type safety.
Root-cause flaky or failing E2E tests from a specific CI run by downloading and analyzing the Playwright HTML report (traces, screenshots, errors).
Automatically gather flaky E2E tests from recent CI runs on the main branch and from recent PRs by wwwillchen/keppo-bot/dyad-assistant, then deflake them.
Generate a pre-flight delegation spec for any agent task -- context packages for memory-weak tools, review gates for opacity-prone tools, compounding checkpoints for stateless…
Delete a saved render profile by name. Use when the user says "delete the render profile", "remove my preset", or "clean up old render profiles".
Test-Driven Development patterns for testing AI deliberation features. Use when adding new deliberation features, adapters, convergence detection, or decision graph components.
Execute one stage from your master checklist: spec review, implementation, testing, and PR open.
Compare reply rates, bounce rates, and positive reply rates broken down by inbox type (SMTP / Gmail / Outlook) for a Smartlead account.
Use when designing cloud infrastructure, CI/CD pipelines, or deployment strategies
Expert delivery management for release planning, deployment coordination, incident response, change management, and SLA tracking across continuous delivery pipelines.
Generate a detailed test plan covering scenarios, environments, data, and reporting for the release.
Analyzes a Delphi / Object Pascal codebase to extract unit-level `uses` dependencies. Use when the user uploads a zip / archive of a Delphi project (or a folder of .pas / .dpr /…
Delta Lake テーブルの設計・最適化・運用を支援するスキル。 テーブル設計（Liquid Clustering、パーティション、Deletion Vectors）、 データ操作（MERGE最適化、CDF、Streaming）、 パフォーマンス（OPTIMIZE、VACUUM、Data Skipping）、 Medallion…
Use to run a zero-dependency sandbox loop that proves the loop machinery works end to end. Triggers on "run the demo loop", "test the loop", "quick loop test", "demo loop", "make…
Deno 2 development workflow including testing with permissions and JSR package management. Use when working with Deno projects, setting up new Deno applications, or when the user…
Deno integration. Manage data, records, and automate workflows. Use when the user wants to interact with Deno data.
Domain-Driven Design patterns and architecture for Deno TypeScript applications. Use when building complex business logic, implementing bounded contexts, or structuring…
Guidelines for developing with Deno and TypeScript using modern runtime features, security model, and native tooling
Use when designing or auditing dependency structure: package boundaries, runtime vs build dependencies, adapter layers, duplicate-purpose libraries, supply-chain risk, upgrade…
Check dependencies for known vulnerabilities using npm audit, pip-audit, etc. Use when package.json or requirements.txt changes, or before deployments.
Политика зависимостей: latest-compatible, source-backed, SLSA Level 2, SBOM, lockfile discipline. Используй для: зависимости, версии, обнови пакет, миграция версий, совместимость,…
TRIGGER when: adding or upgrading any dependency — library, SDK, framework, API, IaC API version (K8s/Terraform/Helm), CRD, or container image. Use BEFORE writing the call.
Review code for proper DI patterns using DryIoc. Ensures no static singletons, validates constructor injection and service lifetimes.
Manage Python dependencies using uv package manager. Use when installing, adding, removing, or updating packages. Always prefer uv over pip.
Master major dependency version upgrades, compatibility analysis, staged upgrade strategies, and comprehensive testing approaches.
Manage major dependency version upgrades with compatibility analysis, staged rollout, and comprehensive testing.
Upgrade dependencies for Java/Kotlin (Gradle/Maven) and TypeScript/Node projects with minimal risk: plan the bump, apply changes incrementally, run tests/builds, and document…