Results for “coding-agent”

Design and review intuitive, scalable, maintainable HTTP APIs. Use this skill whenever the user wants to design a new REST API, review an existing API or spec, write OpenAPI 3.x definitions, or work with HTTP semantics (GET/POST/PUT/PATCH/DELETE), status codes, idempotency (Idempotency-Key), error envelopes (RFC 7807 problem+json), pagination, filtering, versioning, or API auth (API keys, OAuth2 client credentials, rate limits). Also trigger for "API design", "RESTful", "endpoints", "OpenAPI", "Swagger", "ReDoc", "contract testing", or GraphQL and gRPC design questions.

The English summary is being prepared. Showing the original description.

Decompose systems into microservices and apply canonical distributed-systems patterns. Use this skill whenever the user wants to split a monolith into services, design service boundaries, choose between microservices and a modular monolith, implement sagas or the outbox pattern, set up CQRS or event sourcing, configure an API gateway or BFF, add resilience (circuit breakers, retries, timeouts), or implement distributed tracing with OpenTelemetry. Also trigger for distributed transactions, service discovery, event-driven architecture, contract testing with Pact, canary releases, and idempotent consumers.

The English summary is being prepared. Showing the original description.

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

The English summary is being prepared. Showing the original description.

Build AI agents with LangChain v1 and LangGraph in Python. Use this skill whenever the user mentions LangChain, LangGraph, create_agent, init_chat_model, @tool, ToolRuntime, agent middleware, human-in-the-loop, structured output, MCP tools (langchain.mcp, MCPAdapter), RAG, multi-agent handoffs or subagents, checkpointers, Deep Agents (create_deep_agent) or Managed Deep Agents (mda, define_deep_agent). Also trigger for migrating from create_react_agent, LLMChain, AgentExecutor or langchain-mcp-adapters, stream_events, SummarizationMiddleware, and langchain-classic.

The English summary is being prepared. Showing the original description.

Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.

The English summary is being prepared. Showing the original description.

Plan, write, and maintain automated tests in any language, with worked examples in Python and JavaScript/TypeScript. Use this skill whenever the user wants to write or plan tests, apply TDD (red-green-refactor), structure unit tests with arrange-act-assert and given-when-then naming, choose between fakes, stubs, and mocks, set up dependency injection for testability, build integration tests with testcontainers, database seeding, or transaction rollback, write Playwright or Cypress end-to-end tests, use property-based testing with Hypothesis or fast-check, create pytest fixtures or factory_boy factories, review coverage or mutation testing, or triage flaky tests in CI. Also trigger for "test pyramid", "test trophy", "test isolation", "mocking", "snapshot tests", "golden files", "parametrized tests", "make the tests pass".

The English summary is being prepared. Showing the original description.

Opinionated constraints for building better web interfaces with coding agents. Use this skill whenever the user asks you to build, review, or fix a UI — landing pages, dashboards, components, or any frontend code — and whenever you write Tailwind CSS, motion/react animations, accessible component primitives (Base UI, React Aria, Radix), or review agent-generated interface code for quality, accessibility, and performance. Also trigger for "make the UI better", "review my interface", "clean up this component", or any frontend work in React, Next.js, or plain HTML/CSS.

The English summary is being prepared. Showing the original description.

Design relational database schemas (and choose when to go NoSQL) that stay maintainable and fast. Use this skill whenever the user mentions tables, DDL, entities and relationships, normalization (1NF/2NF/3NF), primary and foreign keys, UUID vs bigint IDs, indexes (B-tree, composite, covering, partial), EXPLAIN, constraints (CHECK, UNIQUE, exclusion), transactions and isolation levels, migrations (Alembic, Prisma, Flyway, expand-contract, backfilling), or SQL vs NoSQL (MongoDB, DynamoDB, Cassandra, graph databases). Also trigger for "design the database", "model this domain", "which database should I use", or writing ORM models and migration files for PostgreSQL, MySQL, SQLite, or SQL Server.

The English summary is being prepared. Showing the original description.

Use between coding-agent runs (Codex, Claude Code, Cursor, Aider, etc.) to stop the project from drifting, overbuilding, or accumulating dead weight. Forces a read-only audit that classifies every active item as KEEP / DELAY / REMOVE and ends with a single, unambiguous next action. Trigger on "audit before continuing", "what should I do next", "this project got too big", "between runs", "trim scope", "stop adding stuff", or any signal that the project feels noisy and the next step is unclear. Do NOT load for trivial one-line fixes, when the next action is already obvious, or when you're mid-run and just need to keep going.

The English summary is being prepared. Showing the original description.

Use when a coding agent (Claude Code, Cursor, Codex, Copilot CLI) burns through tokens or usage limits, sessions get slow or hit context limits, or the user asks to cut agent cost. Measures where the tokens go, then applies levers in order: session hygiene, always-loaded context (CLAUDE.md, skills, MCP), tool-output filtering, subagent delegation, output verbosity, and model/effort choice.

The English summary is being prepared. Showing the original description.

Use when turning GitHub issues or bug reports into reproducible coding-agent repair loops with sandboxed execution, focused tests, patch generation, and regression verification.

The English summary is being prepared. Showing the original description.

Reusable skill for coding agents to build mediahost integrations, API clients, scrapers, bulk downloaders, and media tools. Incorporates the unofficial Python wrapper, auth flow, endpoints, rate-limit handling, and cross-language patterns ...

The English summary is being prepared. Showing the original description.

Reusable skill for coding agents (Claude, GPT, etc.) to build, optimize, debug, and productionize AI agents and coding agents in Rust. Embeds Rig/ADK patterns, Tokio async architecture, tracing observability, performance techniques, erro...

The English summary is being prepared. Showing the original description.

Reconstruct workflow behind a past coding-agent artifact using local ax sessions/commits/skills/tool traces. Use when asked how X was built.

The English summary is being prepared. Showing the original description.

Ask which skill or flow fits your situation. A router over the user-invoked skills in this repo.

The English summary is being prepared. Showing the original description.

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

The English summary is being prepared. Showing the original description.

Route agent work through AccInt's MCP memory loop: retrieve prior outcomes, resolve frames, and close commitments with evidence.

The English summary is being prepared. Showing the original description.

Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.

The English summary is being prepared. Showing the original description.

Analyze pi (coding-agent) session JSONL and ego-benchmark-harness run artifacts to explain slow or failed individual tasks, compare execution-path differences between agents or browser tools, and count tool calls and error types. Use this skill whenever users ask to analyze a pi session, explain why a task was slow or failed, compare two runs, investigate an error, locate the session corresponding to tasks.jsonl, inspect an agent execution path, count tool calls, analyze a run ID, or perform any forensic or retrospective analysis of JSONL under ~/.pi/agent/sessions/ or agent-home/.pi/agent/sessions/.

The English summary is being prepared. Showing the original description.

Terminal-Interface, Desktop-App und IDE-Extension Doku lesen Der Open-Source AI-Coding-Agent [*] Mit über 195,000 GitHub-Stars, 950 Contributors und ü

The English summary is being prepared. Showing the original description.

Manage AI assistant configurations and peer-to-peer mesh network connections in the NV oOS Pro Toolkit. Covers assistant creation, model configuration, provider setup, peer discovery, mesh networking, cross-assistant communication, and detecting from a coding-agent session (Zed) which NV oOS assistants / MCP servers are enabled. Use when creating or editing AI assistants, configuring model providers, setting up peer connections, managing mesh network topology, debugging assistant behavior, or answering "is agent X available" / "what tools does this MCP have".

The English summary is being prepared. Showing the original description.

Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.

The English summary is being prepared. Showing the original description.

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

The English summary is being prepared. Showing the original description.

interview the user to design an agentic control loop (sensor, controller, actuator under disturbances) tailored to their codebase, then build it as locally-runnable components plus a scheduled coding-agent workflow

The English summary is being prepared. Showing the original description.