Topic · AI & LLM Engineering
Best Building AI agents skills, page 10
Building AI agents skills, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 433 | Marketing and promotion specialist for Claude ecosystem technology - MCP servers, skills, plugins, and agents. | curiositech/ | 243 | 1 repo | ~2.1k | Automated safety check: Notes | MIT | 1 mo ago |
| 434 | 434.AWS Harness Build a new AI agent on AWS and deploy it easily, OR wrap and deploy an agent you already have, using the Amazon Bedrock AgentCore harness. | hoodini/ | 281 | — | ~4.2k | Automated safety check: Notes | No licence | 2 mo ago |
| 435 | Boilerplate templates for Claude Code extensions. An agent skill from aiskillstore/marketplace. | aiskillstore/ | 430 | 1 repo | ~764 | Automated safety check: Pass | No licence | today |
| 436 | 436.Google Adk Best practices for building AI agents with Google's Agent Development Kit (ADK) in Python, covering agent design, tools, sessions, memory, artifacts, evaluation, and deployment. | Mindrally/ | 268 | — | ~2.5k | Automated safety check: Pass | Apache-2.0 | 1 mo ago |
| 437 | Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). | neo4j-contrib/ | 114 | — | ~4.2k | Automated safety check: Notes | MIT | yesterday |
| 438 | 438.Agent Creator Creates new specialized agents with frontmatter, tools, delegation. | softspark/ | 179 | — | ~1.3k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 439 | Build .NET AI agents, harnesses, and multi-agent workflows with Microsoft Agent Framework using the right agent type, sessions, tools, workflows, hosting protocols, and enterprise guardrails. | managedcode/ | 486 | — | ~4k | Automated safety check: Pass | MIT | today |
| 440 | Build high-performing OpenClaw agents end-to-end with comprehensive safety features. | LeoYeAI/ | 2.2k | — | ~4.3k | Automated safety check: Notes | MIT | 2 mo ago |
| 441 | 441.Create Agent 创建新的 OpenClaw Agent 及其 workspace。包含四个阶段:信息收集、 workspace 构造、系统注册、重启验证。 | LeoYeAI/ | 2.2k | — | ~2.4k | Automated safety check: Pass | MIT | 2 mo ago |
| 442 | 442.Data Specialist 提供数据库设计、优化、数据工程和数据分析能力。当需要处理数据库操作、数据管道或数据分析时使用. An agent skill from Prorise-cool/Claude-Code-Multi-Agent. | Prorise-cool/ | 305 | — | ~1k | Automated safety check: Pass | No licence | 22 days ago |
| 443 | Scaffold production-ready AI agents on Google's Agent Development Kit (ADK): ReAct-style single agents, multi-agent orchestration (Sequential/Parallel/Loop), tool wiring, evaluation, and optional… | jeremylongshore/ | 2.8k | — | ~960 | Automated safety check: Pass | MIT | today |
| 444 | 444.Agent Creator Create production-grade agent .md files aligned with the current Anthropic subagent contract. | jeremylongshore/ | 2.8k | — | ~4k | Automated safety check: Pass | MIT | today |
| 445 | Wire LangChain 1.0 / LangGraph 1.0 tests into a GitHub Actions pipeline — unit tests with FakeListChatModel, VCR-gated integration tests, warning-filter policy, and eval-regression merge gates. | jeremylongshore/ | 2.8k | — | ~4.4k | Automated safety check: Pass | MIT | today |
| 446 | Paste-match catalog of 14 real LangChain 1.0 / LangGraph 1.0 exceptions with named causes and named fixes, plus a triage decision tree. | jeremylongshore/ | 2.8k | — | ~3.7k | Automated safety check: Pass | MIT | today |
| 447 | Works correctly with LangChain 1.0's typed content blocks on AIMessage.content — text, tooluse, image, thinking, document — across Claude, GPT-4o, and Gemini, including multi-modal composition and… | jeremylongshore/ | 2.8k | — | ~3.6k | Automated safety check: Pass | MIT | today |
| 448 | Compose LangChain 1.0 chains with RunnableParallel, RunnableBranch, RunnablePassthrough.assign, and RunnableLambda — correct input/output shapes, debug probes, and typed composition that catches… | jeremylongshore/ | 2.8k | — | ~4.3k | Automated safety check: Pass | MIT | today |
| 449 | Control LangChain 1.0 AI spend with accurate streaming token accounting, model tiering, provider-specific cache hit tuning, per-tenant budgets, and retry dedup. | jeremylongshore/ | 2.8k | — | ~4.9k | Automated safety check: Pass | MIT | today |
| 450 | Load and chunk documents for LangChain 1.0 RAG pipelines correctly — language-aware splitters, table-safe PDF loaders, Cloudflare-compatible web loaders, chunk-boundary strategies that survive… | jeremylongshore/ | 2.8k | — | ~4.1k | Automated safety check: Pass | MIT | today |
| 451 | Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astreamevents(v2) transcript, propagating callback stack… | jeremylongshore/ | 2.8k | — | ~4.6k | Automated safety check: Pass | MIT | today |
| 452 | Build a LangGraph 1.0 Deep Agent — planner + subagents + virtual filesystem + reflection loop — without the state-growth and prompt-inheritance traps. | jeremylongshore/ | 2.8k | — | ~4.9k | Automated safety check: Pass | MIT | today |
| 453 | Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager… | jeremylongshore/ | 2.8k | — | ~3.9k | Automated safety check: Notes | MIT | today |
| 454 | Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs. | jeremylongshore/ | 2.8k | — | ~2.6k | Automated safety check: Pass | MIT | today |
| 455 | Enforce tenant isolation and role-based access across LangChain 1.0 chains and LangGraph 1.0 agents — per-request retriever construction, tenant-scoped rate limits, role-scoped tool allowlists, and… | jeremylongshore/ | 2.8k | — | ~4k | Automated safety check: Pass | MIT | today |
| 456 | Build reproducible evaluation pipelines for LangChain 1.0 chains and LangGraph 1.0 agents — golden datasets, LangSmith evaluate(), ragas RAG metrics, deepeval LLM-as-judge, agent trajectory… | jeremylongshore/ | 2.8k | — | ~3.7k | Automated safety check: Pass | MIT | today |
| 457 | Triage LangChain 1.0 / LangGraph 1.0 production incidents — LLM-specific SLOs, provider outage runbook, latency spike decision tree, cost-overrun response, agent loop containment. | jeremylongshore/ | 2.8k | — | ~3.8k | Automated safety check: Pass | MIT | today |
| 458 | Build a correct LangGraph 1.0 ReAct agent with createreactagent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. | jeremylongshore/ | 2.8k | — | ~3.7k | Automated safety check: Pass | MIT | today |
| 459 | Build a correct LangGraph 1.0 StateGraph — typed TypedDict state with reducers, nodes, edges, compile, and recursion budgets — without hitting the silent-termination and state-replacement traps. | jeremylongshore/ | 2.8k | — | ~3.4k | Automated safety check: Pass | MIT | today |
| 460 | Persist LangGraph agent state correctly with MemorySaver and PostgresSaver — threadid discipline, JSON-serializable state rules, time-travel, schema migration. | jeremylongshore/ | 2.8k | — | ~3.9k | Automated safety check: Pass | MIT | today |
| 461 | Build LangGraph 1.0 human-in-the-loop approval flows with interruptbefore / interruptafter and Command(resume=...) — JSON-serializable state, clean resume semantics, and UI wiring for approval… | jeremylongshore/ | 2.8k | — | ~4k | Automated safety check: Pass | MIT | today |
| 462 | Pick the correct LangGraph 1.0 streammode ("messages" vs "updates" vs "values"), wire it into SSE or WebSocket without proxy-buffering gotchas, and filter astreamevents(v2) server-side before… | jeremylongshore/ | 2.8k | — | ~4k | Automated safety check: Pass | MIT | today |
| 463 | Compose LangGraph 1.0 subgraphs correctly — shared state key propagation, Send / Command(graph=...) dispatch, callback scoping, per-subgraph recursion budgets, and testing each subgraph in isolation. | jeremylongshore/ | 2.8k | — | ~4.1k | Automated safety check: Pass | MIT | today |
| 464 | Build a fast, deterministic local test loop for LangChain 1.0 / LangGraph 1.0 — FakeListChatModel fixtures, pytest config, VCR cassettes with key redaction, warning-filter policy. | jeremylongshore/ | 2.8k | — | ~4.1k | Automated safety check: Pass | MIT | today |
| 465 | Invoke Claude, GPT-4o, and Gemini through LangChain 1.0 without tripping on the content-block, token-accounting, and structured-output quirks that silently break production code. | jeremylongshore/ | 2.8k | — | ~2.7k | Automated safety check: Pass | MIT | today |
| 466 | Build reliable dev / staging / prod isolation for LangChain 1.0 services — Pydantic Settings + SecretStr, cloud Secret Manager in prod, per-env prompt and model version pinning, env-specific… | jeremylongshore/ | 2.8k | — | ~4.2k | Automated safety check: Notes | MIT | today |
| 467 | Wire LangSmith tracing and custom metric callbacks into a LangChain 1.0 chain or LangGraph 1.0 agent correctly — env-var spelling, subgraph propagation, per-tenant dimensions, cost and latency… | jeremylongshore/ | 2.8k | — | ~3.9k | Automated safety check: Notes | MIT | today |
| 468 | Wire LangChain 1.0 / LangGraph 1.0 traces into an OpenTelemetry-native backend (Jaeger, Honeycomb, Grafana Tempo, Datadog) with LLM-specific SLOs, safe prompt-content policy, and subgraph-aware span… | jeremylongshore/ | 2.8k | — | ~3.6k | Automated safety check: Pass | MIT | today |
| 469 | Tune LangChain 1.0 / LangGraph 1.0 Python chains and agents for throughput, latency, and cost — streaming modes, explicit batch concurrency, semantic plus exact caches, persistent message history… | jeremylongshore/ | 2.8k | — | ~3.5k | Automated safety check: Pass | MIT | today |
| 470 | Manage LangChain 1.0 prompts like code — LangSmith prompt hub versioning, XML-tag conventions for Claude, few-shot example selection, discriminated-union extraction schemas, and A/B test wiring. | jeremylongshore/ | 2.8k | — | ~4.4k | Automated safety check: Pass | MIT | today |
| 471 | Rate-limit LangChain 1.0 calls correctly across multi-worker deployments — Redis-backed limiters, asyncio.Semaphore, narrow exception whitelists, and provider-specific throttle handling. | jeremylongshore/ | 2.8k | — | ~4.4k | Automated safety check: Pass | MIT | today |
| 472 | A reference layered architecture for production LangChain 1.0 / LangGraph 1.0 services — LLM factory with version-safe defaults, chain/graph registry, retriever and tool DI, Pydantic-validated… | jeremylongshore/ | 2.8k | — | ~4.7k | Automated safety check: Notes | MIT | today |
| 473 | Compose LangChain 1.0 Python runnables with the production defaults the docs do not warn about: parallel batching, narrow fallbacks, and brace-safe prompts. | jeremylongshore/ | 2.8k | — | ~3.4k | Automated safety check: Pass | MIT | today |
| 474 | Migrate a LangChain 0.3.x Python codebase to LangChain 1.0 / LangGraph 1.0 without breaking production — named breaking changes, codemod patterns, and a phased rollout. | jeremylongshore/ | 2.8k | — | ~3.3k | Automated safety check: Pass | MIT | today |
| 475 | Dispatch LangChain 1.0 chain/agent events to external systems — webhooks, Kafka, Redis Streams, SNS — via async fire-and-forget callbacks, subgraph-aware wiring, and HMAC-signed delivery with… | jeremylongshore/ | 2.8k | — | ~3.9k | Automated safety check: Pass | MIT | today |
| 476 | Build and deploy generative AI agents on Vertex AI: Gemini model selection, RAG with grounded retrieval, function calling, multimodal extraction, evaluation, and Agent Engine deployment with… | jeremylongshore/ | 2.8k | — | ~898 | Automated safety check: Pass | MIT | today |
| 477 | 477.Context Engine Context management engine for AI coding agents. An agent skill from borghei/Claude-Skills. | borghei/ | 881 | — | ~2.2k | Automated safety check: Pass | MIT | today |
| 478 | Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. | borghei/ | 881 | — | ~2.1k | Automated safety check: Pass | MIT | today |
| 479 | Harness Engineering 第一阶段:扫描现有项目,生成 AGENTS.md(目录文件)和完整的 docs/ 知识库结构。 | simbajigege/ | 184 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | 1 mo ago |
| 480 | Harness Engineering 第一阶段第二步:深度分析项目代码,填充业务解决方案、架构、约定、技术决策和质量标准等 docs/ 知识库内容。 | simbajigege/ | 184 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | 1 mo ago |
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More topics in AI & LLM Engineering
- Deep learning415
- Embeddings386
- LLM inference and serving372
- Prompt engineering360
- Retrieval-augmented generation358
- Fine-tuning309
- LLM evaluation308
- Speech recognition and synthesis308
- Structured output and tool calling276
- LLM cost and token optimization259
- LLM API integration255
- Model routing and gateways255
- LLM observability240
- LLM guardrails221
- Computer vision203
- Model hubs and datasets180
- GPU and accelerator computing176
- Diffusion and image models166
- Natural language processing131
- Reinforcement learning66
- AI interpretability23