langchain-performance-tuning
Tune LangChain 1.0 / LangGraph 1.0 Python chains and agents for throughput, latency, and cost — streaming modes, explicit batch concurrency, semantic plus…
Find an Agent Skill for your task. Read the guide and copy the install command.
Updated 10-31 00:15
25,337 results
Tune LangChain 1.0 / LangGraph 1.0 Python chains and agents for throughput, latency, and cost — streaming modes, explicit batch concurrency, semantic plus…
Compose LangChain 1.0 Python runnables with the production defaults the docs do not warn about: parallel batching, narrow fallbacks, and brace-safe…
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when…
Invoke this skill when a switchable personality style layer needs to be stacked on top of a base identity. Typical scenarios include: providing multiple personality options for the same product (such as GPT-5.1's friendly/professional/quirky modes), designing a personality switching mechanism, and preventing personality from leaking into user content. Not applicable to: defining the AI's core role positioning (should use…
Extract a DDD-style ubiquitous language glossary from the current conversation, flag ambiguities, and propose canonical terms. Save to UBIQUITOUS_LANGUAGE.md. Use when the user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when…
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an…
Build your first Claude Agent SDK agent — TypeScript or Python. Covers installation, minimal agent loop, tool definitions, and the difference between…
Use Claude's 1M-token context window effectively — when to use it, how to structure inputs for recall, how to price it, and how to combine with prompt caching…
Use Anthropic's Managed Agents API (/v1/agents, /v1/sessions) — server-side agent runtime that handles the tool loop, compaction, memory, and scaling for you.…
Design MCP tool schemas, names, and descriptions that AI agents actually pick correctly and use without hand-holding. Covers the anti-patterns that make agents…
Set up continuous regression evals so model/prompt/tool changes don't silently break existing behavior. Covers gating thresholds, CI integration, statistical…
Delegate work to sub-agents via the Task/Agent tool — parallel research, isolated context windows, specialized expertise. Covers when sub-agents help vs hurt,…
Zero-account onboarding orchestrator for Latitude. Bootstrap a temporary Latitude account from the terminal (no signup), instrument the app or agent harness…
Partner.skill / Partner — two-direction cost-split workflow between Claude Code and Codex. Direction A (Codex-driven): Codex orchestrates and implements; Claude…
Access cloud storage (S3, GCS, Azure) in Python using fsspec, pyarrow.fs, or obstore. Includes DataFrame integrations (Polars, DuckDB, Pandas, PyArrow),…
Builds, manages, and troubleshoots WhatsApp bots using the BuilderBot Cloud (BBC) MCP Tool v2.3. Covers bot creation for businesses needing appointment…
Use to check whether Book Author Agent Skills has updates in its GitHub repository, compare the installed project version with the repository version, report…
Universal context reviewer: delegates arbitrary context (plans, decisions, documents, architecture proposals) to external agents (Codex + Gemini) for…
Bot platform architecture (Discord, Slack, Telegram, Feishu/Lark, QQ, WeChat). Use when working on inbound webhooks, Chat SDK message routing, agent execution…
Grill the user relentlessly about a plan, decision, or idea, maintaining the project's domain model (CONTEXT.md, ADRs) as decisions crystallise. Use when the…
Fetch library and framework documentation via context7-mcp and fetch-mcp for comprehensive documentation research with version-specific content.
Maintain Go repositories with high-quality standards, adhering to CI/CD, linting, and agent-friendly best practices.
Principles and a linter for writing code that AI coding agents can read and modify correctly. Use when writing, refactoring, reviewing, or structuring code…