“prompt engineering”

Design, manage, and optimize LLM prompts - prompt library, multi-model adaptation, output validation.

Authoritative, source-cited reference for prompt engineering and context engineering with current frontier LLMs (Claude Opus 4.7 / Sonnet 4.6, GPT-5.4 / 5.5…

Aplicar prompt engineering cuando el encargo requiera objetivo y contrato de resultado.

Reusable prompt templates, multi-step workflow chains, and quality gates for any MCP client.

Meerkat

llschmidt

A prompt engineer you talk to.

MCP server for AI-enhanced prompt engineering and request conversion.

Pup - A Prompt Engineering Toolkit

alimalik

Pup is a prompt engineering toolkit for LLMs.

Tokonomics — AI Token Optimizer & Prompt Cache Engine

tokonomics

Local multi-language context compiler, AST program slicer, prefix cache pinner, and air-gapped developer velocity/DORA engine for VS Code, Claude Code, and…

Prompta

padurets

Organize and compose AI prompts with reusable variables

Deterministic prompt-injection firewall, neural deobfuscation engine, EIP-712 typed signature auditor, and autonomous agent safety gateway on Base and Solana…

Agent-callable services from the Engineering Leaders Community: meetup topic evaluation, speaker stage placement, and community reach — grounded in 3,300+ CEE…

Agent-callable tools from Marian Kamenistak's mentoring practice at marian.coach: developer and engineering-manager salary models, a calibrated team-lead…

Collaboration layer for AI engineering teams: prompt management, agent observability and evals

Open-source framework for large language model evaluations, built by the UK AI Security Institute

Open-source agent harness engineering platform with OpenTelemetry tracing, evals and guardrails

Subagent "prompt-engineer" from PostHog/posthog: Use this agent when you need to create, refine, or optimize prompts for LLMs.

Subagent "instructions-writer" from VRSEN/agency-swarm: Write optimized agent instructions using prompt engineering best practices

AGENTS.md from EveryInc/compound-engineering-plugin: 277 lines covering Quick Start, Working Agreement, Directory Layout, Repo Surfaces and more.

Open-source framework that builds, evaluates and self-improves multi-agent LLM workflows from a goal.

Python framework for generative AI agents, pipelines and parallel workflows, with RAG engines, tools, memory and swappable drivers.

Python framework for customer-facing conversational agents that narrows the prompt context each turn using rules, knowledge and tools you define.