Agent Building skills

Browse reusable Agent Skills, each with a clear purpose and practical guidance.

building-agent-systems

AI agent and LLM system engineering reference covering single-agent dev (ReAct, tool calling, plan-execute), multi-agent coordination (swarm, role decomposition, file locking), LLM security (prompt injection, jailbreak defense, output filtering), RAG architecture (chunking, hybrid retrieval, rerank), and prompt engineering / evaluation (RAGAS, LLM-as-Judge). Use when building AI agents, designing RAG pipelines, orchestrating multi-agent workflows, hardening LLM apps, or writing prompts.

231 repo starsObserved in 1 repos

Agent Building

GitHub

cultivating-personas

Distills AI agent voice patterns from conversation into a Persona Voice Card v1.0 (self/user/language/register/emoji_policy/flourish only — no judgment-shaped fields), validates schema + content safety + differentiation, and routes contributions through the existing submission portal. Use when the user wants to crystallize a recurring voice/tone, fork an existing persona, or share one with the community.

231 repo starsObserved in 1 repos

Agent Building

GitHub

cultivating-skills

Distills repeated workflows into reusable skills, improves existing skills, and gates them through a three-tier publish funnel (local → project → community). Use when the agent or user notices a pattern worth crystallizing, when an existing skill is incomplete, or when something deserves community contribution. Default deny on dangerous tools and side effects.

231 repo starsObserved in 1 repos

Agent Building

GitHub

doctrine

The operating doctrine, self-contained in this folder. Invoke BEFORE - delegating work to a subagent via the Agent tool (picking model tier, writing the dispatch prompt) or handling a subagent that failed (escalate/de-escalate); deciding whether to retry, escalate, switch approach, or ask the user; reporting a nontrivial task complete (the done-gate); editing this doctrine or CLAUDE.md; recording a lesson about the harness.

231 repo starsObserved in 1 repos

Agent Building

GitHub

mutsumi-wakaba

若叶睦（Wakaba Mutsumi），《BanG Dream! It's MyGO!!!!!》+《BanG Dream! Ave Mujica》吉他手。 CRYCHIC 前吉他手，Ave Mujica 的 Mortis。具有双重人格切换机制。基于 50+ 来源的深度调研，提炼两种人格各 5 个核心行为模式、完整表达质感、人格切换规则。触发词：「睦」「若叶睦」「mutsumi」「若叶睦模式」「用睦的视角」「扮演若叶睦」「mortis」「墨缇丝」。

231 repo starsObserved in 1 repos

Agent Building

GitHub

orchestrating-adversarial-reviews

Multi-agent adversarial-verification orchestration for high-confidence conclusions. Fan-out finders, then verify every finding through a three-prism panel (exploitability / correctness / refutation) that defaults to disbelief, gate fixes behind load-bearing proof tests that catch agents who falsely claim "done/fixed", and roll out behind a build-first exit-code guard. Use when a fan-out task must produce trustworthy results — security audit, code review, research synthesis, migration — and a single agent's self-report cannot be trusted. Composes with securing-systems (what to look for) and shipping-changes (change closed loop); orchestration engine is the Workflow tool.

231 repo starsObserved in 1 repos

Agent Building

GitHub

spec-driven-dev-v2

Use when an agent will work for hours or days across many files and multiple vertical slices. Drives a three-level Project → Sprint → Task hierarchy with isolated per-task execution, a review round-loop, context packs for subagent reviewers, governance-as-code, and orchestrator-readable state. Designed for long-running drivers like /loop, autoresearch:ship, and goal-driven.

231 repo starsObserved in 1 repos

Agent Building

GitHub

taki-shiina

椎名立希（Taki Shiina），《BanG Dream! It's MyGO!!!!!》鼓手，原 CRYCHIC 成员。基于萌娘百科、Wikipedia、Bangumi、Fandom Wiki、Bilibili 等 10+ 来源的深度蒸馏，提炼 5 个核心行为模式、完整表达质感和角色扮演规则。触发词：「立希」「taki」「椎名立希模式」「用立希的视角」「扮演立希」「rikki」。

231 repo starsObserved in 1 repos

Agent Building

GitHub

andrej-karpathy

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

230 repo starsObserved in 2 repos

Agent Building

GitHub

andrew-ng

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

230 repo starsObserved in 2 repos

Agent Building

GitHub