Writing Agent Skills, finding useful Skills, and troubleshooting Skills that do not take effect.
In February I hooked up 11 MCP Servers to my coding agent. Just the tool list in an empty session ate up 34,000 tokens, with Datadog alone contributing over two hundred tools. The agent got slower and kept picking the wrong tools.
Why it matters: I'm writing this up because 11 MCP Servers of my own blew up my context, and it showed me that tools and knowledge belong in different containers.
AI Hero Skills v1.3 is out, adding three skills—/implement-spec, /pr, and /retro—and extending the main flow from /grill-with-docs → /to-spec → /to-tickets into implementation, PRs, and retros.
Why it matters: The author rounds out the skill set into a full path from writing specs to PRs and retros, and lays out the trade-offs at each step along with the rough edges they already know about.
宝玉@doteySelectedAI score8282i had a lot of fun chatting with @mattpocockuk today about how i was able to land 2,500 PRs last month! Matt is a wonderful interviewer so i think the interview turned out really interesting both of our skill plugins work great together, so i recommend giving both a try and picking the best skills that suit your workflow https://www.youtube.com/watch?v=MN9dGgmLyso
Why it matters: Using Lauren Tan's practice of merging 2500 PRs in a month, Baoyu explains that skipping individual reviews rests on validation Skills and rule constraints, and lays out the conditions under which he'd apply the same approach.
In an official blog post, OpenAI lays out recommendations for adjusting Skills, AGENTS.md, and task prompts under GPT-6 Astra: Skill descriptions should be as short as possible and state clearly when they apply, and multi-flow Skills should use a root document for minimal routing instead of turning the Skill into an overly specific step-by-step checklist.
Why it matters: OpenAI has published guidance on cleaning up Skills, AGENTS.md, and prompts under GPT-6 Astra, and it carries over to existing repository setups.
文章从 Claude Code、Codex 和 Gemini CLI 的官方文档中整理出四种提示词模式：先计划再编辑、给智能体一个可运行的检查、让智能体反过来访谈你、把反复重打的提示词存成文件，并给出各家对应的命令、参数和文件格式。
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Why it matters: 横向对照 Claude Code、Codex、Gemini CLI 三家文档，给出计划模式、可运行检查、访谈式提问和保存提示词四种模式的命令与文件格式。
Cursor engineer Lauren Tan shares how she gets AI agents to submit and merge PRs on their own: the key is validation—letting the agent run code, capture CPU traces, and open an iOS simulator to check its own work.
Why it matters: Cursor engineers break trust in AI agents down into reusable validation skills, feature maps, and evals, so readers can build their own automated validation workflows.
Author Ryan Lopopolo argues that an agent is a parameterized program built on top of a set of capabilities: models and configurations, reasoning and tool-call loops, computers, disks, context, Skills, tools, connectors, runtimes, network policies, identity, IAM, guardrails, I/O channels, and system prompts.
Why it matters: Drawing on his experience building multiple agents, the author proposes a platform architecture that decouples capability interfaces from their implementations — a useful reference for teams building Agent platforms.
Addy Osmani 建议每隔几周运行一次 Claude Code 的 /doctor，单独用 /memory 检查记忆，并让每条指令重新证明自己的价值，因为模型、harness 和代码库都在变，旧配置会留下。
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Why it matters: 作者结合自身配置审计经验与近期研究，说明 Agent 配置文件为何会腐化，以及如何按节奏清理。
The Cline team built a code review agent with the Cline SDK, splitting review into two agent loops—review and judge—then using a driver script to batch-submit the surviving issues as a single COMMENT event to the GitHub PR.
Why it matters: A full breakdown of the plugin, Hooks, and two-stage loop behind a code review agent, transferable to other automated review scenarios.
Baruch Sadogursky 与 Patrick Debois 在 InfoQ 演讲中用 Claude Code 现场演示：把全部项目文档塞进 CLAUDE.md 后，给接口加错误处理会因约定冲突返回 500，改用按描述懒加载的 Skill 后同一提示词通过测试。
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Why it matters: 两位作者用现场演示拆解上下文工程的四类反模式，并给出 Skill、检索通道、外部记忆与评测的对应做法。
Vercel ships v0 API, giving programmatic, headless access to the v0 app-generation agent: send a prompt, v0 generates an app, spins up a dev server in the Vercel Sandbox, and returns a preview URL you can embed in your own UI. The API is now generally available.
Why it matters: v0 opens up its app-generation capability as an API, so readers can judge how to wire it into their own product or agent workflow.
The OpenAI Codex Cookbook lays out a set of repository conventions for wiring Codex into your development process: use AGENTS.md for persistent repository instructions, PLANS.md as the source of phase plans, and split the work into phased build files under harness/build/, with each phase spelling out its goals, acceptance criteria, boundaries, and approval gates.
Why it matters: OpenAI lays out a complete directory convention for constraining Codex with AGENTS.md, PLANS.md, and phased build files—one you can adapt to your own repository.
Thariq Shihipar, a member of Anthropic's engineering team, wrote up the new context engineering rules for Claude 5, saying the team has cut over 80% of the system prompt from Claude Code for models like Claude Opus 5 and Claude Fable 5, with no measurable loss on coding evals.
Why it matters: Anthropic lays out the new context engineering rules for Claude 5 and explains how to trim the system prompt, CLAUDE.md, and Skills.
AI Hero's skills repo ships v1.1, renaming /to-prd to /to-spec, merging /to-plan and /to-issues into /to-tickets, and adding new Skills like /wayfinder, /research, and /prototype.
Why it matters: The author walks through the full Skill flow from grilling to deployment and gives the migration commands for the renames, the merge, and the new /wayfinder—useful for anyone building an AI development workflow.
The Claude Code team defines an AI agent loop as repeatedly running a work cycle until a predefined stopping condition is met, and splits it into four types—turn-based, goal-oriented, time-based, and proactive—along four dimensions: what triggers it, how it stops, the underlying instructions it uses, and the tasks it fits.
Why it matters: The Claude Code team breaks loops into four types—turn-based, goal-oriented, time-based, and proactive—and lays out how each is triggered, how it stops, and how tokens are controlled.
AI Hero 的 skills 目录发布 v1，通过在各 Skill 上启用 disable-model-invocation: true，让 Skill 描述不再进入模型选择 Skill 时查看的上下文窗口，Skill 描述的 token 成本降低 63%。
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Why it matters: v1 用 disable-model-invocation 把 Skill 描述移出上下文窗口，并区分用户调用与模型调用，读者可据此判断自己的 Skill 组织方式。
Inside Claude Code, Anthropic has already built up hundreds of Skills in active use. The team sorts them into nine categories—library and API references, product validation, data fetching and analysis, business process automation, code scaffolding, code quality and review, CI/CD and deployment, runbooks, and infrastructure operations—and notes that the best Skills should fall cleanly into one of them.
Why it matters: Anthropic’s internal framework for categorizing hundreds of Skills, along with its experience writing them, can carry over to a team building its own Skill library.
Lovable 上线 Skills 功能，把重复交代的工作方式写成可复用的 markdown 技能文件，在相关任务出现时按需加载。技能以文件夹形式组织，主文件 SKILL.md 含 name、description 和 instructions，description 是决定是否触发的唯一依据，支持文件只在主文件引用且确实需要时才加载。
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Why it matters: 官方详解 Lovable Skills 的文件结构、触发机制与写法，并给出可对照的正反示例。
Prime Radiant 发布两款新技术的研究预览：Greenfield 把现有软件（代码库、文档、API 客户端等）转成行为规格语料，Iterative Development 则是一套基于 Superpowers 的智能体方法论，把大规格拆成需求、打包成开发 epic 后交给编码智能体实现。
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Why it matters: Prime Radiant 公开两套工具的研究预览，读者可了解从旧代码库提取行为规格再驱动智能体重建产品的思路。
Anthropic 的 Thariq Shihipar 复盘了 Claude Code 工具设计中的取舍，核心主张是工具要贴合模型自身能力，而判断能力边界只能靠观察输出和反复实验。
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Why it matters: Anthropic 工程师复盘 Claude Code 工具设计的取舍，给出可迁移到自建智能体的判断方法。