@vikingmute: handoff 最近用的越来越多了,https://github.com/mattpocock/skills/blob/733d312884b3878a9a9cff693c5886943753a741/skills/in-progress…
摘要
The article discusses using the 'handoff' skill from mattpocock/skills to compress long conversation contexts in Codex, improving response speed for extended tasks. The user recommends using handoff when context length significantly slows down the model.
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handoff 最近用的越来越多了,https://github.com/mattpocock/skills/blob/733d312884b3878a9a9cff693c5886943753a741/skills/in-progress/handoff/SKILL.md… 是目前我最高频使用的 skill 了,发现 Codex 上下文一长,反应速度明显下降的非常厉害,都不是界面卡顿,而是返回的速度,当然本身 GPT 模型本身上下文和其他相比比较小,所以对于长任务,到差不多 70% - 80% 我都会用 handoff 将当前对话压缩成一份 handoff 文件,然后新开一个 session 继续进行,速度要快很多,避免自动压缩。 我想他最新的 /goal 模式应该也是差不多基于相同的原理。
mattpocock/skills
Source: https://github.com/mattpocock/skills
Skills For Real Engineers
My agent skills that I use every day to do real engineering - not vibe coding.
Developing real applications is hard. Approaches like GSD, BMAD, and Spec-Kit try to help by owning the process. But while doing so, they take away your control and make bugs in the process hard to resolve.
These skills are designed to be small, easy to adapt, and composable. They work with any model. They’re based on decades of engineering experience. Hack around with them. Make them your own. Enjoy.
If you want to keep up with changes to these skills, and any new ones I create, you can join ~60,000 other devs on my newsletter:
Quickstart (30-second setup)
- Run the skills.sh installer:
npx skills@latest add mattpocock/skills
-
Pick the skills you want, and which coding agents you want to install them on. Make sure you select
/setup-matt-pocock-skills. -
Run
/setup-matt-pocock-skillsin your agent. It will:- Ask you which issue tracker you want to use (GitHub, Linear, or local files)
- Ask you what labels you apply to ticks when you triage them (
/triageuses labels) - Ask you where you want to save any docs we create
-
Bam - you’re ready to go.
Why These Skills Exist
I built these skills as a way to fix common failure modes I see with Claude Code, Codex, and other coding agents.
#1: The Agent Didn’t Do What I Want
“No-one knows exactly what they want”
David Thomas & Andrew Hunt, The Pragmatic Programmer
The Problem. The most common failure mode in software development is misalignment. You think the dev knows what you want. Then you see what they’ve built - and you realize it didn’t understand you at all.
This is just the same in the AI age. There is a communication gap between you and the agent. The fix for this is a grilling session - getting the agent to ask you detailed questions about what you’re building.
The Fix is to use:
/grill-me- for non-code uses/grill-with-docs- same as/grill-me, but adds more goodies (see below)
These are my most popular skills. They help you align with the agent before you get started, and think deeply about the change you’re making. Use them every time you want to make a change.
#2: The Agent Is Way Too Verbose
With a ubiquitous language, conversations among developers and expressions of the code are all derived from the same domain model.
Eric Evans, Domain-Driven-Design
The Problem: At the start of a project, devs and the people they’re building the software for (the domain experts) are usually speaking different languages.
I felt the same tension with my agents. Agents are usually dropped into a project and asked to figure out the jargon as they go. So they use 20 words where 1 will do.
The Fix for this is a shared language. It’s a document that helps agents decode the jargon used in the project.
Example
Here’s an example CONTEXT.md, from my course-video-manager repo. Which one is easier to read?
- BEFORE: “There’s a problem when a lesson inside a section of a course is made ‘real’ (i.e. given a spot in the file system)”
- AFTER: “There’s a problem with the materialization cascade”
This concision pays off session after session.
This is built into /grill-with-docs. It’s a grilling session, but that helps you build a shared language with the AI, and document hard-to-explain decisions in ADR’s.
It’s hard to explain how powerful this is. It might be the single coolest technique in this repo. Try it, and see.
A shared language has many other benefits than reducing verbosity:
- Variables, functions and files are named consistently, using the shared language
- As a result, the codebase is easier to navigate for the agent
- The agent also spends fewer tokens on thinking, because it has access to a more concise language
#3: The Code Doesn’t Work
“Always take small, deliberate steps. The rate of feedback is your speed limit. Never take on a task that’s too big.”
David Thomas & Andrew Hunt, The Pragmatic Programmer
The Problem: Let’s say that you and the agent are aligned on what to build. What happens when the agent still produces crap?
It’s time to look at your feedback loops. Without feedback on how the code it produces actually runs, the agent will be flying blind.
The Fix: You need the usual tranche of feedback loops: static types, browser access, and automated tests.
For automated tests, a red-green-refactor loop is critical. This is where the agent writes a failing test first, then fixes the test. This helps give the agent a consistent level of feedback that results in far better code.
I’ve built a /tdd skill you can slot into any project. It encourages red-green-refactor and gives the agent plenty of guidance on what makes good and bad tests.
For debugging, I’ve also built a /diagnose skill that wraps best debugging practices into a simple loop.
#4: We Built A Ball Of Mud
“Invest in the design of the system every day.”
Kent Beck, Extreme Programming Explained
“The best modules are deep. They allow a lot of functionality to be accessed through a simple interface.”
John Ousterhout, A Philosophy Of Software Design
The Problem: Most apps built with agents are complex and hard to change. Because agents can radically speed up coding, they also accelerate software entropy. Codebases get more complex at an unprecedented rate.
The Fix for this is a radical new approach to AI-powered development: caring about the design of the code.
This is built in to every layer of these skills:
/to-prdquizzes you about which modules you’re touching before creating a PRD/zoom-outtells the agent to explain code in the context of the whole system
And crucially, /improve-codebase-architecture helps you rescue a codebase that has become a ball of mud. I recommend running it on your codebase once every few days.
Summary
Software engineering fundamentals matter more than ever. These skills are my best effort at condensing these fundamentals into repeatable practices, to help you ship the best apps of your career. Enjoy.
Reference
Engineering
Skills I use daily for code work.
- diagnose — Disciplined diagnosis loop for hard bugs and performance regressions: reproduce → minimise → hypothesise → instrument → fix → regression-test.
- grill-with-docs — Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates
CONTEXT.mdand ADRs inline. - triage — Triage issues through a state machine of triage roles.
- improve-codebase-architecture — Find deepening opportunities in a codebase, informed by the domain language in
CONTEXT.mdand the decisions indocs/adr/. - setup-matt-pocock-skills — Scaffold the per-repo config (issue tracker, triage label vocabulary, domain doc layout) that the other engineering skills consume. Run once per repo before using
to-issues,to-prd,triage,diagnose,tdd,improve-codebase-architecture, orzoom-out. - tdd — Test-driven development with a red-green-refactor loop. Builds features or fixes bugs one vertical slice at a time.
- to-issues — Break any plan, spec, or PRD into independently-grabbable GitHub issues using vertical slices.
- to-prd — Turn the current conversation context into a PRD and submit it as a GitHub issue. No interview — just synthesizes what you’ve already discussed.
- zoom-out — Tell the agent to zoom out and give broader context or a higher-level perspective on an unfamiliar section of code.
- prototype — Build a throwaway prototype to flesh out a design — either a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.
Productivity
General workflow tools, not code-specific.
- caveman — Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler while keeping full technical accuracy.
- grill-me — Get relentlessly interviewed about a plan or design until every branch of the decision tree is resolved.
- handoff — Compact the current conversation into a handoff document so another agent can continue the work.
- write-a-skill — Create new skills with proper structure, progressive disclosure, and bundled resources.
Misc
Tools I keep around but rarely use.
- git-guardrails-claude-code — Set up Claude Code hooks to block dangerous git commands (push, reset –hard, clean, etc.) before they execute.
- migrate-to-shoehorn — Migrate test files from
astype assertions to @total-typescript/shoehorn. - scaffold-exercises — Create exercise directory structures with sections, problems, solutions, and explainers.
- setup-pre-commit — Set up Husky pre-commit hooks with lint-staged, Prettier, type checking, and tests.
相似文章
@mattpocockuk:本周两项新技能!- /handoff 将您的当前会话整理为 Markdown 文件 - /prototype 帮助您构建原型……
Matt Pocock 为其工具宣布了两项新技能:/handoff 可将会话整理为 Markdown,/prototype 适用于 UI 或后端原型设计,此外还有其他更新。
mattpocock/skills
该开源仓库提供了一套可组合的 AI 代理技能与提示词,专为 Claude Code 和 Codex 等编程助手打造,旨在提升模型对齐效果、减少冗长输出,并优化整体工作流。
@rwayne: Context Mode 解决了 AI Agent 的另一半上下文问题:工具输出沙箱化 + 会话持久化。 56 KB 的 Playwright 快照压缩到 299 字节,98% 的数据不进上下文。每次文件编辑、Git 操作、任务决策都存入…
Context Mode is a tool that solves AI agent context problems by sandboxing tool outputs and persisting sessions, achieving up to 98% compression of Playwright snapshots and using BM25 retrieval to reduce context window usage. It supports 15 platforms including Claude Code, Gemini CLI, VS Code Copilot, and is used by major tech companies.
@yihui_indie: 向大家讨教两个问题: 1. 你是如何管理日益增长的 SKILL 的? 2. 大家是如何同步 Claude Code 和 Codex 的 SKILL 的?有没有什么好的同步方案?
用户询问关于如何管理以及同步 Claude Code 和 Codex 技能(SKILL)的方案。
@gyro_ai: https://x.com/gyro_ai/status/2055198700016660826
Matt Pocock 开源了 Skills for Real Engineers,一套小、可组合、可破解的 AI 编程技能,旨在解决 AI 编程中的理解偏差、缺少共享语言、反馈回路缺失和软件熵问题。该工具通过 grill-with-docs、tdd、diagnose 等技能提升 AI 编程效率,并提供了完整工作流。