标签：#skills

🇪🇸 Leer este post en Español I spent months watching my agent re-solve the exact same problems, over and over, because I never sat down and wrote them up once so anyone else could reuse them. That's the kind of technical debt nobody ever puts on a roadmap. So I published alpha-skills : eight installable Agent Skills and my first MCP server on npm . Where the published skills live The installable catalog lives in skills/ , split into three categories: external/ for third-party APIs, local/ for homelab and workflows, and general/ for cross-project utilities. All three are public: one skill in external/ , one in local/ , and six in general/ . local/ describes the use case. skills/ ├── external/ │ └── nextdns-api/SKILL.md ├── local/ │ └── progressive-search/SKILL.md └── general/ ├── agent-context-generator/SKILL.md ├── nestjs-iam-patterns/SKILL.md ├── nestjs-advanced-patterns/SKILL.md ├── nestjs-graphql/SKILL.md ├── tuning-claude-code/SKILL.md └── obsidian-second-brain/SKILL.md <DIAGRAM 02: 02-public-skills-structure-en.png> The eight skills Three categories: external/ for third-party services, local/ for homelab infrastructure and personal workflows, general/ for cross-cutting utilities that don't depend on any one service. Skill Category Use it for nextdns-api external NextDNS API progressive-search local Code and documentation search agent-context-generator general Project context nestjs-iam-patterns general Authentication and permissions nestjs-advanced-patterns general NestJS internals and architecture nestjs-graphql general Code-first and schema-first GraphQL tuning-claude-code general Claude Code configuration obsidian-second-brain general Note organization and review Each command installs one skill. Run the command for the one you need. 1. nextdns-api A full reference for the NextDNS REST API: profiles, security/privacy/parental-control settings, denylist and allowlist management, analytics, query logs. This is the one the MCP server below is built directly agai

🇺🇸 Read this post in English Llevo meses haciendo que mi agente resuelva los mismos problemas una y otra vez porque nunca me tomé el tiempo de escribirlos una sola vez, bien, y dejar que otros los reusaran. Ese es exactamente el tipo de deuda técnica que nadie pone en un roadmap. Así que publiqué alpha-skills : ocho Agent Skills instalables y mi primer servidor MCP en npm . Dónde están las skills publicadas El catálogo instalable vive en skills/ , separado en tres categorías: external/ para APIs de terceros, local/ para homelab y flujos de trabajo, y general/ para utilidades transversales. Las tres son públicas: una skill en external/ , una en local/ y seis en general/ . local/ describe su ámbito de uso. skills/ ├── external/ │ └── nextdns-api/SKILL.md ├── local/ │ └── progressive-search/SKILL.md └── general/ ├── agent-context-generator/SKILL.md ├── nestjs-iam-patterns/SKILL.md ├── nestjs-advanced-patterns/SKILL.md ├── nestjs-graphql/SKILL.md ├── tuning-claude-code/SKILL.md └── obsidian-second-brain/SKILL.md Las ocho skills Tres categorías: external/ para servicios de terceros, local/ para infraestructura de homelab y workflows propios, general/ para utilidades transversales que no dependen de ningún servicio en particular. Skill Categoría Para qué sirve nextdns-api external API de NextDNS progressive-search local Búsqueda de código y documentación agent-context-generator general Contexto de proyecto nestjs-iam-patterns general Autenticación y permisos nestjs-advanced-patterns general Internals y arquitectura de NestJS nestjs-graphql general GraphQL code-first y schema-first tuning-claude-code general Configuración de Claude Code obsidian-second-brain general Organización y revisión de notas Cada comando instala una skill. Ejecuta el de la que necesites. 1. nextdns-api Referencia completa de la API REST de NextDNS: perfiles, seguridad/privacidad/control parental, listas de bloqueo y permitidas, analíticas, logs de consultas. Es la que respalda al MCP server que desc

AI agents are good at interpreting goals, but prose instructions are a weak place to enforce exact rules. If a skill says "keep the commit subject short" or "never commit without approval," an agent can still misunderstand the boundary. The open-source how-to-create-a-skill-tutorial shows a practical split: let the agent make judgments, and let small local scripts validate repeatable rules. This tutorial builds the smallest useful version of that pattern: a commit-crafter skill with a SKILL.md file, a Python validator, and tests that run with the Python standard library. TL;DR An Agent Skill is a directory containing at least SKILL.md . Put the workflow and safety boundaries in that file. Put exact validation in a script. Keep the script deterministic, return meaningful exit codes, and run it before presenting the result to a user. The finished repository's example skill validates Conventional Commit messages. You can copy the same structure for release notes, config generation, research reports, or any other workflow with rules that can be checked mechanically. Prerequisites You need: Python 3.12 or newer for the repository's CI example. Git if you want the skill to inspect staged changes. An agent that supports the Agent Skills directory convention. A shell. The commands below use POSIX syntax; the files themselves are also designed for Windows. The project has no stable release tag at the time of writing. The examples and commands below are checked against the current main branch. Read the Agent Skills specification if your client uses a different discovery directory. 1. Create the skill directory The repository documents two useful scopes. A personal skill belongs in your user skills directory. A project skill belongs in the repository so a team can review and install it with the project. mkdir -p .agents/skills/commit-crafter/scripts mkdir -p .agents/skills/commit-crafter/references The required layout is simple: commit-crafter/ |-- SKILL.md |-- scripts/ | `--

Tired of explaining the same things again and again to your AI Agent? Frustrated because the AI keeps forgetting minute things custom to your codebase which needs to be kept in mind in each change? This is the current scenario for most people using AI agents to build their software. You handoff a task to it, it gives back the solution but misses something. You explain that to it, it nods back and then does it again. I myself did it until i came to know about Skills. What are Skills? Remember the CONTRIBUTING.md file we find in almost every open source repository? The file which explained anyone coming to the repo what to check, understand and keep in mind when contributing to it so that you don’t break it. The Skills works like that for any AI Agent who is going to make changes in your codebase. Its a folder that your AI checks anytime it needs to perform a specific task, specialized jobs or multi-step workflows without requiring you to prompt every time. And the best thing is, it follows an open standard that works with almost every AI agent be it Claude Code, Cursor, Copilot and more. It follows a folder-based structure around a SKILL.md file containing YAML metadata about that skill and instructions for that in markdown. How to build a Skill? Skills can vary from simple instructions to multi-step workflows depending on your need and there are 3 ways (limited by my knowledge) to build a skill: Manually First you need to create a dedicated folder for your skill and place a SKILL.md file inside it. This file needs to have 2 things: YAML frontmatter for metadata( name & description ) Instructions in markdown. Below is a basic sample SKILL.md file for your reference: — - name: word-counter description: Counts the total number of words in a given text. — - Word Counter Instructions Take the user’s input text. Count the total number of words. Return only the final word count as a number. Using a generator/CLI It is a tooling interface (command-line or script) which can

I thought I was building a better planning engine. What I actually built was a machine for showing...

Two weeks ago I watched an agent run a full product launch on Waitlister, my waitlist tool. It created the waitlist, generated and published a landing page, signed up a test address, checked the signup was real by fetching the public page unauthenticated, then unpublished and deleted everything it had made. Nobody touched the dashboard. The interesting part isn't that run. It's what I found while getting there, because almost none of it was visible from a browser. Why I bothered My users are pre-launch founders, which is exactly the group now building landing pages by prompting Claude, Cursor, or v0 instead of opening a site builder. "Add a waitlist to my site" is a normal thing to ask an agent to do, and increasingly nobody types my product name at all. The uncomfortable part is when an agent hits a 404 or installs a package that doesn't exist, it doesn't debug. It picks a different tool in the next sentence and never tells the user it switched. You lose without ever seeing a bounce. What I shipped, in order of how much it turned out to matter Full API coverage for the whole job. Not most of it. More below, because this one was worth the other five combined. A skill.md route. One page written for an agent rather than a person: a decision tree (no API key yet, go this way; account key, go that way), both code paths, and a self-check at the end so the agent can confirm it worked. An OpenAPI spec at a fixed URL. Valid 3.0.3 at /openapi.json , all five endpoints, auth, rate limits, error shapes. Endpoint changes update the spec and the SDK types in the same PR or they don't merge. A real npm SDK , plus four aliases under the names an agent is likely to reach for. An MCP server , 14 tools, so agents that speak MCP get typed calls instead of reading my docs. llms.txt and llms-full.txt , an index of the docs in plain text with a short block at the top saying what this product is and where the golden path starts. What was broken Honest list. The file I wrote for agents was

Developers are owning more of the delivery system around code, not just code itself. Join us during GitHub Universe to meet other devs, learn something new, and explore what's next. The post From coder to orchestrator: How agents shift the role of a developer appeared first on The GitHub Blog .

You know the moment. You've handed Claude Code (or Codex, or Cursor, or whatever you're driving today) a real task , not "fix this typo," but "refactor the auth middleware and update the tests." It goes off and works. Even if you've turned off the "ask before edit" prompts and let it run end to end without interruptions, you still want to check in to see what it actually did, watch it reason through a tricky part, catch something before it goes further than you meant. That can take minutes. And in those minutes, you're either sitting there watching a terminal scroll, or you wander off and genuinely forget to come back and check. Nobody was watching. That's the whole problem in one sentence: coding agents got capable enough to work unattended for real stretches of time, but the interface is still "stare at a terminal or forget about it." So, what is SKI SKI is a free desktop app that gives your coding agent a voice - in both directions. You talk to Claude Code, Codex, Cursor, Gemini CLI, and a growing list of other agents by speaking instead of typing. And this is the part that actually changes the workflow - you hear them reply out loud. When the agent finishes a task or hits a decision point, it says so: "Tests pass, want me to ship it?" You don't have to be staring at the terminal to catch that. You can be in another window, another room, or heads-down on a different project entirely. It lives as a small ambient widget on your desktop - a floating pill, or, if you're on a MacBook, a notch bar docked right under the camera housing , you hold a key to talk, and everything - speech-to-text and text-to-speech both runs locally on your machine. Why "voice for coding agents" and not just "voice dictation" This distinction actually matters, and it's the one thing I'd want you to walk away with if you read nothing else. Dictation tools have existed for a while, and good ones too, they turn your speech into text and drop it wherever your cursor is. That's genuinely useful,

AI agents don't usually become unreliable all at once. They degrade quietly. One session the agent...

If you have ever pointed a coding agent at a multi-day goal, you know the failure mode. It is not that the model writes a bad function. It is that on turn 40, the agent no longer remembers what the objective was, which decision you already made, what is out of scope, or what the last run actually proved. The context window rolled over, and the plot went with it. LoopX is an attempt to fix that specific problem. It calls itself "loop engineering for long-running AI agents," and it is a local control plane that sits above your agent runtime rather than replacing it. The one-sentence version Your agent (Codex, Claude Code, Cursor, whatever) executes bounded loops. Something (a heartbeat, a cron job, you hitting enter) triggers the next loop. LoopX holds the state that has to survive between those loops. The project draws the separation like this: Layer Role Codex / Claude Code / Cursor Execute a bounded agent loop: read, write, run commands, respond Goal mode / automation / CLI / TUI Trigger or schedule the next loop LoopX Preserve goals, gates, todos, run history, quota, evidence, handoff state That third row is the whole product. LoopX is not an executor and not an autonomous production controller. It is a state kernel with a CLI. Why "just use a todo file" isn't enough A TODO.md plus a long system prompt gets you surprisingly far. It falls over once any of these become true: The goal changed halfway through, and nothing recorded why . A decision genuinely needs a human, and that request evaporated into a chat message nobody read. Two agents are touching the same repo and neither knows who owns what. The last run claimed success, and there is no artifact proving it. Some work is safe and read-only, some crosses into writes, production, or private data, and the distinction lives only in your head. LoopX makes those things explicit and machine-readable, which is what lets a loop run longer without becoming less accountable. The concepts, in plain English Lifetime goals

If you've experimented with AI-driven testing, you've probably lived this cycle: you ask an AI agent to "test the checkout flow," and it does something — clicks around, declares success, and leaves you unsure what was actually verified. The next day you ask again and it does something different. The browser automation works; the testing discipline is missing. That gap is what Snagly is for. Rather than describe it, I pointed it at softwaretestingtrends.com — my own production site, nothing fixed beforehand — and recorded the whole thing. It found eleven issues, including a critical accessibility bug on my own signup page. One of its findings turned out to be wrong, and I'll come back to that, because it matters more than the ones it got right. 📺 Watch the full walkthrough — installed from an empty folder, run against production, ~20 minutes. What it is Snagly is a free, MIT-licensed set of 30 skills for AI coding agents — GitHub Copilot , Claude Code , Cursor, Codex and 70+ others — that turn "an AI that can drive a browser" into "an AI that tests like a QA professional." A skill, if you haven't met them yet, is a reusable instruction set that teaches the agent a specific working method — when to use it, what rigor it requires, what evidence to capture, and what it must never do. Each skill in Snagly has one job, and they hand off to each other the way a real testing practice does: start-testing is the front door — say "what can you test here?" and it routes you to the right skill, checking prerequisites before handing off. Discovery & strategy : scenario-mapper explores your site and produces a prioritized list of test scenarios; test-case-writer expands any of them into a reviewable spec; test-plan sets strategy, cadence, and release exit criteria; qa-onboarding writes the guide for your next hire. Execution : flow-runner drives real user journeys step by step, asserting outcomes (not just that clicks happened) and capturing evidence the moment anything fails. cru

In June 2026, Peter Steinberger reported that his system spent $1,305,088.81 over 30 days and...

Two weeks ago, I published "Why Agent Evaluation Is Harder Than Model Evaluation." The core argument:...

Learn how I modernized an old codebase of mine using stacked sessions and pull requests in the GitHub Copilot app. The post Stacked sessions and pull requests in the GitHub Copilot app appeared first on The GitHub Blog .

Eighteen months ago, MCP was the thing. Every demo and chatbot connector was running on MCP under the...

Here's a problem every agent builder eventually runs into: your assistant needs to be good at a lot of things. Writing SQL. Composing emails. Debugging code. Maybe more later. So where do all those instructions go? The obvious answer is: cram them all into the system prompt. But that "obvious" answer causes real problems as your agent grows. In this post, I'll walk through those problems first, then show how the skills pattern in LangChain fixes them, using a small working example. The problem: one giant prompt Let's say you want a single assistant that can write SQL, draft emails, and help debug code. The naive approach is one big system prompt: You are a helpful assistant. When writing SQL: - Write queries using only SELECT, INSERT, UPDATE, DELETE - Always use parameterized queries to prevent SQL injection - Format SQL keywords in UPPERCASE - Include brief comments for complex queries When writing emails: - Keep emails concise and professional - Use clear subject lines - Structure: greeting -> purpose -> details -> call to action -> sign-off - Adjust tone based on context When debugging: - First reproduce the error - Check logs and error messages - Isolate the root cause - Suggest a fix with explanation ... This works fine for three domains. But keep adding more — legal reviewing, data analysis, customer support scripts, code review standards — and you run into a few concrete issues: 1. Prompt bloat. Every single request pays the token cost of every domain's instructions, even if the user only asked about SQL. That's slower and more expensive for no benefit. 2. Instructions start to blur together. When the SQL rules, the email rules, and the debugging rules all sit in the same block of text, the model has to sift through everything at once. It's easy for instructions from one domain to bleed into another, or for the model to lose track of which rule applies where. 3. It doesn't scale. Every time you want to add a new specialty, you're editing one long, increasingl

Say "fable-mode" (or "operate carefully," or "high-judgment mode" or "think like fable") in a Claude Code or Codex session, and one line buried in the skill file does most of the work: "Never promote 'plausible' to 'confirmed' in your summary." That single rule is the whole philosophy in miniature — don't let something that merely looks right pass as something that was checked. Fable-mode is not a tool, a model swap, or a set of new commands. It's a behavioral overlay — a skill file at ~/.claude/skills/fable-mode/SKILL.md or ~/.codex/skills/fable-mode/SKILL.md or any other agent you use, that gets loaded into the turn and changes how the agent scopes work, verifies its own claims, and reports results. Nothing about what Agents can do changes. What changes is the discipline around using it. What actually changes The skill is organized into seven blocks, and each one targets a specific failure mode that shows up in normal agent use: Judgment before action. The first move is classification: is this a question, a problem description, or a change request? For the first two, the deliverable is an assessment — investigate and stop, don't start editing files nobody asked you to edit. Irreversible actions (deletes, pushes, overwrites) require a stop-and-confirm, and specifically: "before deleting or overwriting anything, actually look at the target." Verification as the non-negotiable core. This is the section that does the heavy lifting. Every "done" claim needs evidence produced after the last edit — a test run before your final change proves nothing about your final change. Claims get graded on a three-tier scale: Confirmed (you ran it and saw the result), Plausible (the code reads right but you haven't executed it), Assumed (inherited from the user's description). The rule is that you report the grade you actually earned, not the one that sounds better. Reasoning habits. Hold two hypotheses before committing to a diagnosis. Treat your memory of a library's API as a prior

If you work with a coding agent, count the things watching it right now. Linters. Git hooks. CI. Specs. A memory store. A rules file it's supposed to obey. Half a dozen systems, all making sure the agent builds the right thing the right way. Now count what keeps you oriented across the eleven things you have in flight. For most of us it's a markdown file we hope we remembered to update. We spent two years building harnesses for the agent and left our own work on the honor system. Map the tooling on two axes and that gap turns into a specific, hard-to-unsee hole. This post is the map. (This is part two of three. Part one, The AI orientation tax: it's missing context, not discipline , argued that the cost you're paying is a context bug rather than a character flaw. If you haven't read it, you don't need to. This one stands alone.) Two loops, not one The thing people lump together as "agent workflow" is really two loops at different altitudes: The execution loop: "is the agent building **this one task * right?"* Scope it, design it, write it, test it. One unit of work. The orientation loop: "do you and the agent share an honest picture of **what you're working on * across all the units?"* Capture, check state, prioritize, review, run over the whole board, daily and weekly. Almost every tool you've heard of lives in the first loop. That's not a criticism. It's just where the money and the visible pain were. But it means when people say "we solved agent memory" or "we solved context," they solved it for the execution loop. The orientation loop got left to you and a markdown file. You feel the difference the moment each one breaks. When the execution loop breaks, something yells: a failing test, a red build, a review comment. When the orientation loop breaks, nothing yells. The agent confidently re-suggests the thing you rejected yesterday. You rebuild a mental map you already had this morning. The only signal is a vague sense that you're moving slower than the tools prom

The cost of writing code dropped; the cost of owning it didn't. A framework for deciding which changes are actually cheap in the AI era. The post The cost of saying yes has changed appeared first on The GitHub Blog .

New to GitHub? This beginner's guide explains version control, repositories, and pull requests—plus everything else you need to start working confidently on GitHub. The post GitHub for Beginners: Your roadmap to mastering the GitHub essentials appeared first on The GitHub Blog .