# Agent Skills Agent Skills is an open format for extending AI agent capabilities with specialized knowledge and workflows, using folders centered on a SKILL.md file. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving HCLS AI reasoning with open-source agent skills DevFeed: [Improving HCLS AI reasoning with open-source agent skills]() Original publisher: [Read original article]() Author: Michael Hsieh Published: 2026-09-16T19:00:00Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Agent Skills](), [Artificial Intelligence](), [foundation-models](), [Open Source](), [Bioinformatics](), [Medical imaging]() Tags: [agent-skills](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-quick-suite](), [announcements](), [artificial-intelligence](), [foundation-models](), [generative-ai](), [healthcare](), [healthcare-and-life-sciences](), [intermediate-200](), [kiro](), [life-sciences](), [medical-imaging](), [open-source](), [strands-agents]() ### AI overview This post presents 38 open-source agent skills spanning 11 healthcare and life sciences domains. The skills encode domain decision procedures for AI agents, and the reported evaluation found a 70-86% head-to-head win rate over agents without the skills. ### Source excerpt AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but applying it incorrectly. This post shares 38 open-source agent skills across 11 HCLS domains that close this gap, with installation steps, three worked use cases, and a 410-prompt evaluation showing a 70-86% win rate. ## Review argues 1Password's AI patching benchmark overstates poor patch quality DevFeed: [Review argues 1Password's AI patching benchmark overstates poor patch quality]() Original publisher: [Read original article]() Author: "Anish Naik"; "Dan Guido"; "Benjamin Samuels"; "Marcelo Morales" Published: 2026-09-15T11:00:00Z Content type: opinion Language: en Sources: [The Trail of Bits Blog](), [The Trail of Bits Blog]() Topics: [Artificial Intelligence](), [Benchmark](), [Vulnerabilities](), [Testing](), [Agent Skills]() Tags: [agent-skills](), [agents](), [ai](), [benchmark](), [open-source](), [patch-the-planet](), [report](), [testing](), [vulnerabilities]() ### AI overview The article argues that 1Password's 26% clean-fix headline gives a misleading picture of AI patching because the benchmark included difficult vulnerabilities, prompts instructing agents to apply wrong fixes, trials that prohibited testing, and differing reasoning settings. It reports that 2,634 of 3,067 eligible patches blocked the supplied exploit and announces two agent skills for validation and review. ### Source excerpt 1Password's FLAWED report, published on August 6, 2026, gives defenders a misleading picture of AI patching. Its headline says models produced clean fixes only 26% of the time. That figure includes experiments that deliberately instructed agents to apply the wrong fix, along with experiments in which agents could not compile or test their patches. The report risks making defenders less effective by discouraging them from using technology that could help them fix more vulnerabilities. Teams that take its headline at face value may leave repairable vulnerabilities unaddressed. We want our work to help defenders fix more vulnerabilities. This post shares real-world data on human and agent patch quality from our consulting projects and Patch the Planet. We're also releasing two agent skills: post-patch-validation to help agents test fixes, and review-walkthrough to help engineers review them. How the experiment produces a misleading headline Our review of 1Password's code and data found four choices that make its 26% clean-fix rate a misleading guide to ordinary patching work.1 The sample was selected for difficult fixes. The authors chose six vulnerabilities because their fixes were complex. Clean-fix rates ranged from 3% to 60% across those bugs, so the average depends heavily on which vulnerabilities made the list.2 Two prompts tell agents to apply the wrong fix. Those prompts account for 22% of the data. Combining them with ordinary repair attempts makes the reported rate depend partly on how often the researchers chose to give agents bad advice. More than a third of the trials prohibit testing. One evaluation mode prevents agents from building or running code and accounts for 36% of the data. The headline combines those trials with experiments in which agents could test their patches and act on the results. The models ran at different reasoning settings. GPT-5.5 ran at medium effort and Opus 4.8 at high. These were the tools' defaults. Neither model was tested at i ## And the winners are: Announcing the results of the OpenSearch Agent Skills Hackathon DevFeed: [And the winners are: Announcing the results of the OpenSearch Agent Skills Hackathon]() Original publisher: [Read original article]() Author: James McIntyre Published: 2026-09-14T23:00:09Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Agent Skills](), [Hackathon](), [Code]() Tags: [agent-skills](), [audit](), [blog](), [github](), [hackathon](), [latency](), [opensearch](), [root-cause-analysis](), [technical]() ### AI overview This OpenSearch article announces the winners of the OpenSearch Agent Skills Hackathon. It describes the competition requirements and judging criteria, then highlights the first-place Unclosed skill, which performs auditable log root-cause analysis through premise audits, hypothesis trees, and closure checks. ### Source excerpt Meet the three winners of the OpenSearch Agent Skills Hackathon. Their skills tackle log root-cause analysis, GDPR compliance, and slow-query diagnostics, all built read-only, fully auditable, and shipped with real evaluation. The post And the winners are: Announcing the results of the OpenSearch Agent Skills Hackathon appeared first on OpenSearch. ## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills]() Original publisher: [Read original article]() Author: Alex Porcelli Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Agent Skills](), [Artificial Intelligence](), [Large Language Model](), [NeMo]() Tags: [agent](), [agent-skills](), [agentic](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [architectures](), [artificial-intelligence](), [business](), [decision-models-agentic-ai](), [development](), [enterprise](), [enterprise-architecture](), [governance](), [infoq](), [large-language-models](), [llms](), [ml-data-engineering](), [models](), [nemo](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [skills](), [transcripts]() ### AI overview Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions. ### Source excerpt Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli ## Agent Plugins package your skills, tools, and more DevFeed: [Agent Plugins package your skills, tools, and more]() Original publisher: [Read original article]() Author: Kevin Hou; Haoyu Wang; Alan Blount Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Agent Skills](), [Model Context Protocol (MCP)](), [MCP Server](), [Google](), [amazon](), [Microsoft](), [OpenAI](), [Vercel]() Tags: [agent-skills](), [amazon](), [google](), [maintainers](), [manifest](), [mcp](), [mcp-server](), [microsoft](), [openai](), [plugin](), [portable](), [skills](), [vercel]() ### AI overview The article introduces Agent Plugins 1.0.0, a vendor-neutral specification for packaging Agent Skills and MCP servers into portable plugins. It standardizes the manifest and directory layout so developers can distribute one package across clients while preserving client-specific flexibility. Google is joining the Core Maintainers and beginning support in its products. ### Source excerpt Agent Plugins 1.0.0 is a new, vendor-neutral directory specification--backed by Google, Amazon, Microsoft, and others--for packaging Agent Skills and MCP servers into a single portable unit. By standardizing the manifest (plugin.json) and utilizing a fixed directory layout, it eliminates the need for developers to maintain separate wrappers or configurations to support different AI coding agents and IDEs. Google has officially joined as a Core Maintainer and already rolled out support in the Agents CLI and Data Agent Kit, allowing developers to start building and distributing interoperable plugins today. ## Enable on-demand expertise with Agent Skills in Genkit Go DevFeed: [Enable on-demand expertise with Agent Skills in Genkit Go]() Original publisher: [Read original article]() Author: Daniela Petruzalek Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Agent Skills](), [Agent Harness](), [context window](), [Go](), [Script]() Tags: [agent](), [agent-skills](), [architecture](), [context-window](), [developers](), [go](), [skills](), [workflows]() ### AI overview This article explains how Agent Skills in Genkit Go provide on-demand specialized expertise through progressive disclosure. Skills package instructions, references, and scripts in modular SKILL.md bundles, exposing only metadata initially and loading full content when a task requires it. ### Source excerpt To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed. ## Audit Your Auth0 Tenant with Auth0 Agent Skills DevFeed: [Audit Your Auth0 Tenant with Auth0 Agent Skills]() Original publisher: [Read original article]() Author: Carlos Aguilar Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Auth0 Blog]() Topics: [Auth0](), [Agent Skills](), [Security](), [configuration](), [AI Agent]() Tags: [agent-skills](), [ai-agent](), [ai-coding-agents](), [auth0](), [cli](), [configuration](), [developer-tools](), [security]() ### AI overview This article introduces Auth0 Agent Skills, including the Auth0 HealthCheck skill, which gives local AI coding agents scoped visibility into a live Auth0 tenant. It audits security and configuration, evaluates plan and use-case fit, generates reports and recommendations, and provides approved Auth0 CLI fixes without automatically applying changes. ### Source excerpt Audit your Auth0 tenant's security and configuration, assess capability and plan fit, and generate a personalized report with prioritized recommendations for B2C, B2B, or internal applications. ## Leverage Android skills and Gemma 4 in Android Studio Quail 4 DevFeed: [Leverage Android skills and Gemma 4 in Android Studio Quail 4]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-09-01T15:00:00Z Content type: article Language: en Sources: [Android Developers Blog]() Topics: [Agent Skills](), [Android Studio](), [Artificial Intelligence](), [LLMs](), [Google](), [gemma4]() Tags: [agent-skills](), [ai](), [android-skills](), [android-studio](), [development](), [gemma-4](), [google](), [llms]() ### AI overview Android Studio Quail 4 is stable and includes Android skills bundled directly into the IDE. The curated, preloaded skills help its agent handle complex Android workflows, including API changes, migrations, configuration, profiling, navigation upgrades, and adaptive UI work. ### Source excerpt Posted by Amman Fasil Asfaw, Product Manager, Android Studio Android Studio Quail 4 is now stable and ready for you to use in production. This is the final stable release for Android Studio Quail. The new features in Android Studio enable you to build premium apps with AI efficiently and effectively. Check out the video below to see the most helpful new features from the last 4 releases that can help improve and speed up your development. Here is a deep dive into what's new in Android Studio Quail 4: Android skills bundled into Android Studio While LLMs are incredibly capable at generic coding queries, they frequently write incorrect or outdated code when confronted with rapidly evolving Android APIs, platform-specific migrations, or complex configuration structures.To solve this, we bundle Android skills that have been curated by the team who builds Android, directly into Android Studio. Following the open-standard agent skills specification, these are modular, AI-optimized instructions designed specifically to guide LLMs through complex Android workflows. Android skills are now pre-loaded directly into the IDE, so you can start using them without having to manually download additional files. When you prompt the Android Studio agent, we analyze your prompt and search against the metadata for installed skills, automatically invoking them when they're most relevant. Your agent gains instant domain expertise, applying Google's best practices with less overhead spent on long, manual setup prompts. Android Studio comes preloaded with 23 curated skills, including: Need help upgrading your build? You have the Android Gradle Plugin (AGP) 9 Upgrade skill. Want to profile your app for any performance issues? You have the Android Profiler skill. Ready for a Jetpack Navigation framework upgrade? You have the Navigation3 skill. Adapting your app UI to different Android devices? You have the Adaptive skill. We also encourage you to create your own custom skills to extend Agent M ## How Datadog saves over $1 million each month by optimizing AI usage DevFeed: [How Datadog saves over $1 million each month by optimizing AI usage]() Original publisher: [Read original article]() Author: Bowen Chen; Yosra Chelbi; Nader Khalil; Dmitrii Ulianin Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Optimization](), [Artificial Intelligence](), [cloud cost management](), [Claude](), [Agent Skills]() Tags: [agent-skills](), [ai](), [anthropic](), [claude](), [claude-code](), [cloud-cost-management](), [cost](), [optimization]() ### AI overview Datadog describes how it tracks AI spending and optimizes tool configurations, model choices, and workflows to reduce monthly AI costs by over $1 million while balancing performance. ### Source excerpt Learn how Datadog uses agent evaluations, spending guardrails, and context optimization to save over $1 million each month while balancing cost and performance. ## Just landed in the Neon CLI DevFeed: [Just landed in the Neon CLI]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-25T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Command-line interface](), [Databases](), [MCP](), [Agent Skills](), [API keys]() Tags: [agent-skills](), [agents](), [ci](), [cli](), [coding](), [database](), [diagnostics](), [mcp](), [product](), [tool]() ### AI overview An update on recent Neon CLI changes, including the shorter npm package name, agent-focused setup for MCP and skills, project and branch linking, environment-variable management, database diagnostics, dashboard access, and project-scoped keys. ### Source excerpt The Neon CLI moved fast over the last couple of months. It is a key tool for agents, who do all the work without ever opening the Console - let's take a look at some of recent changes. ## Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator DevFeed: [Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Agent Skills](), [AI Agent](), [Benchmark](), [Nvidia](), [Open Source](), [Agent Harness](), [Security]() Tags: [agent](), [agent-skill](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [benchmark](), [build-ai-agents](), [codex](), [cursor](), [developer-tools-techniques](), [featured](), [nvidia](), [open-source](), [prompt-injection](), [sandbox](), [security](), [simulation-modeling-design](), [trustworthy-ai]() ### AI overview NVIDIA SkillEvaluator is an open-source evaluation layer for measuring how packaged skills affect AI-agent performance. It compares agent runs with and without a skill, using static validation, embedding-based distinctiveness checks, and live task evaluations in isolated sandboxes. The article reports benchmark results for more than 300 verified skills across over 30 NVIDIA products and describes integrations with Claude Code, Codex, Cursor, Skills.sh, ClawHub, and Hermes Hub. ### Source excerpt AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding... ## How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit DevFeed: [How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-18T18:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [ALCHEMI](), [AI-assisted coding](), [Agent Skills](), [PyTorch](), [CUDA](), [GPU](), [Python](), [AI Development]() Tags: [agent-skills](), [agentic-ai-generative-ai](), [ai-agent](), [ai-coding](), [alchemi](), [coding](), [computational-chemistry-materials-science](), [compute](), [cuda](), [developer-tools-techniques](), [featured](), [gpu](), [python](), [pytorch](), [simulation-modeling-design]() ### AI overview This article presents an end-to-end workflow for using AI coding agents with NVIDIA ALCHEMI Toolkit to build GPU-accelerated atomistic materials simulations. It explains how ALCHEMI agent skills and reference files provide API knowledge, describes the Python, PyTorch, CUDA and NVIDIA GPU environment, and reports validation across 45 generated pipelines. ### Source excerpt Atomistic simulation requires three things: knowledge of the science, compute-efficient implementation of simulations, and accessible interfaces to the... ## New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot DevFeed: [New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot]() Original publisher: [Read original article]() Author: Confluent Staff Published: 2026-08-18T14:00:10Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Artificial Intelligence](), [apache-flink](), [Kafka](), [streaming-data-processing](), [real-time](), [MCP Server](), [Agent Skills](), [MCP](), [Inference](), [Google](), [ibm]() Tags: [ai](), [ai-ready-data](), [ai-tools](), [apache-flink](), [confluent](), [confluent-cloud](), [google](), [ibm](), [kafka](), [mcp](), [mcp-server](), [real-time](), [skills](), [streaming-data-processing](), [support](), [tools]() ### AI overview Confluent announces updates to Confluent Intelligence and related AI tools for building production AI systems on Apache Kafka and Apache Flink. The release adds expanded time-series model support, a generally available Real-Time Context Engine, updates to the fully managed MCP Server, Agent Skills for AI coding assistants, and Confluent Copilot. The features are intended to provide agents and applications with fresh business context and governed access to live data and infrastructure. ### Source excerpt Explore new AI features and AI tools: support for IBM Granite Time Series models and TimesFM models (EA), enhanced Real-Time Context Engine experience, new Agent Skills, Confluent Copilot ## Ship agent skills like packages: discovery index, digests, and install sources DevFeed: [Ship agent skills like packages: discovery index, digests, and install sources]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-12T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Agent Skills](), [Agent Skill](), [ai-coding](), [Publishing](), [GitHub](), [make](), [rubygems]() Tags: [agent-experience](), [agent-skill](), [agent-skills](), [ai](), [coding](), [coding-agents](), [dx](), [github](), [jamstack](), [json](), [llms](), [npm](), [rubygems](), [sha-256](), [skills]() ### AI overview This tutorial explains how to publish agent skills so AI coding agents can discover and install them. It covers a .well-known/agent-skills/index.json discovery index, SHA-256 integrity digests, single-file, multi-file, and bundle packaging, multiple install commands, and hosting skills from a GitHub repository while re-hosting the served files on the publisher's domain. ### Source excerpt How to publish agent skills so any AI coding agent can find and install them: the .well-known/agent-skills/index.json discovery index, SHA-256 integrity digests, single-file vs multi-file vs bundle packaging, and every install command. Worked from the catalog we just open-sourced. ## Eval-driven development: How we build better agent skills for Firebase DevFeed: [Eval-driven development: How we build better agent skills for Firebase]() Original publisher: [Read original article]() Author: Charlotte Liang Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Agent Skills](), [Firebase](), [AI-assisted coding](), [Development](), [Model Context Protocol](), [Command-line interface]() Tags: [agent](), [agent-skills](), [ai](), [ai-agents](), [best-practices](), [cli](), [coding-agents](), [development](), [firebase](), [mcp]() ### AI overview This article explains how Firebase combines Agent Skills, the Firebase CLI, and MCP servers to help AI coding agents produce better Firebase code. It describes skills as focused, outcome-driven guidance for current APIs, best practices, Firestore development, and Firebase Security Rules, and notes that they can help agents discover recent features beyond their training data. ### Source excerpt How we build better agent skills for Firebase ## AWS Supports Agent Plugins: An Open Standard for Portable Agent Extensions DevFeed: [AWS Supports Agent Plugins: An Open Standard for Portable Agent Extensions]() Original publisher: [Read original article]() Author: Libby Clark Published: 2026-08-06T17:21:21Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Agent Skill](), [Agent Skills](), [AI Agent](), [MCP Server](), [Open Source](), [Amazon Web Services](), [Model Context Protocol]() Tags: [agent-skill](), [agent-skills](), [ai-agent](), [aws](), [cursor](), [javascript](), [mcp](), [mcp-server](), [microsoft](), [model-context-protocol](), [open-source](), [openai](), [vercel](), [vs-code]() ### AI overview AWS supports Agent Plugins 1.0.0, an open source, vendor-neutral specification for packaging AI agent extensions in a common format. The standard is intended to let developers package an extension once and distribute it across compatible clients such as Kiro, VS Code, Cursor, and other tools. Its initial version standardizes Agent Skills and MCP servers, with hooks and custom agents planned for future versions. ### Source excerpt If you've ever built an MCP server or agent skill, you know that packaging it means adapting for one client, re-writing for another, and repeating that for every tool your team uses. Agent Plugins 1.0.0 is an open source, vendor-neutral specification that gives AI agent extensions a common packaging format so you can package an [...] ## Introducing Agent Plugins DevFeed: [Introducing Agent Plugins]() Original publisher: [Read original article]() Author: Jonathan Hefner Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Agent Skills](), [Model Context Protocol (MCP)](), [MCP Server](), [JSON](), [Artificial Intelligence](), [Extension]() Tags: [agent-skills](), [json](), [manifest](), [mcp](), [mcp-server](), [plugin](), [plugins](), [validation]() ### AI overview Agent Plugins 1.0.0 is an open, vendor-neutral standard for packaging Agent Skills and MCP servers into distributable plugins. It defines a directory structure with a plugin.json manifest and fixed component locations, while leaving installation, distribution, policy, user experience, and client-specific capabilities to individual clients. ### Source excerpt Today, Agent Plugins 1.0.0 is publicly available. Agent Plugins is an open, vendor-neutral standard for plugins that extend AI agents. Agent Skills provide reusable instructions and resources for AI agents. MCP servers connect agents to tools and services. Both can be reused across clients, but clients often package and discover them differently. Agent Plugins gives compatible clients a common format: a directory with a plugin.json manifest and fixed locations for its components. The format is intentionally small and easy to implement, and it leaves installation, distribution, policy, user experience, and client-specific capabilities to each client. One package for the portable parts Extension authors often adapt the same component to several client formats. Even though the underlying Skill or MCP server is identical, clients often expect different top-level metadata, discovery paths, or MCP configuration. Agent Plugins gives those shared components one predictable, structured home: A minimal JSON manifest (plugin.json) identifies the specification version and names the plugin: Those two fields are the minimum requirement for the manifest, and the rest of the contract is represented in the file structure of the directory itself. A reusable component should not need to be repackaged for every client, so the format specifies only what a client needs to discover and load what is inside. Every compatible client checks for plugin.json at the plugin root. Clients that support Skills discover them under skills/. Clients that support MCP servers read their configuration from mcp.json. A client can support either component type or both. After the client validates the manifest, components are validated independently, so one invalid component does not disable unrelated ones. For plugin authors, that means fewer client-specific conventions for the same component. For client implementers, the specification defines a small, deterministic contract for discovery, validation, and lo ## Marketplace integrations now install provider skills DevFeed: [Marketplace integrations now install provider skills]() Original publisher: [Read original article]() Author: Jathin Singaraju Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Agent Skills](), [Vercel](), [Command-line interface](), [npm]() Tags: [agent-skills](), [cli](), [integrations](), [npm](), [skills](), [update](), [vercel]() ### AI overview Vercel Marketplace integrations installed through the Vercel CLI now also install the provider's agent skills from skills.sh when available. The update lets agents use an integration immediately, and adds terminal commands for discovering providers and listing Marketplace categories. ### Source excerpt When you install a Vercel Marketplace integration from the Vercel CLI, it now also installs that provider's agent skills from skills.sh, so your agents know how to use it: This happens automatically for any provider that publishes skills. You can also find integrations without leaving the terminal: vercel integration discover browses available providers. vercel integration categories lists all the Marketplace categories they're grouped into, like databases, AI, and observability. Update to the latest Vercel CLI with npm i -g vercel@latest, then read the integration documentation to get started. Read more ## Introducing Agent Plugins 1.0.0 DevFeed: [Introducing Agent Plugins 1.0.0]() Original publisher: [Read original article]() Author: Jonathan Hefner Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Agent Skills](), [Model Context Protocol](), [client](), [JSON](), [ChatGPT](), [codex](), [cursor](), [GitHub](), [Kiro](), [vs-code]() Tags: [agent](), [agent-skills](), [announcement](), [chatgpt](), [codex](), [cursor](), [distribution](), [github](), [github-copilot](), [guide](), [kiro](), [manifest](), [mcp](), [plugins](), [policy](), [portable](), [skills](), [user-experience](), [vs-code]() ### AI overview Agent Plugins 1.0.0 is an open, vendor-neutral standard for packaging Agent Skills and MCP servers into portable plugins. It defines a common root manifest and fixed component locations while allowing each compatible client to control installation, distribution, policy, user experience, and client-specific capabilities. ### Source excerpt Agent Plugins 1.0.0 is now available. It is an open, vendor-neutral standard for packaging Agent Skills and MCP servers into portable plugins. Compatible agent clients can discover and load them. Agent Plugins defines a common format: a root plugin.json manifest, plus fixed locations for portable components. Each client keeps control of installation, distribution, policy, user experience, and client-specific capabilities. At launch, Agent Plugins is supported across the following clients: ChatGPT and Codex Cursor GitHub Copilot Kiro VS Code Read the specification and implementation guides, learn more in the announcement post, and follow our guide to build a plugin on the new standard. Read more ## Why AI-assisted attacks made software supply chain security its own category DevFeed: [Why AI-assisted attacks made software supply chain security its own category]() Original publisher: [Read original article]() Published: 2026-08-03T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [supply-chain-security](), [Security](), [Artificial Intelligence](), [Open Source](), [Agent Skills](), [Application Security](), [CI/CD]() Tags: [agent-skills](), [ai](), [ai-assisted-attacks](), [ci-cd](), [containers](), [open-source](), [secure-by-default](), [security](), [software-supply-chain](), [software-supply-chain-security](), [supply-chain-security]() ### AI overview The article argues that AI has changed the economics and speed of software supply chain attacks, making software supply chain security a distinct category requiring dedicated tooling, budgets, and strategy. It describes how AI benefits defenders and attackers, while agents accelerate dependency adoption and introduce risks through artifacts such as agent skills. ### Source excerpt Software supply chain security is now its own category. Discover why AI is driving a shift toward trusted, secure-by-default open source. ## Introducing Supabase Evals DevFeed: [Introducing Supabase Evals]() Original publisher: [Read original article]() Author: Matt Rossman Published: 2026-07-31T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [AI-assisted coding](), [Benchmark](), [Open Source](), [coding](), [Command-line interface](), [MCP Server](), [Agent Skills](), [Database](), [debugging](), [observability](), [GitHub Issues]() Tags: [agent-skills](), [ai](), [ai-coding](), [benchmark](), [cli](), [coding](), [github-issues](), [mcp](), [mcp-server](), [open-source](), [testing]() ### AI overview Supabase introduces an open-source benchmark and framework for evaluating how well AI coding agents build with Supabase. It runs agents such as Claude Code, Codex, and OpenCode through realistic tasks, including schema construction, Edge Function debugging, and RLS policy fixes, then measures their performance in benchmark and regression suites. ### Source excerpt Our open-source benchmark for how well AI coding agents build with Supabase. ## Agent skills are changing platform engineering. But are they secure? DevFeed: [Agent skills are changing platform engineering. But are they secure?]() Original publisher: [Read original article]() Author: Nigel Douglas Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Agent Skills](), [Platform Engineering](), [Security](), [Artificial Intelligence](), [OpenClaw](), [anthropic]() Tags: [agent-skills](), [agentic-ai](), [ai](), [ai-agents](), [open-source](), [openclaw](), [platform-engineering](), [security]() ### AI overview The article examines how Agent Skills--folders containing instructions, scripts, and resources--extend AI agents with specialized capabilities in platform engineering. It discusses their productivity benefits, their distribution through public libraries such as ClawHub, and the security risks created by untrusted skills and expanding tool sprawl. ### Source excerpt Explore how Agent Skills fit into the platform engineering stack, the security risks of public skill libraries, and how OSM helps detect malicious AI skills ## How to use Chrome's Modern Web Guidance to prevent AI agents from writing legacy frontend code DevFeed: [How to use Chrome's Modern Web Guidance to prevent AI agents from writing legacy frontend code]() Original publisher: [Read original article]() Author: Emmanuel John Published: 2026-07-22T13:00:25Z Content type: tutorial Language: en Sources: [LogRocket Blog]() Topics: [AI-assisted coding](), [Agent Skills](), [Web platform](), [Front end](), [Web Development](), [CSS](), [HTML](), [browser](), [Retrieval Augmented Generation (RAG)]() Tags: [agent-skills](), [ai](), [ai-agents](), [ai-coding](), [browser](), [css](), [dev](), [frontend](), [how-to](), [html](), [retrieval-augmented-generation-rag](), [uncategorized](), [web-platform]() ### AI overview Chrome's Modern Web Guidance adds current web platform guidance, browser compatibility data, and best practices to AI coding workflows. The article explains how this can help agents favor simpler native HTML, CSS, and browser API solutions over unnecessary legacy JavaScript, dependencies, and outdated patterns. ### Source excerpt Chrome's Modern Web Guidance embeds modern web platform skills into AI coding agents, helping them choose native HTML, CSS, and browser APIs over legacy patterns. The post How to use Chrome's Modern Web Guidance to prevent AI agents from writing legacy frontend code appeared first on LogRocket Blog. ## \[May-Jun 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[May-Jun 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-07-22T01:22:04Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [Google](), [Google AI](), [AI Agent](), [Agent Skills](), [Model Context Protocol (MCP)](), [Windows Subsystem for Linux](), [Python](), [HTML](), [Command-line interface](), [CSV](), [SDKs]() Tags: [2026](), [ai](), [ai-agent](), [ai-agents](), [antigravity](), [cli](), [community](), [google](), [google-ai](), [here](), [html](), [mcp](), [python](), [sdk](), [skills](), [wsl]() ### AI overview This article highlights May-June 2026 activities and achievements from Google AI communities. It summarizes community projects, codelabs, experiments, and guides involving Google Antigravity, Gemini, the Antigravity SDK, AI agents, MCP servers, agent skills, CLI and IDE workflows, HTML dashboards, CSV data, and Windows Subsystem for Linux. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravity [Codelab] Building Trustable AI at 100 MPH by GDEs in the US: Hemanth HM, Vikram Tiwari, Lynn Langit, Sebastian Gomez, Rabimba Karanjai; Googler: Ajeet Mirwani; and the partner: Ocupop guides you in building a trustable AI prototype inspired by the field test from the Unstoppable GDE Cohort. The project summary, Bridging the Domain Gap: AI Race Coach built with Antigravity and Gemini and the members of the July cohort were featured on Google for Developers Blog✨. Skills over System Prompts: Building an Anki Tutor with the Antigravity SDK by AI GDE Ertuğrul Demir (Türkiye) demonstrates the value of a modular approach to AI agent design by creating custom Python tools and reusable skill packages for an Anki flashcard tutor using Antigravity SDK. (Image source) Startup on a Shoestring -- Building with a 50c Budget using Google Antigravity Cost & Token Monitors💸 by AI GDE Jigyasa Grover (US) conducted an SDK-powered cost engineering experiment to observe an AI agent prioritize, panic, and ship an MVP landing page under intense budget pressure. Google Antigravity CLI: Orchestrating Parallel AI Agents by AI GDE Aashi Dutt (India) explores how to create an interactive HTML dashboard from a raw CSV file with a single command and explains the benefits of the subagent architecture. (Image source) Configuring MCP Servers and Skills for Antigravity CLI and IDE by Cloud GDE Darren Lester (UK) provides a deep-dive guide on configuring MCP servers and agent skills for the new Antigravity CLI and IDE. He also shared Resolving WSL Friction with Google Antigravity: the Agy 2.0 and Agy IDE Edition discussing how to resolve friction between the Antigravity suite and the Windows Subsystem for Linux environment. Google Antigravity 2 & Gemini 3.5 [Next page]()