# agent-skills Published articles for agent-skills. 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. ## Put Redis data and engineering guidance to work in ChatGPT Work DevFeed: [Put Redis data and engineering guidance to work in ChatGPT Work]() Original publisher: [Read original article]() Author: Olga Lopaci Published: 2026-09-11T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Developer Tools](), [AI Chat](), [AI Bots]() Tags: [agent-skills](), [chatgpt](), [codex](), [engineering](), [observability](), [rag](), [redis](), [review](), [security](), [skills](), [tech](), [work]() ### AI overview Redis launched a development plugin for ChatGPT Work and Codex that supplies current Redis guidance for writing, reviewing, and troubleshooting code. It also describes connecting Redis data to ChatGPT Work's Data agent for plain-language exploration and investigation. ### Source excerpt Redis has launched a development plugin that brings current Redis engineering guidance into ChatGPT Work and Codex. It helps teams write, review, and troubleshoot Redis code without switching between documentation and development tools. Alongside Ope... ## AWS open-sources Pizza Bot: email-style inbox for background AI agents DevFeed: [AWS open-sources Pizza Bot: email-style inbox for background AI agents]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-10T22:54:00Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI Bots](), [AI Chat]() Tags: [agent-skills](), [agents](), [ai](), [ai-agents](), [amazon-web-services-aws](), [mcp](), [model-context-protocol](), [open-source]() ### AI overview AWS has open-sourced Pizza Bot, a self-hosted inbox interface for reviewing and triaging work from background AI agents. The desktop app supports scheduled work, human-decision requests, and configurable model providers. ### Source excerpt Amazon Web Services (AWS) has released a new open-source application dubbed Pizza Bot, which gives developers an email-style inbox for The post AWS open-sources Pizza Bot: email-style inbox for background AI agents appeared first on The New Stack. ## ClickHouse is a launch partner for the Data agent in ChatGPT Work DevFeed: [ClickHouse is a launch partner for the Data agent in ChatGPT Work]() Original publisher: [Read original article]() Author: Aditya Chidurala; Teresa Blanco Published: 2026-09-10T15:13:26Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI Chat](), [dashboards](), [AI Bots]() Tags: [agent](), [agent-skills](), [analytics](), [chatgpt](), [clickhouse](), [codex](), [dashboards](), [mcp](), [mcp-server](), [oauth]() ### AI overview ClickHouse announces a ChatGPT Work plugin that connects ClickHouse Cloud through OAuth for natural-language data exploration, reports, and interactive dashboards. ### Source excerpt ClickHouse joins the Data agent in ChatGPT Work, connecting ClickHouse Cloud to natural-language queries, reports, and interactive dashboards. ## v0 adds one-click integrations for email, auth, search, and databases DevFeed: [v0 adds one-click integrations for email, auth, search, and databases]() Original publisher: [Read original article]() Author: Jathin Singaraju Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Chat]() Tags: [agent-skills](), [auth](), [code](), [databases](), [integration](), [react](), [search](), [skills](), [vercel]() ### AI overview Vercel's v0 adds inline Marketplace integrations for providers including Resend, Amazon OpenSearch, MongoDB Atlas, Algolia, and Clerk. Connected providers can be configured automatically, and provider skills can guide generated code. ### Source excerpt We're working toward bringing parity across Vercel integration and v0, starting with Resend, Amazon OpenSearch, MongoDB Atlas, Algolia and Clerk. Prompt v0 with what you want to build, and when your prompt requires a provider, v0 renders a connect card in the chat. With Marketplace integrations in v0, you get: Connect as you build: Prompt v0 with what you want to create, and connect the required provider inline. Automatic setup: Once connected, v0 handles the required environment variables and configuration. Provider skills loaded automatically: When you connect a provider that publishes agent skills, v0 loads them and generates code that follows the provider's recommended patterns. Provider-specific capabilities: Resend, for example, adds transactional email with React Email components. To get started, open a v0 chat and prompt what you want to add to your stack. Read more ## 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. ## The iOS Simulator Inside Cursor, Claude Code, and Codex DevFeed: [The iOS Simulator Inside Cursor, Claude Code, and Codex]() Original publisher: [Read original article]() Author: Antoine van der Lee Published: 2026-09-07T13:07:15Z Content type: opinion Language: en Sources: [SwiftLee]() Topics: [iOS](), [cursor](), [Claude Code](), [codex](), [App](), [Tooling]() Tags: [agent](), [agent-skills](), [ai-development](), [app-development](), [claude-code](), [codex](), [cursor](), [developer-tools](), [ios]() ### AI overview An opinion article examines how the iOS Simulator is becoming integrated into Cursor, Claude Code, and Codex as app development shifts toward orchestrating AI agents. It argues that visual testing, performance review, and critical human validation remain essential, and describes AI Agents for the iOS Simulator as a way to automate navigation and validation within the developer workflow. ### Source excerpt App development is changing, and the iOS Simulator is too, as it starts to appear in Cursor, Claude Code, and Codex in different ways. I've been developing apps since 2009 and the RocketSim developer tool since 2019, and I can tell we're reinventing ourselves like never before. I also started to wonder: what is the ... -> The post The iOS Simulator Inside Cursor, Claude Code, and Codex appeared first on SwiftLee. ## 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: release Language: en Sources: [Android Developers Blog](), [Android Developers Blog]() Topics: [Android skills](), [Android Studio](), [Large Language Model](), [releases](), [gemma4]() Tags: [agent](), [agent-skills](), [android-gradle-plugin](), [android-skills](), [android-studio](), [gemma](), [jetpack](), [llms](), [performance](), [release]() ### AI overview Android Studio Quail 4 is a stable production release that preloads curated Android skills for its agent. The skills help LLMs handle Android-specific workflows such as upgrades, profiling, navigation, 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 ## 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 ## Neon is now available in Grok Bot DevFeed: [Neon is now available in Grok Bot]() Original publisher: [Read original article]() Author: Dominik Koch Published: 2026-09-01T12:00:00Z Content type: news Language: en Sources: [Blog -- Neon Docs]() Topics: [Bot](), [Databases](), [Model Context Protocol]() Tags: [agent](), [agent-skills](), [bots](), [database](), [mcp](), [mcp-server](), [migration](), [product](), [sql](), [testing](), [tools](), [workflows]() ### AI overview Neon is available as a Grok Bot plugin, combining Neon agent skills with the Neon MCP Server to let authorized bots manage Neon database workflows. ### Source excerpt Neon is now available as a plugin in Grok Bot. You just have to connect your Neon account once and you'll be able to ask a Grok bot to build on Neon. ## Auditing Coding-Agent Configuration, Skills, Memory, and Hooks DevFeed: [Auditing Coding-Agent Configuration, Skills, Memory, and Hooks]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-27T14:31:10Z Content type: tutorial Language: en Sources: [Elevate]() Topics: [Agent Skill](), [configuration](), [AI-assisted coding](), [Claude](), [sdlc]() Tags: [agent](), [agent-skill](), [agent-skills](), [claude](), [coding-agents](), [configuration](), [markdown](), [sdlc]() ### AI overview This practical guide argues that coding-agent configuration files can become outdated as models, harnesses, and codebases change. It recommends periodically reviewing configuration, memory, skills, and hooks, while acknowledging concerns about token costs, excessive instructions, and uncertain effects on quality. The author describes using Claude's /doctor and maintaining Agent Skill packages. ### Source excerpt A practical guide to auditing what your coding agent still needs. ## 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. ## Algolia joins the Vercel Marketplace DevFeed: [Algolia joins the Vercel Marketplace]() Original publisher: [Read original article]() Author: Sam Halstead Published: 2026-08-19T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI search](), [AI-assisted coding]() Tags: [agent-skills](), [api](), [cli](), [search](), [vercel]() ### AI overview Algolia can now be provisioned and managed through the Vercel Marketplace, with credentials added to project environment variables. The integration supports indexed search, crawling Vercel domains, InstantSearch interfaces, recommendations, AI ranking, and agent skills for API use. ### Source excerpt You can now provision and manage Algolia directly from the Vercel Marketplace. Algolia is a hosted search platform. You send it your content, it builds an index, and your app queries that index instead of your database. Results come back in milliseconds, with typo tolerance, synonyms, and facet counts built in. Key capabilities: Index product catalogs, documentation, or any other content Add recommendations and AI ranking to surface the most relevant results Point the Algolia Crawler at a Vercel domain to index your site automatically Build search interfaces with Algolia's InstantSearch libraries Installing adds your Algolia credentials to your project's environment variables; redeploy to pick them up. Algolia's agent skills install too, so your coding agent knows how to use the API out of the box. Install Algolia on the Vercel Marketplace or with the Vercel CLI: vc i algolia. Read more ## 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... ## Neon skills landed in the Vercel CLI DevFeed: [Neon skills landed in the Vercel CLI]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-14T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding]() Tags: [agent](), [agent-skills](), [backend](), [cli](), [coding](), [database](), [deployment](), [integration](), [postgres](), [product](), [skills](), [vercel]() ### AI overview The article explains that installing a Vercel Marketplace integration through the CLI can also install provider agent skills. It uses Neon as an example: one command provisions a database, connects it to a Vercel project, makes connection variables available locally, and gives a coding agent instructions for using the database. ### Source excerpt Vercel just gave their CLI a new trick: when you install a Marketplace integration from the terminal, it also installs that provider's agent skills. A single command now provisions a Neon database on your Vercel project and hands your coding agent the instructions it needs to actually use it. ## 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 ## Skill packs are now available on skills.sh DevFeed: [Skill packs are now available on skills.sh]() Original publisher: [Read original article]() Author: Andrew Qu Published: 2026-08-07T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Bots]() Tags: [agent](), [agent-skills](), [community](), [github](), [local](), [skills](), [update]() ### AI overview skills.sh now lets users bundle multiple agent skills into unlisted, shareable packs. Packs can be assembled from community skills, local folders and ZIPs, and public or private GitHub repositories, then installed or updated with commands. ### Source excerpt You can now bundle multiple agent skills into a shareable pack on skills.sh. Build a pack from community skills, your own local folders and zips, or public and private GitHub repositories. Every pack is unlisted, with its own URL. Share that link with a single person, or your whole team to standardize the skills across your projects. Install a pack with one command: Run npx skills update to update a pack to its latest version. Explore and create packs at skills.sh/packs. Read more [Next page]()