# 翻译结果： security-threat-model: Let Agent perform threat modeling based on code repositories
### Official Translation: This article introduces OpenAI's curated Skill **security-threat-model**: it enumerates trust boundaries, assets, attacker capabilities and abuse paths based on repository evidence, and outputs a structured Markdown threat model. The article verifies the official workflow, reporting contract, installation and activation methods of Codex/Cursor/Claude Code (including the Trail of Bits plugin), as well as typical prompt words and usage boundaries.
Read More`render-deploy`: Analyze codebase, generate Blueprint, and deploy applications to Render
This article introduces OpenAI Curated Agent Skill `render-deploy`: it analyzes the codebase to generate a render.yaml Blueprint, or directly creates a single service via Render MCP, and provides a deep link to the Dashboard to complete the deployment. It verifies the installation methods (npx skills / Codex skill-installer), the MCP configuration for Cursor/Claude/Codex, the heuristic guidelines for choosing between Blueprint and Direct Creation, as well as precautions such as Git remote, image service restrictions, `sync:false` for secrets, port binding and other matters, and complements deployment-related Skills like Vercel/Cloudflare.
Read MoreOne-click Deployment of Frontend Projects to Netlify with the netlify-deploy Skill
### Translated Abstract: This article introduces the OpenAI curated skill `netlify-deploy`: it uses the Netlify CLI (`npx netlify`) to complete authentication, site association/creation, dependency installation, and preview/production deployment. It explains the installation methods in the Agent Skills directories of Cursor, Codex, Claude Code and other platforms, provides official workflow command examples, notes for `netlify.toml`, as well as usage suggestions such as sandbox networking and key management.
Read MoreSelf-improving Programming Agents Have Gone Viral: Long-term Autonomous Tasks, What Are Developers Betting On?
# 2026-08-10 GitHub Trending Leader: prime-agent by PrimeIntellect-ai The repo gained approximately 2,356 stars in a single day. Positioned as a self-improving RLM Agent oriented towards coding and long-term autonomy, this article verifies its two core abstractions based on the official repository and blog: Recursive Language Model (persistent IPython and programmatic sub-agents) and Continual Harness (small-step rewrite prompts/memory/skills via /refine with rollback capability). It explains how daemon, objectives, and bounded/autonomous modes support long-running tasks, clarifies that "self-improvement" refers to scaffolding state iteration rather than weight self-training, and also warns about the non-safe sandbox risk.
Read MoreClaude Code v2.1.224: Multiple independent sessions can send messages directly to each other, say goodbye to copy-pasting between terminals
Anthropic launched cross-session messaging in Claude Code v2.1.224 (released on August 7, 2026): independently running sessions on macOS/Linux can discover each other via ListAgents and send each other plain text summaries via SendMessage, without manually copying context between terminals. The messages do not carry conversation history or files, and the recipient still needs to approve sensitive operations on their own; the communication uses local sockets on the same machine, and cross-machine support is limited to replies via Remote Control. This article introduces the mechanism, scenarios, usage and security boundaries based on official documentation.
Read MoreClaude Code Launches Default Auto Mode on August 14: AI Programming Agents Move from "Review Line-by-Line" to Autonomous Execution
Anthropic announced that starting August 14, newly created Claude Code sessions for Pro, Max and Team users will enable Auto Mode by default, replacing one-by-one permission pop-ups with a safety classifier. Official controlled experiments show that the classifier intercepts approximately 89% of dangerous commands, far higher than the approximately 14% interception rate of manual approval; PR output for Team users increased by about 25%. This article sorts out the Auto Mode mechanism, safety data, applicable scope of each subscription plan, and developer response suggestions.
Read MoreJupyter Notebook: Official OpenAI Skill to Enable Agents to Generate Jupyter Notebooks in Compliance with Specifications
OpenAI's curated Skill jupyter-notebook targets two scenarios of experimental and tutorial Jupyter Notebooks. It generates structurally standardized .ipynb files through bundled templates and the new_notebook.py scaffolding script, and is also equipped with experiment/tutorial mode guides and quality checklists. This article verifies the official SKILL.md and repository scripts, and introduces the installation methods (Codex $skill-installer, Cursor .cursor/skills), typical commands and Agent prompts, as well as applicable scenarios and precautions.
Read Morecli-creator: Turn API Documentation into Agent-Invokable Command-Line Tools
cli-creator is a curated Skill in the OpenAI Skills repository. It guides AI to generate installable, composable command-line tools that output stable JSON from API documents, OpenAPI, curl, or SDK scaffolds, and is paired with a Companion Skill for subsequent Agent reuse. This article introduces its core command contracts (doctor/discovery/resolve/read/write), runtime selection for Rust/Node/Python, authentication priority, as well as installation and typical usage in tools such as Codex and Cursor.
Read More# Sentry Skill: Integrating Production Error Troubleshooting into AI Agent Workflows
Sentry is an Agent Skill maintained by OpenAI in the curated directory of the openai/skills repository, which enables read-only observability queries via the official Sentry CLI: listing unresolved issues, viewing event details, and calling issue explain/plan to generate root cause analysis and repair suggestions. This article introduces its positioning, core commands, installation methods for both the CLI and the Skill (Codex's $skill-installer and Cursor's .cursor/skills), as well as precautions including the default 24-hour production environment query scope, JSON output format and security desensitization, which is suitable for DevOps/SRE to integrate online troubleshooting into AI workflows.
Read MoreLinear Skill: Let AI Agents Directly Operate Linear Tickets, Not Just Write Code
### 译文： Officially selected by OpenAI: Skill Linear, which enables Agents to read, create and update Issues, projects and team workflows via Linear MCP. This article introduces its positioning, core capabilities, installation steps for Codex/Cursor, typical usage scenarios such as Bug triage and Standup synchronization, as well as precautions including OAuth and rate limits.
Read Moreyeet: Official OpenAI Skill, Run stage → commit → push → Create PR in one sentence
yeet is a curated Agent Skill maintained by OpenAI in the openai/skills repository. It only uses the GitHub CLI to complete git add, commit, push and create or update a Pull Request in one go when the user explicitly requests it. This article verifies its workflow based on the official SKILL.md: automatically creating a branch from the default branch, perceiving PR templates, avoiding duplicate PRs, and creating new PRs as Drafts. It also explains the installation methods and typical trigger phrases in Codex and Cursor. It is suitable for GitHub developers who want to hand over the final mile of Git release to AI Agents.
Read More# migrate-to-codex: Official Migration Skill to One-Click Move Claude Code Configuration to Codex
OpenAI's official Skill `migrate-to-codex` can convert Claude Code's instructions, Skills, Hooks, MCPs and sub-Agents into Codex's standard directory structure, supports scan, plan, dry-run and verification, and outputs a migration report. This article introduces installation and activation, typical commands, as well as the complementary relationship and limitations between it and Codex /import.
Read More# Slack-GIF-Creator: Let AI Help You Make GIFs Compliant with Slack Specifications
Slack-gif-creator is an Agent Skill in Anthropic's official Skills repository. It encapsulates constraints such as GIF dimensions, frame rate and color specifically for the Slack scenario, and provides a GIFBuilder assembly tool, validators for verification, as well as guides on easing and animation concepts. This article introduces its positioning, core capabilities, as well as installation methods and typical usage in AI programming tools such as Cursor and Claude Code, helping developers quickly produce GIFs that can be uploaded to Slack using natural language.
Read MoreDetailed Explanation of canvas-design Skill: Incorporating Design Philosophy into AI to Generate PNG/PDF Static Visual Works
Canvas-design is a creative Agent Skill in Anthropic's official Skills repository. It adopts a two-step workflow of "first formulating design philosophy, then expressing it on the canvas, and finally conducting secondary refinement", and outputs .md manifesto and .png/.pdf visual finished products. This article verifies its core capabilities, canvas-fonts font resources, installation methods in Cursor and Claude Code, as well as typical usages such as posters and brand visuals and copyright considerations based on the official SKILL.md.
Read More# brand-guidelines: Let AI Agent Automatically Apply Anthropic Official Brand Specifications
`brand-guidelines` is an official Anthropic Skills example that writes brand colors (#141413, #d97757, etc.) and Poppins/Lora font rules into `SKILL.md`, enabling Agents to automatically align with visual specifications when generating PPTs, documents, and HTML. Based on verification against the official `SKILL.md` and the repository README, this article introduces its positioning, color palette details, installation methods for Cursor/Claude Code/Claude.ai, typical prompts, as well as reusable patterns and precautions for building enterprise self-built design specification Skills.
Read More### 正式译法（符合技术文章标题规范）： # Algorithmic Art: Integrate p5.js Algorithmic Art into AI Programming Assistants with Agent Skill
### Official Creative Skill: algorithmic-art from Anthropic This skill generates interactive algorithmic art based on p5.js and seed randomization. After verifying against the official SKILL.md document, this article explains its two-stage workflow of "algorithmic philosophy + HTML finished product", the constraints of the viewer.html template, as well as the installation methods and typical prompt words in Cursor, Claude Code and Claude.ai. It is suitable for developers who want to quickly create generative art or learn creative Agent Skills.
Read More# unit-test-generator: Let AI Agent Automatically Generate Unit Tests According to Specifications
The unit-test-generator is a community example Skill in the JackyST0/awesome-agent-skills repository. It standardizes the workflow of "identifying code, analyzing boundaries, selecting frameworks, and generating test cases" via SKILL.md, and supports multiple programming languages including Python, JavaScript/TypeScript, Java, Go, Rust, etc., as well as testing frameworks such as pytest, Jest, JUnit, etc. This article introduces its positioning, core capabilities, installation methods on platforms like Cursor/Codex, official divide function examples, applicable scenarios such as test case supplementation and team specification implementation, as well as usage notes.
Read MoreCode Review: Equip AI Programming Assistants with "Standardized Code Review Workflows"
code-review is an official sample Skill built into the awesome-agent-skills repository. It defines a unified code review process for AI programming tools such as Cursor, Claude Code, and Copilot via SKILL.md. It covers five types of checks including syntax, logic, security, performance and style, and outputs a standard report containing problem lists, improvement suggestions and three scores. This article introduces its origin, core capabilities, one-click/manual installation methods, typical usage and applicable scenarios, to help developers standardize AI-assisted code review quickly.
Read More### 正式译法（适配技术文档场景）： Setting up CI: Let AI Agents Help You Build GitHub Actions CI/CD Pipelines
# setup-ci from awesome-cursor-skills: A beginner-friendly DevOps skill for GitHub Actions This skill detects Node.js, Python, Go and other projects via the SKILL.md specification, then generates a `ci.yml` workflow that includes linting, testing, type checking and optional deployment. It emphasizes the use of `npm ci`, dependency caching and secret security best practices. This article covers installation (deploying to `.cursor/skills/`), typical usage and precautions, and is ideal for quickly setting up CI/CD for new projects.
Read MoreFour Perspectives on One PR: Practical Combat of Parallel Code Review Skills
### 翻译版本1（正式技术文档风格）： `parallel-code-review` is a Cursor-Native Skill in the `awesome-cursor-skills` repository. It leverages the Task tool to launch four read-only explore sub-agents simultaneously to review the same code diff across four dimensions: security, performance, correctness, and readability, before the main agent deduplicates and consolidates the results into a hierarchical report. After verifying against the official `SKILL.md`, this article introduces the installation methods (via `.cursor/skills/` or `npx skills`), four-step workflow (scope the changes, conduct parallel review, synthesize the report, and optional repair), as well as applicable scenarios and limitations. ### 翻译版本2（更贴合开源社区表述习惯）： `parallel-code-review` is a Cursor-native skill hosted in the `awesome-cursor-skills` repo. It uses the Task tool to spin up four read-only explore sub-agents at once to audit the same code diff from four perspectives: security, performance, correctness, and readability. The main agent will then deduplicate and merge the feedback into a tiered report. This article first cross-checks with the official `SKILL.md`, then walks through the installation steps (either via `.cursor/skills/` directory or `npx skills`), the four-stage workflow (select the changes to review, run parallel audits, compile the final report, and apply fixes optionally), plus the applicable use cases and current constraints of the skill.
Read MoreExtracting Skills from Repeated Patterns: Detailed Explanation of the Meta-skill building-skills-from-patterns
### 正式翻译（符合技术文档语境）： `building-skills-from-patterns` is a meta-skill in the `awesome-cursor-skills` repository. It teaches the Cursor Agent to recognize recurring multi-step workflows and save them as `SKILL.md` files under the `.cursor/skills/` directory. This article introduces its trigger conditions, four-step workflow, division of labor with Rules and Hooks, as well as installation methods and typical use cases, helping developers advance from "repeatedly dictating workflows" to "building reusable Skills on their own".
Read More# Systematic Debugging: Integrate Structured Debugging Methodology into Skill to Enable AI Troubleshooting Without Blind Guesses
### 正式翻译： The `systematic-debugging` repository comes from `awesome-cursor-skills`. It will write the five-step debugging process of reproduction, isolation, hypothesis, verification and repair into `SKILL.md`, covering git bisect, binary search and minimal reproduction. This article introduces its origin, core capabilities, installation methods in AI programming tools such as Cursor, as well as typical usages such as automatic triggering and explicit invocation via `/systematic-debugging`, helping developers constrain Agents to troubleshoot based on evidence rather than randomly modifying code.
Read More# 翻译结果： Architecture Decision Records: Documenting Technical Decisions with Agent Skill
### Translation: Let me introduce the *architecture-decision-records* skill included in awesome-cursor-skills: it teaches AI Agents to document technical decisions in accordance with ADR specifications, covering their backgrounds, alternative options and justifications. The skill covers when to write ADRs, template structures, a six-step workflow, installation methods and typical prompt examples, helping teams turn architectural governance from verbal agreements into retrievable decision logs.
Read MoreAdding E2E Tests: Building Playwright End-to-End Tests from Scratch with Agent Skill
### 正式翻译： # Adding E2E Tests from awesome-cursor-skills: Teach AI Agents to Build Playwright E2E Testing from Scratch This tutorial covers end-to-end setup with Playwright: installing dependencies, configuring `playwright.config.ts`, writing smoke tests and Page Objects, supplementing npm scripts and `.gitignore`, integrating GitHub Actions CI, and emphasizes best practices for `data-testid` and Playwright Codegen. It explains the positioning of this skill, its differences from webapp-testing, installation methods for Cursor/Claude Code, and a seven-step implementation workflow, making it suitable for frontend and full-stack projects that have not yet adopted E2E testing.
Read MoreAdding Auth: Enabling AI Agents to Integrate with Auth.js Authentication via Standard Procedures
*adding-auth* has been included in awesome-cursor-skills. The integration of Auth.js v5 with Next.js App Router has been organized into an 8-step workflow: dependency installation, setting up AUTH_SECRET, auth.ts configuration, OAuth setup, Route Handler implementation, login UI and route protection, and optional database Adapter. Verified against the official SKILL.md and Auth.js documentation, this article introduces the installation method and typical usage, suitable for web developers who need to quickly integrate OAuth and sessions.
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