# Codex Features and Practical Usage ☁️ Codex fixes repos you haven't even cloned locally, working on them in parallel in the cloud and turning the diffs into PRs. A way of working that never ties up your own machine. 🏷️ Title: Codex Cloud 🔗 URL: 📘 Overview Codex Cloud lets you delegate tasks to the cloud, where Codex works in the background in its own environment. It can run multiple tasks in parallel, and when you delegate work it clones your repository into that cloud environment. When done, it turns the diff into a pull request automatically. ⚙️ How It Works ・Tasks can be delegated from several entry points: the web platform, the IDE extension, and GitHub integrations, all running in parallel inside cloud environments. ・On delegation, the repo is cloned into the cloud environment, giving an isolated setup free of local-machine dependencies. ・You shape the environment with setup scripts (initialization before a task runs), environment configuration (repo selection, tools, dependencies), and internet access controls (whether public networking is allowed). ・Results become PRs automatically, linked to the originating GitHub issue, or you can apply the diff locally through the IDE extension. ・Where the local CLI runs on your machine with direct filesystem access, Cloud offloads execution to managed containers, giving you parallelization, background processing, and lower local resource use. 🛠️ Practical Usage ・Go to and connect your GitHub account to enable repo integration and PR creation. ・For reproducibility, define dependency installs and build prep in the environment's setup script. Enable internet access selectively, only for tasks that truly need external resources. ・Keep prompts clear and constrained with the right level of detail rather than vague. 💡 Use Cases Fire off several independent bugs or small features as concurrent cloud tasks, then review the resulting PRs one by one for parallel development. Even for a repo you've never set up locally, a working setup script lets Codex start immediately. ⚠️ Caveats ・Cloud execution runs in containers with defined resource limits and cannot reach local-only tools or private networks without explicit configuration. ・Always review the diff before merging an auto-generated PR. ・Enable internet access only when needed, in line with your security requirements. #OpenAICodex# #CodexCloud#

# Codex Features and Practical Usage 💬 Just mention `@/Codex` in a bug-report thread. It spins up a cloud task with the whole conversation as context and comes back with a fix PR. 🏷️ Title: Chat / Ticket Integration (Slack / Linear) 🔗 URL: 📘 Overview The Slack integration kicks off a Codex cloud task whenever you mention `@/Codex` in a channel or thread. Thread context is included automatically, so you don't have to restate the earlier discussion. Linear works similarly, launching tasks from a ticket. ⚙️ How It Works ・Mention `@/Codex` with a prompt in a thread and the bot detects it, starting a cloud task. Codex reacts with 👀 and replies with a task link. ・Alongside your message, the preceding thread history is sent, so Codex acts with full awareness of the prior discussion. On completion it posts results or threaded replies depending on workspace settings. ・The environment is chosen automatically: Codex matches accessible environments against your request, falls back to your most recently used environment when ambiguous, and runs against the default branch of the first repository in that environment's config. ・Enterprise admins can disable answer posting in ChatGPT workspace settings, limiting replies to task links only so sensitive environment details don't surface in Slack. 🛠️ Practical Usage ・You'll need a ChatGPT Plus plan or higher, a connected GitHub account, at least one configured cloud environment, and (workspace-dependent) Slack admin approval. ・To target a specific repository, name it in the mention, e.g. `@/Codex fix the above in openai/codex`. ・For complex threads, summarize the key points in your latest message so context isn't lost. 💡 Use Cases When a customer bug report blows up in a thread, ask `@/Codex` to fix it once the conclusion is clear. Without opening the repo locally, the assignee gets a context-aware fix PR. From Linear, the same flow launches a task using the ticket's content as context. ⚠️ Caveats ・If GitHub or Slack connections lapse, you'll get prompts to reconnect. ・When the environment is ambiguous, Codex might run against an unintended repo, so name the target explicitly for important requests. ・For detailed Linear mechanics, consult the Linear-specific documentation rather than this Slack-focused page. #OpenAICodex# #Slack#

# Codex Features and Practical Usage 🔍 Open a PR and get back only the genuinely serious findings, not noise. Codex's GitHub integration posts automated code reviews scoped to high-priority P0/P1 risks. 🏷️ Title: PR Review (@/codex review) 🔗 URL: 📘 Overview Codex's GitHub integration has the AI review a pull request's diff and post high-signal feedback. By design it filters to only P0 and P1 issues, so review comments stay focused on high-priority risks instead of drowning you in nitpicks. ⚙️ How It Works ・For a manual review, comment `@/codex review` on the PR. Codex reacts with 👀 and then posts a GitHub code review. ・Enable Automatic reviews in settings and a review runs the moment a PR is opened, with no mention required. ・Steer the review with a "Review guidelines" section in a top-level `AGENTS.md`. Codex applies the closest `AGENTS.md` to each changed file, so you can nest deeper instructions for package-level scrutiny. ・Mentioning `@/codex` with anything other than `review` kicks off a cloud task using the PR as context, e.g. `@/codex fix the CI failures`. 🛠️ Practical Usage ・First enable the Code review toggle for your repo in the settings (Codex cloud setup is a prerequisite). ・To lock in your review lens, add a "Review guidelines" section to `AGENTS.md` with rules like "Don't log PII" and "Verify authentication middleware wraps every route." ・For a one-off focus, append it inline: `@/codex review for security regressions`. ・To go from review straight to a fix, comment `@/codex fix the P1 issue` and Codex starts a cloud task to address it. 💡 Use Cases On a team where reviewers are stretched thin, let Codex do a first pass to clear P0/P1 issues before human review, cutting down review round-trips. Codify non-negotiable concerns like security and PII handling in `AGENTS.md` so every PR is checked for them automatically. ⚠️ Caveats ・Requires Codex cloud setup on the repo and code review enabled for it. ・The manual trigger must be the exact string `@/codex review`. ・Automatic reviews depend on the toggle being configured correctly, so verify settings during onboarding. #OpenAICodex# #CodeReview#

# Codex Features and Practical Usage 🔌 Turn Codex from an agent that only touches code into a partner that drives your issue tracker, database, and design tools directly. The key is MCP (Model Context Protocol). 🏷️ Title: External Tool Connectivity (MCP) 🔗 URL: 📘 Overview MCP is the standard protocol that connects Codex to external tools and context. Register an MCP server and Codex can call that tool directly to read and write. Configuration is shared across the CLI and IDE extension, so the same setup works no matter which client you use. ⚙️ How It Works ・The simplest way to add a server is via the CLI: `codex mcp add -- `. Inside the TUI, `/mcp` lists your active servers. ・There are two transports: STDIO servers, which start a local process (configure `command`, `args`, `env`, `cwd`), and Streamable HTTP servers, accessed by URL (configure `url`, `bearer_token_env_var`, `http_headers`). ・Authentication supports Bearer tokens sourced from environment variables, or OAuth. For OAuth-capable servers, run `codex mcp login `. ・You can govern tools per server: `enabled_tools` / `disabled_tools` for allow and deny lists, and `default_tools_approval_mode` (`auto`/`prompt`/`approve`) for the approval style. ・Tunables include `startup_timeout_sec` (default 10s), `tool_timeout_sec` (default 60s), and `required` (fail Codex startup if the server can't initialize). 🛠️ Practical Usage ・To add Context7 for up-to-date developer docs, it's a one-liner: `codex mcp add context7 -- npx -y @/upstash/context7-mcp`. ・For finer control, edit `~/.codex/config.toml` (or project-scoped `.codex/config.toml` for trusted projects) and define an `[mcp_servers.figma]` entry with its `url`, a `bearer_token_env_var`, and any `http_headers`. 💡 Use Cases Hand Codex an end-to-end flow: read the spec from your issue tracker, implement against the live DB schema, and pull a Figma design to build the UI. Wrap an internal API as an MCP server and Codex can invoke your custom business logic directly. ⚠️ Caveats ・You're granting external tools read/write access, so narrow permissions with `disabled_tools` and approval modes for safety. ・Don't hardcode tokens in the config file; read them from environment variables via `bearer_token_env_var`. ・For plugin-provided MCP servers, you don't specify the startup command yourself; you control enabled state and tool policy under `plugins..mcp_servers.`. #OpenAICodex# #MCP#

# Codex Features and Practical Usage 🧩 Tired of re-explaining the same workflow to Codex every time? Agent Skills let you package a reusable playbook once, and Codex loads it only when it is actually needed. 🏷️ Title: Agent Skills (SKILL.md) 🔗 URL: 📘 Overview Agent Skills package routine workflows into a single `SKILL.md` file you can reuse. Only a skill's name and description sit in Codex's context at all times; the full body loads when Codex decides to invoke it. You scope skills by where you place them: repository-wide, personal, or machine-level. ⚙️ How It Works ・A skill is a folder with `SKILL.md` (required) plus optional `scripts/` (executable code), `references/` (docs), `assets/` (templates), and `agents/openai.yaml` (UI config). ・`SKILL.md` opens with frontmatter containing `name` and `description`. A good description states clearly when the skill triggers and what its boundaries are. ・Codex scans several locations in priority order: the repo's `.agents/skills`, `$REPO_ROOT/.agents/skills`, your personal `$HOME/.agents/skills`, the admin path `/etc/codex/skills`, and OpenAI's bundled built-in skills. ・Progressive disclosure keeps only names, descriptions, and paths in the initial context (capped around 8,000 characters). The full body loads on invocation, so many installed skills won't bloat the prompt. 🛠️ Practical Usage ・The easiest way to author one is the built-in `$skill-creator`. It walks you through what the skill does, when it triggers, and whether to bundle scripts (instruction-only is the default). ・Invoke explicitly with `/skills` in the CLI/IDE, or mention a skill by name like `$skill-name`. Codex also selects skills implicitly when your task matches the description. ・To forbid implicit selection for a skill, set `policy.allow_implicit_invocation` to `false` in `agents/openai.yaml`. ・To disable a skill without deleting it, add a `[[skills.config]]` entry with its `path` and `enabled = false` in `~/.codex/config.toml`. 💡 Use Cases Encode error-prone, brittle routines such as release procedures, how to run the E2E suite, or the correct way to use an internal library. Anyone on the team then gets consistent results from Codex. Put shared workflows under the repo and personal habits under `$HOME/.agents/skills` for a clean split. ⚠️ Caveats ・Duplicate skill names across locations are not merged; both appear in the selector, so keep names unique. ・A vague description breaks implicit matching. Front-load key use cases and be concrete. ・With many skills installed, descriptions may be shortened to save context. If changes don't show up, restart Codex. #OpenAICodex# #AgentSkills#

# Codex Features and Practical Usage 📝 Stop repeating the same context in every prompt. AGENTS.md is a layered instruction file that teaches Codex your build steps and code conventions for good. 🏷️ Title: Hierarchical Project Instructions (AGENTS.md) 🔗 URL: 📘 Overview AGENTS.md files are persistent instructions Codex reads before starting work. Encode your project norms, build commands, and code standards in Markdown, and Codex automatically discovers and applies them. The defining trait is a layered model where files closer to your current directory take precedence. ⚙️ How It Works Codex builds an instruction chain with strict precedence. ・Global (`~/.codex/`): checks `AGENTS.override.md` first, then `AGENTS.md` ・Project (Git root down to cwd): at each level checks `AGENTS.override.md`, then `AGENTS.md`, then custom fallback names ・Merge: files concatenate from the root downward with blank lines; later files (closer to cwd) override earlier ones It stops adding files once the combined size hits `project_doc_max_bytes` (32 KiB default). Naming conventions: ・`AGENTS.md`: the standard instruction file ・`AGENTS.override.md`: a temporary replacement that takes precedence at its level ・fallback names: configurable via `project_doc_fallback_filenames` (e.g. `TEAM_GUIDE.md`) 🛠️ Practical Usage Split by role for best effect. ・`~/.codex/AGENTS.md` (global): universal personal preferences, e.g. "Always run `npm test` after editing JS," "Prefer `pnpm`" ・repo-root `AGENTS.md`: team norms like linting standards, documentation expectations, and review criteria ・subdirectory `AGENTS.override.md` (e.g. `services/payments/`): override broader rules for a specific domain without deleting parent guidance Verify what loaded by asking Codex to summarize the current instructions, e.g. `codex --ask-for-approval never "Summarize the current instructions."` 💡 Use Cases In a monorepo where only the payments service needs a different test command or review bar, dropping a `services/payments/AGENTS.override.md` switches the rules on only when Codex works under that path. The "Review guidelines" you write here also apply to GitHub's `@/codex review`. ⚠️ Caveats Codex rebuilds the instruction chain on every run, so there is no cache to clear — if guidance looks stale, restart in the target directory. Empty files are skipped and non-existent fallback names are ignored. The `CODEX_HOME` environment variable overrides the default profile location. #OpenAICodex# #AGENTSmd#

# Codex Features and Practical Usage 🚀 "One agent for everywhere you code." OpenAI Codex is an AI coding agent you can hand entire tasks to — from generation to understanding, review, and debugging. 🏷️ Title: Codex Fundamentals 🔗 URL: 📘 Overview Codex is OpenAI's AI coding agent for software development. Rather than just autocompleting code, it reads your existing project structure and conventions and carries out tasks autonomously. It is built into the ChatGPT Plus, Pro, Business, Edu, and Enterprise plans. ⚙️ How It Works Codex centers on five core capabilities. ・Code generation: describe what you want, and it writes code that fits your existing structure and naming conventions. ・Codebase understanding: it reads complex or legacy code and explains how the system is organized. ・Code review: it surfaces bugs, logic errors, and unhandled edge cases. ・Debugging: it traces failures, diagnoses root causes, and proposes targeted fixes. ・Task automation: it handles refactors, tests, migrations, and setup workflows. Underpinning all of this are two foundations that keep it safe: a sandbox that defines execution boundaries, and an approval policy that decides when to stop and ask. 🛠️ Practical Usage Codex's hallmark is that it runs "everywhere you code," through several entry points. ・CLI: launch `codex` in your terminal and work interactively ・IDE extension: delegate right from your editor ・Web / cloud: run tasks on repos you do not have locally, in parallel ・GitHub integration: ask for a review with `@/codex review` on a PR ・Slack integration: mention `@/codex` in a thread to kick off a task A good path is to start with the CLI via `npm i -g @/openai/codex`, then expand into GitHub and Slack as you get comfortable. 💡 Use Cases Practical patterns include: on day one in an unfamiliar repo, asking "Tell me about this project" to grasp the big picture; having bugs cleaned up before review; or delegating a tedious bulk refactor wholesale. Humans stay focused on direction and review. ⚠️ Caveats Codex is an autonomous agent that reads/writes files and runs commands. Create Git checkpoints (commits) before and after tasks so you can always roll back safely. Authenticating with a ChatGPT account is recommended; some functionality may be limited with API-key auth. #OpenAICodex# #AICoding#

OpenAI Codex tracked ARR reached $8.83B for the week ending Aug. 10, representing an estimated 45.0% of OpenAI’s enterprise ARR. Latest tracked growth: +20.8% MoM. Full tracking update: