Use when an agent needs to delegate a task to the OpenAI Codex CLI from another agent environment such as Claude Code, OpenClaw, or similar. Covers checking whether Codex CLI is installed, running one-off Codex prompts with `codex exec`, resuming sessions, collecting outputs, attaching images or files as input with `-i`/stdin, and handling Codex image generation including finding and reporting generated image file paths.
npx skills add https://github.com/sugarforever/01coder-agent-skills --skill codex-cli
Use this skill when the user asks this agent to talk to Codex, consult Codex, delegate a coding/review/research task to Codex CLI, or ask Codex to generate an image.
The core interface is:
codex exec "prompt"
For automation, prefer non-interactive codex exec over interactive codex.
Before using Codex CLI, verify it is available:
command -v codex
codex --version
codex exec --help
If codex is missing, tell the user Codex CLI is not available in this environment and stop. Do not simulate a Codex response.
If codex exec --help is available, use it as the source of truth for the installed CLI flags. Codex CLI changes over time.
Use this for normal delegation:
codex exec -C "$PWD" -s read-only "Review this repository and identify likely bugs"
For tasks that may edit files, use the sandbox the user requested or the host agent allows:
codex exec -C "$PWD" -s workspace-write "Implement the requested change"
For a response file:
codex exec -C "$PWD" -o /tmp/codex-last-message.md "Summarize this project"
For machine-readable events:
codex exec -C "$PWD" --json -o /tmp/codex-last-message.md "Analyze this project"
If the prompt is long, pass it via stdin:
codex exec -C "$PWD" - < /tmp/prompt.txt
When using shell commands from another agent, avoid dangerous flags unless the user explicitly asked for them. Prefer read-only for review, planning, critique, image generation, and analysis.
For non-trivial tasks, shape the delegated prompt with OpenAI's Codex best-practices structure:
Goal:
Context:
Constraints:
Done when:
Keep the prompt scoped to one task. For complex implementation work, ask Codex for a plan first in read-only mode, then run a separate write task after the plan is accepted.
Codex emits a session id in normal startup output and in JSONL events as thread.started.
To resume:
codex exec resume <session-id> "Continue from the previous task"
If the installed CLI supports a different resume syntax, follow codex exec resume --help.
Codex cannot browse or read image files on its own. To let Codex *see* an image, attach it explicitly with -i/--image. This is the input side; image generation (below) is the output side.
Attach a single image:
codex exec -C "$PWD" -s read-only -i screenshot.png "Explain the error shown here"
Attach multiple images. Both forms work — comma-separated or a repeated flag:
codex exec -i before.png,after.png "Compare these two UI states"
codex exec -i mock1.png -i mock2.png "Which layout is closer to the spec?"
Best practices:
-i flags before the prompt text.-s read-only so Codex inspects but does not edit.For non-image files there is no attach flag. Two options:
-C "$PWD" and name the file in the prompt ("Review src/auth.ts"). Use this for files already in the repo.<stdin> block:codex exec -C "$PWD" "Summarize the attached log" < /tmp/build.log
Codex can generate images through its built-in image generation tool when asked through codex exec.
Example:
codex exec "Generate a black banana image with aspect ratio 16:9"
Important behavior:
$CODEX_HOME/generated_images/<codex-session-id>/.CODEX_HOME is unset, use ~/.codex/generated_images/<codex-session-id>/.After any Codex image-generation request, find the image path before replying.
First extract the Codex session id from output if available. It may look like:
session id: 019e8cbf-5054-7131-9440-ee592f0d8a17
Then check:
ls -l "${CODEX_HOME:-$HOME/.codex}/generated_images/<session-id>"
If the session id is not known, list recent generated images:
find "${CODEX_HOME:-$HOME/.codex}/generated_images" -type f -maxdepth 3 -print
When possible, sort by modification time:
find "${CODEX_HOME:-$HOME/.codex}/generated_images" -type f -maxdepth 3 -print0 \
| xargs -0 ls -lt
If the user requested a project asset, copy or move the selected image into the project after generation if permissions allow. Never leave a project-referenced asset only under $CODEX_HOME/generated_images.
If the user requested a specific aspect ratio, verify dimensions before answering.
On macOS:
sips -g pixelWidth -g pixelHeight /path/to/image.png
Portable fallback:
file /path/to/image.png
Report actual dimensions and whether they match approximately or exactly. Many generated images use nearest integer dimensions, so a 16:9 request may produce dimensions like 1672 x 941, which is effectively 16:9 but not mathematically exact.
For normal Codex tasks, report:
For image tasks, always report:
$CODEX_HOME or copied into the projectExample response:
Codex generated the image here:
/Users/me/.codex/generated_images/019e.../ig_....png
Actual size: 1672 x 941, effectively 16:9.
The output does not expose the exact image backend model, so I cannot verify whether it was gpt-image-2.
If Codex exits with network, auth, or model refresh errors, report the error plainly and include the relevant stderr lines.
If Codex claims it generated an image but no image file exists under $CODEX_HOME/generated_images, say so explicitly and do not invent a path.
If the host agent cannot read $CODEX_HOME/generated_images, ask the user to grant access or run:
find "${CODEX_HOME:-$HOME/.codex}/generated_images" -type f -maxdepth 3 -print0 | xargs -0 ls -lt
Do not pass secrets to Codex unless the user explicitly asks and understands the risk.
Use read-only for consultation. Use workspace-write only when Codex is expected to edit files. Avoid danger-full-access unless the user explicitly requested it in a controlled environment.
Do not claim Codex performed work solely from its final message. When file changes matter, inspect the filesystem or Git diff after the run.
agent-im 会话技能 - 通过 liblib.tv 的 AI 能力生成和编辑图片/视频。覆盖场景包括:生成(文生图、文生视频、图生视频、做动画、画一个xxx、来段xxx)、编辑修改(把xxx换成yyy、去掉xxx、加上xxx、改成xxx、调整xxx、局部修改、改镜头)、风格转换(风格迁移、转绘、换风格)、视频续写延长、复刻视频/TVC/宣传片、短剧/短漫剧生成、音乐MV生成、产品广告/展示片制作、分镜/故事板设计、教育视频/短视频制作。当用户提到 liblib、libtv、上传参考图/视频、查看生成进度时也应触发。关键判断:只要用户的请求涉及 AI 图片或视频的创作、生成、编辑、修改,无论措辞如何(如"画只猫"、"做个海报"、"把纸船换成爱心"、"这个视频帮我改一下"、"帮我复刻这段视频"、"用这首歌做个MV"、"一句话生成短剧"),都必须触发此技能。
This skill should be used when the user asks to "generate video prompts", "create Seedance prompts", "write video descriptions", mentions "Seedance", "seedance", "即梦", "即梦平台", "视频提示词", "视频生成", "AI视频", "短剧", "广告视频", "视频延长", or discusses video prompt engineering, AI video generation, or Seedance 2.0 workflows.
Best practices and techniques for writing effective AI video generation prompts. Covers: Veo, Seedance, Wan, Grok, Kling, Runway, Pika, Sora prompting strategies. Learn: shot types, camera movements, lighting, pacing, style keywords, negative prompts. Use for: improving video quality, getting consistent results, professional video prompts. Triggers: video prompt, how to prompt video, veo prompts, video generation tips, better ai video, video prompt engineering, video prompt guide, video prompt template, ai video tips, video prompt best practices, video prompt examples, cinematography prompts
This skill is a practical, 'use-it-while-debugging' reference for getting a LiveKit + Letta voice agent working reliably.
Download screenshot baselines from the latest CI run and commit them. Use when asked to update, accept, or refresh component screenshot baselines from CI, or after the screenshot-test GitHub Action reports differences. This skill should be run as a subagent.
| Turn vague taste, screenshots, URLs, product notes, or "make it feel like this" references into a grounded DESIGN.md plus an implementation handoff. Use it before prototypes, decks, redesigns, or image remix work when the user needs a reusable visual direction rather than a one-off prompt.
>- Upload local assets (images, mockups, extracted HTML, design markdown) to a Stitch project. ALWAYS use this skill when you need to upload visual assets, HTML pages, or design docs to Stitch, particularly when direct MCP tool calls fail or truncate due to base64 token limits.
This skill helps users automatically extract channel-level and video detail data from a specific YouTube channel via BrowserAct API. Agent should proactively apply this skill when users express needs like extracting channel video data, getting latest or popular videos from a YouTube channel, tracking competitor channel content, extracting video metrics such as views likes comments, retrieving subscriber count and channel info, monitoring posting cadence of a YouTube channel, gathering video data for content strategy analysis, getting earliest videos of a YouTube creator, analyzing engagement signals across a full channel, and downloading structured YouTube video details without manual scraping.
Take sugarforever/codex-cli from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.