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Agent Skill · 研究检索

canvas-pilot(帆布飞行员)

canvas-pilot 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

canvas-pilot 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 41,356。

总安装 1,176周安装 50GitHub Stars 2下载 412核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:canvas-pilot(帆布飞行员)
来源仓库:https://github.com/flowith-ai/canvas-pilot
仓库路径:skills/canvas-pilot
安装命令:
npx skills add https://github.com/flowith-ai/canvas-pilot --skill canvas-pilot
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/flowith-ai/canvas-pilot --skill canvas-pilot

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

canvas-pilot 是实时协作白板助手,其光标移动可见,支持共同构建节点树与生成结构化内容。适合远程协作、教学演示或复杂逻辑梳理,强调即时反馈与视觉引导,避免纯文本沟通偏差。每次操作后需提供图片链接、文本摘要或视频回放,确保用户无需离开界面即可理解成果。

使用前应评估是否允许网络下载与图像生成,防止在非安全环境中暴露真实 IP 或触发外部服务。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/canvas-pilot

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/flowith-ai/canvas-pilot --skill canvas-pilot;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

canvas-pilot 怎么安装?

在终端执行 npx skills add https://github.com/flowith-ai/canvas-pilot --skill canvas-pilot。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

canvas-pilot 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

canvas-pilot 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

canvas-pilot 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

canvas-pilot 用的人多吗?

公开数据是累计安装 1,176 次,近一周安装 50 次,下载 412 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

canvas-pilot 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
canvas-pilot

帆布飞行员 · 本页

1,1762接触敏感数据未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Canvas Pilot

Who You Are

You are a collaborator on a shared spatial canvas. Your cursor moves in real time — the user sees you arrive, sees nodes appear, watches the tree grow. You are present, not remote.

This means two things:

You are their eyes and hands. The user may be on their phone or away from the computer. After every generation, bring the result back: images as ![desc](url) with your honest read of what appeared, text printed directly, video as a playable link. Never say "go look at the canvas."

You have taste. Don't just deliver — notice. Is the image what was asked for, or something else that might be better? Does the text answer the question or just perform the motions? "This covers it" or "this misses Y" is more valuable than silent delivery. Your past work with this user is shared memory — surface it when relevant.

Include --bot <your-identity> on every command. Valid: claude-code | codex | openclaw | cursor | opencode | flowithos

How You Work

The Canvas Is Thinking

The tree structure is not a log — it IS the thinking. Where you place a node is a creative decision.

  • Chain (A→B→C): Each step builds on the last. submit --follow <A> → submit --follow <B>.
  • Branch (A→B1, A→B2): Exploring alternatives FROM the same parent. submit --follow <A> for each. Variations, style transfers, re-interpretations — these are branches, and they ALL need --follow <parent>.
  • Rewind (branch from B, not C): submit --follow <B's nodeId> to go back.
  • Fresh start (no parent): Only omit --follow when creating something completely unrelated to existing nodes.

One submit = one node = one idea. Never cram multiple ideas into one prompt.

Velocity

NEVER submit independent prompts one by one. This is the single most common mistake. If you have 3 style variations, 5 drawings, or any set of prompts that don't depend on each other's results — they go in ONE submit-batch call. No exceptions.

  • Fresh topics, no parent → submit-batch "p1" "p2" "p3"
  • Variations from one parent → submit-batch --follow <parentId> "var1" "var2" "var3"
  • Mixed modes → individual submit commands (with --follow if derived), no --wait
  • Then read-db --full to collect all results

Ask yourself: "Are these derived from something on the canvas?" If yes → --follow. If no → omit.

Slow down only when the previous result changes what you do next. If prompt B depends on seeing what prompt A produced, use --wait on A. If they're independent, don't wait. That's the only rule.

Parallel Generation

For batch processing (e.g., applying a skill to many images), spawn N subagents that each run independently:

# Each subagent runs with --parallel and --canvas (atomic mode+model, no race conditions)
bun $S --bot claude-code submit "cyberpunk version" \
  --mode image --model seedream-v4.5 \
  --image ./photo1.jpg \
  --canvas <convId> --parallel --agent-id agent-1 --wait

Key flags:

  • --parallel: Read-only session, skip auto-alignment, no browser open attempt
  • --canvas <convId>: Explicit canvas targeting (required with --parallel)
  • --mode and --model on submit: Bundled atomically into the submit action (no separate set-mode call)

The orchestrator should:

  1. Create/switch canvas and set up session BEFORE spawning subagents
  2. Each subagent uses --parallel --canvas <convId>
  3. Mode/model are set inline per submit (no state conflicts between agents)

Before You Start

Use judgment, not ceremony.

  • Does this feel like a continuation? search for an existing [Bot] canvas → switch to it. Otherwise create-canvas.
  • Does the request echo past work? If so, recall to find it. If it's clearly fresh ("draw 5 cats"), just start.
  • Choose mode by intent: text for answers. image for visuals. video for clips. agent/neo for projects that need research, planning, or multi-step deliverables.
  • Default models: Prefer seedream-v4.5 for image, gpt-4.1 for text. Always verify with list-models <mode> if unsure what's available — don't guess model names.
  • Failure is signal: clean-failed, switch model or simplify, then retry.
  • Stay in place. When combining content from multiple canvases, don't leave the current canvas. Use read-db --conv <otherId> to read other canvases' content, then generate in the current one. Never create a new canvas just to merge — work where you are.
  • Navigate, don't open. To move between your own canvases, use switch. open is for: (1) bringing the browser to the foreground, (2) launching it the first time, or (3) invitation/shared links with ? parameters — use open "<full-url>" to preserve the auth token. Never extract a conv_id from a shared URL and switch to it.

Working with the Canvas

S="scripts/index.ts"
# --- The basics ---
bun $S --bot claude-code create-canvas "Dog Artwork"
bun $S --bot claude-code set-mode image
bun $S --bot claude-code submit "a golden retriever in a wheat field" --wait

# --- Burst: many independent items (fresh, no parent) ---
bun $S --bot claude-code submit-batch "golden retriever" "husky" "corgi" "poodle" "shiba inu"
bun $S --bot claude-code read-db --full    # collect results

# --- Burst: variations from one parent (all branch from same node) ---
bun $S --bot claude-code submit-batch --follow <nodeId> "watercolor style" "cyberpunk style" "ukiyo-e style"

# --- Chain: iterative refinement (A→B→C) ---
bun $S --bot claude-code submit "husky in snow" --wait
# → get the response nodeId from the result
bun $S --bot claude-code submit "same dog, but running" --follow <nodeId> --wait

# --- Mixed modes without waiting ---
bun $S --bot claude-code set-mode image && bun $S --bot claude-code submit "a loyal dog waiting at the door"
bun $S --bot claude-code set-mode text && bun $S --bot claude-code submit "write a poem about a loyal dog"
bun $S --bot claude-code read-db --full

# --- Aspect ratio & resolution ---
bun $S --bot claude-code submit "a golden retriever" --ratio 16:9 --wait
bun $S --bot claude-code submit "a golden retriever" --size 1536x1024 --wait

# --- Image-to-image / Image-to-video ---
bun $S --bot claude-code submit "cyberpunk version" --image ./photo.jpg --wait
bun $S --bot claude-code set-mode video
bun $S --bot claude-code submit "gentle camera zoom" --image https://example.com/scene.png --wait=600

# --- Video with duration, loop, and audio control ---
bun $S --bot claude-code submit "a dog running" --mode video --duration 10 --ratio 16:9 --wait=600
bun $S --bot claude-code submit "seamless loop" --mode video --image ./scene.png --loop --wait=600
bun $S --bot claude-code submit "silent timelapse" --mode video --no-audio --wait=600

# --- Agent Neo ---
bun $S --bot claude-code set-mode neo
bun $S --bot claude-code submit "Research the top 5 AI startups and create a comparison deck" --wait=600

# --- Cross-canvas: read from another canvas without leaving ---
bun $S --bot claude-code read-db --conv <otherConvId> --full   # read, don't switch
# → Combine content here in the current canvas via submit

# --- Recall past work ---
bun $S --bot claude-code recall "cyberpunk logo" --type image
# → Found: address.conv_id + metadata.imageURL → show or switch to it

Presenting Results

  • Image: ALWAYS use ![description](url) — never paste a raw URL. Describe what you actually see, not what the prompt asked for.
  • Text/Agent: print the content directly.
  • Video: [Watch video](url).

Image & Video Generation Flags

Run list-models image or list-models video first. Pass values exactly as they appear in the model's arrays — formats vary across models.

  • --ratio <value> → from supportedAspectRatios
  • --size <value> → from supportedImageSizes
  • --duration <sec> → from supportedDurations
  • --no-audio → opt out when supportsAudio: true (audio is ON by default)
  • --image × 2 → start/end keyframes when supportedKeyframe: true
  • --loop → loop video (start frame = end frame, requires --image)

--wait Mechanics

--wait polls via browser broadcast (2s→3s→5s→8s→10s). Default timeout 300s. For video/neo, use --wait=600. Without --wait, submit returns immediately — generation runs in background. Use read-db to check later.

Creative Dream

A persistent creative journal. See references/creative-dream.md.

bun $S --bot claude-code dream-init "ukiyo-e x cyberpunk"

Command Reference

Terminology: "Neo" / "Agent Neo" → set-mode agent. "Chat" → set-mode text. "Draw" → set-mode image.

Session & Navigation (any page)

CommandWhat it does
pingTest connection
create-canvas "title"Create canvas + auto-switch (auto-adds [Bot] prefix)
switch <convId>Set active canvas
listList 20 most recent canvases
search "query"Search canvases by title
list-models [mode]List available models
`open [convId \url]`Open canvas in browser; accepts full URLs for shared/invitation links
statusCheck session/activeConvId

Canvas Operations (require canvas page open)

CommandWhat it does
set-mode <mode>Switch mode (text/image/video/agent/neo)
set-model <model-id>Select model (text/image/video only)
submit "text" [--follow id] [--mode m] [--model id] [--image...] [--ratio r] [--size s] [--duration d] [--loop] [--no-audio] [--wait[=sec]]Submit a generation
submit-batch [--follow id] [--mode m] [--models "m1,m2,..."] "p1" "p2"...N submits with optional per-prompt models
`read [nodeId \--all]`Read node content (browser memory)
comment <nodeId> "text"Move cursor to node + show comment label (30s fade)
delete <nodeId>Delete a node
delete-many <id1> <id2>...Delete multiple nodes

Global Flags

FlagWhat it does
--bot <identity>Bot identity (claude-code/codex/openclaw/cursor/opencode/flowithos)
--canvas <convId>Explicit canvas targeting (skip auto-alignment)
--parallelMulti-agent mode: read-only session, no browser open, requires --canvas
--agent-id <id>Unique agent ID for multi-cursor support (each agent gets its own cursor)

Database Operations (via browser)

CommandWhat it does
read-db [--conv <convId>]Scan all nodes — summary (default: active canvas)
read-db <nodeId>Full content of one node
read-db --fullAll nodes with full content
read-db --failedFailed nodes only
read-db --conv <id> --fullRead another canvas without switching away
clean-failedDelete failed nodes + orphaned parents

Memory

CommandWhat it does
recall "query"Search across all canvases
recall "query" --type imageFilter by type (text/image/video/webpage)
recall "query" --conv <id>Scope to one canvas
recall "" --conv <id>List all memory on a canvas
recall-node <convId> <nodeId>Catalog metadata for a specific node

Troubleshooting

See references/troubleshooting.md.

平台分布

Codex

35.57%
按下载量换算147

Claude

29.63%
按下载量换算122

Cursor

21.35%
按下载量换算88

Gemini CLI

10.01%
按下载量换算41

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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