Plan Canvas logo

Plan Canvas

CommunityPopular
affaan-m
plan-canvas

Open plans and HTML artifacts in a local browser canvas where the human annotates elements, chats, and approves or requests changes without leaving the page. Use when presenting a plan for review, or when feedback like "move this, change that" is easier pointed at than typed.

Overview

Publisheraffaan-m
RepositoryECC
Skill nameplan-canvas
Stars
270.2K
Forks
40.4K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by affaan-m on GitHub. Read the source before you install it.

Installation

Install the Plan Canvas AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/affaan-m/ECC/tree/main/.agents/skills/plan-canvas
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/plan-canvas .claude/skills/plan-canvas
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Canvas in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Plan Canvas on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Plan Canvas is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Plan Canvas

Review loop for plans and visual artifacts: you write the artifact, the human reviews it in the browser — annotating the exact element they mean, chatting, and delivering an Approve plan / Request changes verdict — while you block on a single CLI call that returns their feedback as JSON.

Inspired by lavish-axi; rebuilt ECC-native around the /plan confirmation gate, with zero dependencies.

When to Use

  • You just wrote a plan artifact (.claude/plans/*.plan.md from /plan) and need the CONFIRM/approve decision — the canvas verdict replaces a typed "yes/proceed".
  • The user should point at what to change: reviewing designs, comparisons, reports, or any local .md / .html artifact.
  • The user asks for /plan-canvas, a visual review, or "open it in the browser".

Do NOT use for: code review of diffs (/code-review), running web apps, or remote URLs. The canvas serves local artifact files only.

How It Works

Invoke the CLI as ecc-plan-canvas — the bin shipped by the ecc-universal package (on PATH after a global/plugin install; node "$CLAUDE_PLUGIN_ROOT/scripts/plan-canvas.js" also works for plugin installs). Run it from the project you are reviewing in; it works from any working directory. It manages a detached loopback server (127.0.0.1:4517) shared by all sessions, keyed by artifact path — no session ids to track.

The workflow is a plain CLI-plus-JSON loop, so it is model- and harness-agnostic: any agent that can run a shell command and read stdout drives it the same way (Claude Code, Codex, Cursor, Gemini, OpenCode, Copilot). Trigger it however your harness surfaces skills — e.g. /plan-canvas in Claude Code, $plan-canvas in Codex — or just run the ecc-plan-canvas commands directly.

bash
# 1. Open the artifact in the user's browser (returns immediately)
ecc-plan-canvas open .claude/plans/feature.plan.md

# 2. Block until the human responds. Leave running; re-run if interrupted:
#    queued feedback is never lost.
ecc-plan-canvas await .claude/plans/feature.plan.md

Stay listening, or the human talks to an empty chair

Feedback only reaches you while an await is actually parked on the session. If your turn ends with nothing listening, the message sits in the queue and, from the human's side of the glass, sending appears to do nothing at all.

So run await as a background task when your harness supports one (in Claude Code, a Bash call with run_in_background: true). It exits the moment feedback arrives and the harness hands you the JSON, which keeps the loop alive across turns instead of dying with the foreground call. A foreground await works too, but only until the harness time-limits it.

Two backstops exist, and neither is an excuse to skip the above:

  • ecc-plan-canvas pending lists feedback queued with no listener. Check it whenever you are unsure whether you missed something.
  • The stop:plan-canvas-pending hook blocks your turn from ending while canvas feedback is undelivered, and hands you the messages. If you are reading feedback from that hook, you stopped listening too early.

await prints JSON when the human acts:

json
{
  "status": "feedback",
  "items": [
    { "kind": "annotation", "text": "Split this into two phases",
      "anchor": { "selector": "h2:nth-of-type(3)", "tag": "h2", "snippet": "Phase 2: Migration" } },
    { "kind": "verdict", "verdict": "request-changes" }
  ]
}
  • kind: "chat" — freeform message; answer in the canvas, not the terminal.
  • kind: "annotation" — feedback anchored to an element (anchor.selector, anchor.snippet show what they pointed at; anchor.textRange.text when they highlighted a passage).
  • kind: "verdict" — approve means the plan is CONFIRMED: stop polling, end the session, and start implementing. request-changes means revise the artifact (the canvas live-reloads it) and keep the loop going.

3. Always respond in the canvas, then keep listening. One command does both:

bash
ecc-plan-canvas await <file> --reply "Split Phase 2 as requested. Take a look."

Every human message gets a reply in the canvas, even a one-liner like "On it, rewriting the risk table now." Silence in the chat panel is indistinguishable from a broken canvas, which is exactly the failure this loop exists to prevent. Answer there, not only in the terminal.

While you work, keep the chat honest with the activity indicator:

bash
# animated "agent is thinking..." bubble; refresh it during long work
ecc-plan-canvas typing <file> --state thinking
# switch to "agent is typing..." just before a reply lands
ecc-plan-canvas typing <file> --state typing

await sets thinking for you the moment it hands you a batch, and --reply clears it. Both states self-expire, so a crashed agent decays to an honest "queued" instead of leaving the human watching dots forever. Refresh thinking if a revision takes more than a minute.

4. End when review concludes: ecc-plan-canvas end <file>.

Diagrams (Mermaid)

When part of the plan is a flow, architecture, sequence, state machine, ER model, or dependency graph, author it as a fenced ```mermaid block instead of ASCII art or a wall of prose — the canvas renders it as a themed diagram the human can point at. Reach for it when a picture reads faster than a paragraph; skip it for simple lists or tables.

markdown
```mermaid
flowchart LR
  A[Market resolves] --> B{Watchers?}
  B -->|yes| C[Enqueue jobs] --> D[Fan-out worker]
```

Diagrams render in the ECC dark theme with the accent palette. Mermaid loads in the browser from a pinned CDN; if that is unavailable (offline), the block degrades to showing its source, so the review is never blocked. Point a local mirror at ECC_PLAN_CANVAS_MERMAID_URL for air-gapped use.

Rules

  • Markdown artifacts render in ECC's plan template (including Mermaid blocks); .html artifacts render as-is with the annotation layer injected. For HTML authoring guidance use the frontend-design-direction and artifact-design skills.
  • Edit the artifact file to revise — the canvas live-reloads on save. Never re-run open to refresh.
  • {"status": "ended", "endedBy": "user"} (or sessionEnded: true on a feedback batch) means the user closed the review: stop polling, deliver remaining updates in chat, and do not reopen. A plain open on that session is refused; pass --reopen only when the user asks to resume.
  • Sibling assets (images, CSS) must sit next to the artifact and be referenced by relative path.
  • The server is loopback-only and exits after 30 idle minutes (ECC_PLAN_CANVAS_IDLE_MS); stop shuts it down explicitly. State lives in ~/.claude/plan-canvas/ (ECC_PLAN_CANVAS_STATE_DIR).

Examples

Plan approval flow — /plan writes .claude/plans/notifications.plan.md and must WAIT for confirmation:

bash
ecc-plan-canvas open .claude/plans/notifications.plan.md
ecc-plan-canvas await .claude/plans/notifications.plan.md
# → {"status":"feedback","items":[{"kind":"verdict","verdict":"approve"}]}
ecc-plan-canvas end .claude/plans/notifications.plan.md
# plan is confirmed — begin implementation

Revision loop — feedback arrives, you edit the file, reply, keep listening:

bash
# await returned annotations → edit the .plan.md (canvas live-reloads)
ecc-plan-canvas await <file> --reply "Reworked the risk table."
# → blocks again until the next response

Anti-Patterns

  • Polling with --timeout-ms in a loop. It exists for tests. Leave the plain await running instead.
  • Ending your turn with no await listening while the review is still open. That is the one failure the human experiences as "I sent a message and nothing happened".
  • Reading the feedback but answering only in the terminal. The human is looking at the canvas.
  • Reopening after a user-initiated end "just to show" something.
  • Pasting the whole plan into chat and opening a canvas — pick the canvas and keep the terminal summary to one line.
  • Parsing the canvas chat from state files — everything you need arrives via await.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Plan Canvas AI skill do?

Open plans and HTML artifacts in a local browser canvas where the human annotates elements, chats, and approves or requests changes without leaving the page. Use when presenting a plan for review, or when feedback like "move this, change that" is easier pointed at than typed.

Why use Plan Canvas on TypingMind?

Because you install it once and use it with any model. Plan Canvas is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Plan Canvas in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/plan-canvas. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Plan Canvas?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Plan Canvas?

As many as you like. As long as a model supports skills, you can use Plan Canvas with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Plan Canvas AI skill free?

Yes. It is published on GitHub by affaan-m under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇