Skills Plugins MCP Prompt Model 博客 我的中心

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.

DeepseekModel Curated skill Quality Excellent · 90 v1.0.0

Get

https://deepseekmodel.com/api/download.php?id=affaan-m-ecc-agents-skills-plan-canvas-skill-md&format=skill
Download .skill Standard format with system_prompt and model_config, ready for any agent framework
The actual content of the system_prompt field in the .skill file.
name plan-canvas description 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. metadata {"origin":"ECC"} 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. # 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: { "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: 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: # 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. ```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: 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: # 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 .
Keywords that activate this skill. Click one to copy it.

This skill does not provide trigger words.

The downloaded .skill package contains the following fields.
Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
.skillpro Enhanced format with scripts, tools, dependencies and hooks Download
.json Plain JSON export with system_prompt and model parameters only Download
Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

每日精选 Skill 推荐,免费送到你邮箱

输入邮箱,每天接收一个精选 AI Agent 技能推荐。完全免费,持续更新。

验证码 --

提交后我们会发送一封确认邮件,点击邮件里的链接才会开始收信。

完全免费,取消任意时间。我们不会发送垃圾邮件。