Canvas logo

Canvas

Organization
kansoku-trade
canvas

A canvas is a live React panel the user opens beside the chat, saved as journal/canvases/<slug>.canvas.tsx. You MUST use a canvas whenever you produce a standalone analytical artifact — multi-symbol comparisons, capital-flow or price reads across several names, event before/after studies, scenario-and-plan write-ups, session or weekly post-mortems, multi-period chart layouts, coverage-and-gap reports, or any answer that is carried by numbers laid out visually. If you catch yourself about to write a markdown table of market data, stop and build a canvas instead. You MUST also read this skill once per conversation before creating, editing, or debugging any .canvas.tsx file — save_canvas and apply_patch refuse until you have. Do not use a canvas for a single quote or one-line answer, or for the four fixed chart types (flow / cohort / sepa / intraday), which belong to the `chart` skill. Triggers: 画布、自定义面板、 拼一张图、并排对比、自定义图表、多标的对照, canvas, save_canvas, custom panel, side-by-side comparison.

Overview

Publisherkansoku-trade
Repositorykansoku
Skill namecanvas
Stars
329
Forks
40
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

    Published by kansoku-trade on GitHub. Read the source before you install it.

Installation

Install the 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/kansoku-trade/kansoku/tree/main/packages/core/skills/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/kansoku-trade/kansoku.git /tmp/kansoku
mkdir -p .claude/skills
cp -r /tmp/kansoku/packages/core/skills/canvas .claude/skills/canvas
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

A canvas is one .canvas.tsx file the app compiles so the user can open it beside the chat.

Rule text is English so it survives every runtime. Canvas content is output — it follows TD-LANG-01: 中文白话.

Workflow

1. Decide whether to use a canvas

The trigger is whether the numbers are the deliverable. If they are a step toward something else, skip the canvas.

Use one when: several symbols are compared across the same metrics; a read spans multiple periods or charts; an event is studied before and after; a directional call comes with scenarios or an entry/stop/target plan; a session or week is reviewed; any structured table longer than a handful of rows.

Do NOT when: the answer is one quote or one sentence; the user wants one of the four fixed chart types (chart skill); the user wants a journal entry or stock note (markdown under journal/ and stocks/); the data was an intermediate step; you do not have the numbers yet.

2. Fetch first, then embed

fetch_kline / read_data_pack / bash longbridge / research files. Write the numbers into the TSX, or offload them to a data file (see "Where data comes from" below). A canvas itself still cannot fetch — no sandboxed network access — but it can pull live quotes and K-line through the two hooks in that section.

Indicators are computed server-side and passed in; CandleChart draws, it does not compute (ema needs { label, points }, not periods). Attribute every number's vintage in the caption (TD-DATA-02). What you could not fetch goes in Coverage, never into a guess (TD-DATA-01).

3. Write the canvas

  • One file, saved via save_canvas({ slug, title, source }). Slug is kebab-case. A canvas may have sibling data files (see below); it has no other helper .tsx/.ts files.
  • Exactly one export default, the top-level component.
  • Import from @kansoku/canvas, or a same-directory JSON data file (./<name>.json). No other relative paths, no react, no node:, no npm.
  • Banned in source: fetch(, XMLHttpRequest, import(, require(, setTimeout / setInterval, document., window.. 64 KB limit.
  • Revising: if the current source is not already in this conversation (you wrote or read it earlier and nothing else changed it), use bash cat -- journal/canvases/<slug>.canvas.tsx first; otherwise patch directly. Do not re-read after a successful apply_patch — its result already confirms the write. Send one apply_patch call carrying every hunk (*** Begin Patch / *** Update File: <path> / @@ context / , -, + lines / *** End Patch). Keep the same path. Use save_canvas only when creating a canvas or replacing the whole source. One question, one slug.
  • Free builds may keep at most 3 canvases. Overwriting an existing slug is always allowed; a fourth new slug is rejected until the user upgrades to Pro.

4. Where data comes from

  • Small data (a handful of numbers, a short series): inline it in the TSX as before.
  • K-line, always through a tool — never hand-typed bars. Use snapshot_candles({ slug, name, symbol }) for a post-hoc read: the server builds a three-timeframe CandleFeed and writes journal/canvases/<slug>.<name>.json; import snap from './<name>.json' and pass it to CandleChart as source={snap} tf="m5". Use useCandles(symbol) instead only when the user explicitly wants to watch the market live — it returns a live-updating CandleFeed. Either way CandleChart never takes hand-typed bars for real market data.
  • Any other data too big or awkward to inline: save_canvas_data({ slug, name, json }) writes journal/canvases/<slug>.<name>.json, then import x from './<name>.json' in the source. Both data tools require the canvas (the slug) to already exist — save_canvas first if it does not.
  • Live quotes: useQuote(symbol) returns a live-updating QuoteCell | null, e.g. for Stat. Its fields are last、pct、regularLast、regularPct、session、turnover?、 asOf?;QuoteCell / CandleFeed / CandleFeedTf / TimeframeKey 的完整定义在 $KANSOKU_APP_SKILLS_DIR/canvas/sdk/shared.d.ts。
  • Caption discipline: a canvas driven by useQuote / useCandles writes Source as 「实时」; one driven by a snapshot file writes the data's cutoff time, taken from CandleFeed.asOf.
  • The only permitted empty state is <CandleChart source={null} .../> rendering its built-in「等待行情…」placeholder while a live feed has not delivered a first frame. Every other empty state is still forbidden (see below).
  • Live subscription budget: at most 6 combined useQuote / useCandles calls per canvas.

Never render empty states. No data means omit the element — no placeholder text, no 「暂无数据」, no zeroed rows, no empty chart frame. Coverage is the sole exception; naming gaps is its job. If the whole canvas would be empty, say what is missing instead.

Label every plot. Charts get screenshotted alone. Each needs a title naming the specific measure (08-28 相对各自开盘价(都从 0 起), not 走势图), yUnit only when the axis carries a real dimension (% / USD / 亿) — a category axis of tickers takes no xUnit — series names when multi-series, and any transformation stated (归一化 / 累计 / 相对开盘). A missing title renders as Untitled — never ship that. A Section holding one chart gets no title of its own: the chart title is the heading.

Components. The table below is the complete allow-list; referencing an export that does not exist — or inventing a prop — is the most common failure, and an unknown prop is silently dropped rather than erroring. Exact prop shapes are declared next to this file in $KANSOKU_APP_SKILLS_DIR/canvas/sdk/. Use bash cat to read them instead of guessing, starting with core.d.ts — it holds everything the mandatory parts of the skeleton use (layout, text, Stat, Table, Compare, Coverage, Source). Read the rest only when you reach for them: charts.d.ts, CandleChart.d.ts, analysis.d.ts (Scenarios / RRPlan / Timeline), control.d.ts, theme.d.ts, live.d.ts (useQuote / useCandles), shared.d.ts (QuoteCell / CandleFeed 等行情数据结构).

GroupComponents
LayoutCanvas (root), Section, Grid, Row, Stack, Card, Divider
TextH1 H2 H3, Heading, Text, Link, Callout, Pill, Badge, Source
NumbersStat, Metric, Table, Compare, Coverage
ConclusionsScenarios, RRPlan, Timeline
ControlsToggle, Select, Param
ChartsLineChart, BarChart (signed), AreaChart, PieChart, Sparkline, CandleChart (bars or source+tf)
LiveuseQuote(symbol), useCandles(symbol) — hooks, not components; feed Stat / CandleChart source

Four of them validate themselves against the discipline rules: Scenarios flags probabilities that miss 100 (TD-SCENARIO-01), RRPlan reddens reward-to-risk under 1.5 (TD-RR-01), Coverage carries TD-DATA-01, Source carries TD-DATA-02.

Interactivity is useState / useMemo plus Toggle / Select / Param. There is no useEffect.

Param only rewrites numbers already in the canvas. Do not use it to switch symbols, switch timeframes, or trigger a new fetch. Give both min and max or neither — one side alone is rejected at save. Native <input> / <textarea> are rejected; use Param / Toggle / Select.

Design guidance

Flat, dense, square. No gradients, no emojis, no shadows, no corner radius beyond 2px. A canvas that looks like a generic dashboard is a failed canvas.

Structure — five parts, fixed order

Skip a part with no content. Never reorder. Never push the conclusion to the bottom.

PartComponentsRule
1 ConclusionCalloutOne paragraph answering the question asked. Answer first.
2 Key numbersGrid + Stat≤ 4. The ones part 1 depends on.
3 EvidenceCompare / Table / chartsEverything traces back to part 1.
4 Forward viewScenarios / RRPlanOnly with a real directional call.
5 BoundariesCoverage + SourceWhat is missing, and when the data is from.

Parts 1 and 5 are mandatory. All data and no conclusion is not acceptable.

Hierarchy and color

The conclusion and the number driving it get space; detail stays compact. Squint test: blur your eyes — can you tell what this canvas concluded?

tone encodes price direction only (up / down / neutral), never good-versus-bad — 「亏损收窄」is good news with a down direction, and up makes it read backwards. Directionless numbers (成交额, 市值, 天数) take no tone. Callout tone="warn" means "hold off", at most one per canvas.

Hard limits

Grid columns ≤ 4 · Stat ≤ 4 per screen · charts ≤ 6 per canvas · Text paragraph ≤ 3 lines · no Section for fewer than 2 elements.

Say X → use Y

Nothing on the left may be hand-rolled with Table.

To showUseNot
Symbols across the same metricsCompareTable / a row of Pill
Cases with probabilities and triggersScenariosTable / several Callout
Entry / stop / target and reward-to-riskRRPlanTable / three Stat
Events in time orderTimelineTable / a run of Text
Which data exists and which does notCoverageTable
A tiny inline trendSparklineLineChart
One number with its changeStat (delta = the number only; words like「30 天最强」go in note)a number inside Text
A number the user should change so other numbers updateParama native input, a slider plus a separate field, Text
Genuine multi-row detailTable—

Slop patterns — forbidden

Two or more of these means redesign.

  • All data, no conclusion — the most common failure.
  • Hand-rolled tables — anything from the mapping table rebuilt as Table.
  • Intent attribution — 「主力在出货」「有人故意砸盘」. Unfalsifiable (TD-INTENT-01); cite price, volume, structure.
  • Narrating noise — giving a ±2% day a cause (TD-NOISE-01).
  • Unlabeled numbers — no unit, no time basis.
  • Emojis as icons, status markers, or bullets.
  • Rainbow coloring — most elements are neutral; color is scarce and means something.
  • Wall of identical cards — mix open sections with cards.
  • Giant text — nothing above H1, never H1 stacked on H1.

Self-check before saving

  1. Conclusion visible on the first screen?
  2. Every number carries a unit and a time basis?
  3. Nothing from the mapping table hand-rolled with Table?
  4. Coverage or Source states the data boundary?
  5. Slop list scanned?
  6. Squint test: does one thing stand out?

Skeleton

The five-part shape as a real, typechecked file: apps/web/src/features/canvas/demo/skeleton.canvas.tsx. Read it and adapt it — do not invent another structure. Every component at once: apps/web/src/features/canvas/demo/kitchenSink.canvas.tsx, viewable at /canvases/demo.

Handing it over

Tell the user the slug so they can open it beside the chat. First canvas of the conversation: one sentence on what a canvas is. Canvas they did not ask for: one sentence on why it beat plain text. Later ones: just the slug.

Troubleshooting

rejected: lists one line per reason — fix those, do not work around them. Save also rejects unknown @kansoku/canvas exports, unknown JSX tags, invented props, and source that fails to compile (invalid TSX or leftover imports). save_canvas refuses outright until this skill has been read once in this conversation.

Compile and runtime errors are written into journal/canvases/.meta.json; inspect that file with bash cat -- journal/canvases/.meta.json when the diagnostic record is needed.

A blank canvas almost always referenced an export that does not exist. A prop that has no effect was invented — use bash cat to check it against the declarations in $KANSOKU_APP_SKILLS_DIR/canvas/sdk/.

missing data file: <slug>.<name>.json means the source imports a data file that has not been written yet — call save_canvas_data or snapshot_candles with that name first, then save_canvas / apply_patch.

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 Canvas AI skill do?

A canvas is a live React panel the user opens beside the chat, saved as journal/canvases/<slug>.canvas.tsx. You MUST use a canvas whenever you produce a standalone analytical artifact — multi-symbol comparisons, capital-flow or price reads across several names, event before/after studies, scenario-and-plan write-ups, session or weekly post-mortems, multi-period chart layouts, coverage-and-gap reports, or any answer that is carried by numbers laid out visually. If you catch yourself about to write a markdown table of market data, stop and build a canvas instead. You MUST also read this skill...

Why use Canvas on TypingMind?

Because you install it once and use it with any model. 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 Canvas in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kansoku-trade/kansoku/tree/main/packages/core/skills/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 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 Canvas?

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

Is the Canvas AI skill free?

It is published on GitHub by kansoku-trade. Check the repository for licensing terms. 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.

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