openbitfun-canvas

openbitfun-canvas is a skill for Claude Code, Codex from GCWing/BitFun. It costs 154 tokens per session (1,921 once invoked), scanned A, original, MIT.

A workflow for creating a standalone interactive React artifact beside the chat. React is a library for building web interfaces; the artifact can present analyses, charts, tables, timelines, and other structured results.

In plain words
What is it for?
It is for displaying quantitative analyses, investigations, audits, architecture reviews, reports, and interactive explorations.
Why use it?
It gives data-heavy or analytical work a separate view instead of burying it in a chat reply.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for displaying quantitative analyses, investigations, audits, architecture reviews, reports, and interactive explorations.

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Install with agentmods
npx agentmods add skills/gcwing/bitfun/openbitfun-canvas
About the project

BitFun is a desktop AI agent that turns tasks into interactive mini-apps while also writing code, creating documents, and controlling computers. It is for software development and general desktop work across Windows, macOS, and Linux. Its catalogue entries extend the agent with skills, agents, and instructions.

GCWing/BitFun · 2,077 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add GCWing/BitFun --skill openbitfun-canvas
Clone the repo
git clone --depth 1 https://github.com/GCWing/BitFun

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for openbitfun-canvas

README.md
[![agentmods](https://agentmods.dev/badge/skills/gcwing/bitfun/openbitfun-canvas/github.svg)](https://agentmods.dev/skills/gcwing/bitfun/openbitfun-canvas)
Your own site
<a href="https://agentmods.dev/skills/gcwing/bitfun/openbitfun-canvas"><img src="https://agentmods.dev/badge/skills/gcwing/bitfun/openbitfun-canvas/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for openbitfun-canvas

Your own site · 80×15
<a href="https://agentmods.dev/skills/gcwing/bitfun/openbitfun-canvas"><img src="https://agentmods.dev/badge/skills/gcwing/bitfun/openbitfun-canvas.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 154 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,921 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00154 $0.01921
Opus 5 $0.00077 $0.00960
Sonnet 5 $0.00031 $0.00384
Haiku 4.5 $0.00015 $0.00192

Measured 5d ago against content hash 6ab53a123f30, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

openbitfun-canvas scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

The scan reads SKILL.md. This mod also ships 16 executable files (sdk/appearance.d.ts, sdk/callout-tone-icons.d.ts, sdk/canvas-tokens.d.ts, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

  • canvas — 86% identical, 55 lines differ
  • canvas — 84% identical, 44 lines differ
  • canvas — 83% identical, 91 lines differ
src/crates/assembly/core/builtin_skills/openbitfun-canvas/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

A canvas is a single TSX source artifact that OpenBitFun compiles so the user can open it beside the chat. Follow the workflow below in order.

Workflow

1. Decide whether to use a canvas

The trigger is user intent, not response shape. Ask: would the user benefit from viewing this output as its own standalone artifact, separate from the chat? If the output is a means to an end (a drafted message, a code fix, a dashboard in another tool), skip the canvas.

Use a canvas when the agent produces new standalone analytical output:

  • Quantitative analyses and metrics breakdowns (e.g. "send 500 requests and tell me how many fail")
  • Billing or account investigations that surface structured findings from database queries
  • Security audits or architecture reviews with categorized findings
  • Cross-system data analyses and overlap reports
  • Structured data from tools or services where the data IS the deliverable
  • Financial analyses, margin decompositions, usage trend reports
  • Tables with more than a handful of rows that the user asked to see

Do NOT use a canvas when:

  • The user asks for work in a specific tool — "create a Datadog dashboard" means give them a Datadog dashboard, not a canvas
  • The user has a specific deliverable — "draft a support response", "fix this code", "make this PR"
  • The user is working within an existing artifact — improving an HTML dashboard, editing an existing file
  • The user is doing targeted debugging or active development, even if structured findings emerge along the way
  • Short factual answers, one-off file edits, or quick clarifying questions
  • Tools are queried as an intermediate step for a different deliverable (e.g. querying Stripe to draft a support reply)

2. Write the canvas

Location. OpenBitFun Canvases are session-scoped artifacts created with CreateCanvas. For a new canvas, always call CreateCanvas with a concise title and complete TSX source; do not stop after telling the user what the source would be or showing code in chat. For small targeted revisions, use PatchCanvas with exact unique text replacements. Use ReadCanvas first when you do not have the latest source in context. Use UpdateCanvas only for large rewrites that need a complete replacement TSX source.

Read the full file on GitHub · 109 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 109 lines · 154 tokens per session scan A 6ab53a123f30

Subscribe to this mod's changes

openbitfun-canvas is a skill published in the GitHub repository GCWing/BitFun (2,077 stars, last pushed 5d ago), licensed MIT. It adds 154 tokens to every session and 1,921 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-06.

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