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.
npx agentmods add skills/gcwing/bitfun/bitfun-canvasnpx skills add GCWing/BitFun --skill bitfun-canvasgit clone --depth 1 https://github.com/GCWing/BitFunWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00151 | $0.01884 |
| Opus 5 | $0.00076 | $0.00942 |
| Sonnet 5 | $0.00030 | $0.00377 |
| Haiku 4.5 | $0.00015 | $0.00188 |
Grade A, and why
bitfun-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 2d ago.
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.
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 BitFun 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. BitFun 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.
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- sdk/appearance.d.ts 1.7 KB runs code
- sdk/callout-tone-icons.d.ts 581 B runs code
- sdk/canvas-tokens.d.ts 3.6 KB runs code
- sdk/chart-primitives.d.ts 10 KB runs code
- sdk/collapsible-section.d.ts 2.4 KB runs code
- sdk/dag-layout.d.ts 3.3 KB runs code
- sdk/diff-view.d.ts 4.8 KB runs code
- sdk/form-primitives.d.ts 6.5 KB runs code
- sdk/hooks.d.ts 5.6 KB runs code
- sdk/index.d.ts 3.7 KB runs code
- sdk/swatch.d.ts 1.3 KB runs code
- sdk/todo-list.d.ts 1.5 KB runs code
- sdk/ui-primitives.d.ts 19 KB runs code
- sdk/ui-primitives.test.d.ts 59 B runs code
- sdk/usage-bar.d.ts 2.3 KB runs code
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.
- 2d ago First seen · 109 lines · 151 tokens per session scan A d498d7480ab5
bitfun-canvas is a skill published in the GitHub repository GCWing/BitFun (1,871 stars, last pushed 2d ago), licensed MIT. It adds 151 tokens to every session and 1,884 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 2026-08-30.
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