Borrowing it
Nothing to install: this file belongs to butttons/dora. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/butttons/dora/main/.pi/skills/toon/SKILL.mdgit clone --depth 1 https://github.com/butttons/doraWrote 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.
[](https://agentmods.dev/skills/butttons/dora/toon)<a href="https://agentmods.dev/skills/butttons/dora/toon"><img src="https://agentmods.dev/badge/skills/butttons/dora/toon/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.
<a href="https://agentmods.dev/skills/butttons/dora/toon"><img src="https://agentmods.dev/badge/skills/butttons/dora/toon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.11384 |
| Opus 5.5 | $0.00020 | $0.04554 |
| Sonnet 5.5 | $0.00010 | $0.02277 |
| Haiku 4.5 | $0.00005 | $0.01138 |
Grade A, and why
toon 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 1mo 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 — 933 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token-Oriented Object Notation (TOON)
Token-Oriented Object Notation is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for LLM input as a drop-in, lossless representation of your existing JSON.
TOON combines YAML's indentation-based structure for nested objects with a CSV-style tabular layout for uniform arrays. TOON's sweet spot is uniform arrays of objects (multiple fields per row, same structure across items), achieving CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably. For deeply nested or non-uniform data, JSON may be more efficient.
The similarity to CSV is intentional: CSV is simple and ubiquitous, and TOON aims to keep that familiarity while remaining a lossless, drop-in representation of JSON for Large Language Models.
Think of it as a translation layer: use JSON programmatically, and encode it as TOON for LLM input.
[!TIP] The TOON format is stable, but also an idea in progress. Nothing's set in stone – help shape where it goes by contributing to the spec or sharing feedback.
Table of Contents
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.
- 1mo ago First seen · 933 lines · 51 tokens per session scan A 42fc044f2d74
toon is a skill published in the GitHub repository butttons/dora (109 stars, last pushed 7mo ago), licensed MIT. It adds 51 tokens to every session and 11,384 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
mastermind-prompt-refiner
Rewrite a request for a different agent or reusable prompt while preserving the original intent verbatim. Use when the user explicitly asks to improve/rewrite a prompt, or immediately before handing a genuinely rough request to a cold agent that will not see the original conversation. Do not activate merely because…
prompt-library
Use when looking for a ready-made agent or task template (code review, support, data analysis, translation, extraction) instead of writing one from scratch.
prompt-creation
Use when writing a new system prompt from scratch, structuring it with Anthropic's 9-element format, or choosing emphasis levels and few-shot examples.
prompt-testing
Use when comparing two prompt variants, defining quality/efficiency/robustness metrics, or deciding whether to adopt a challenger prompt over a baseline.
prompt-optimization
Use when auditing an existing prompt for clarity, structure, or completeness issues, fixing vague instructions, or producing a before/after optimization report.
create-art-prompt
Fabric pattern: createartprompt.