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 instructions/niall-young/aitofigma/agents-mdgit clone --depth 1 https://github.com/Niall-Young/AItoFigmaWrote 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/instructions/niall-young/aitofigma/agents-md)<a href="https://agentmods.dev/instructions/niall-young/aitofigma/agents-md"><img src="https://agentmods.dev/badge/instructions/niall-young/aitofigma/agents-md.svg" alt="Measured on agentmods" 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 | $0.00372 | $0.00372 |
| Opus 5 | $0.00186 | $0.00186 |
| Sonnet 5 | $0.00074 | $0.00074 |
| Haiku 4.5 | $0.00037 | $0.00037 |
Grade A, and why
AItoFigma AGENTS.md 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.
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.
What it actually says
Repository instructions
Mandatory workflow for code changes
- Every task that creates, modifies, renames, or deletes code must invoke and complete the
good-readmeSkill workflow. - Every task that creates, modifies, renames, or deletes code must invoke and complete the
gitworkSkill workflow. - These two Skill workflows are mandatory for code changes even when the user does not mention them explicitly. They are not required for read-only, research-only, or documentation-only tasks unless the user explicitly requests them. Follow each Skill's own applicability, safety, verification, and reporting requirements.
Content boundaries
- Keep repository-level documentation, licensing, and contributor instructions at the project root.
- Keep the complete distributable Skill under
skills/ai-to-figma/. ItsSKILL.md,agents/,assets/,references/, runtime scripts, and runtime requirements must stay inside that directory. - Treat
skills/ai-to-figma/as the Skill source of truth. Do not place Skill runtime files at the repository root. - Keep smoke tests, end-to-end captures, temporary fixtures, generated previews, and validation artifacts under
.ai-to-figma/or another ignored temporary directory. Do not commit them as Skill or project content. - Do not add a root package manifest solely to run temporary validation.
Change requirements
- Preserve relative links inside the Skill when moving or renaming its resources.
- Keep the Chinese and English sections of
README.mdsemantically aligned. UpdateREADME.zh-CN.mdwhen the Chinese project instructions change. - Validate the packaged Skill with the system
skill-creatorvalidator againstskills/ai-to-figma/. - Run any functional smoke checks from ignored local workspaces and report them separately from committed project files.
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.
- 5d ago First seen · 23 lines · 372 tokens per session scan A b37bd9f1bdb8
AItoFigma AGENTS.md is an instructions file published in the GitHub repository Niall-Young/AItoFigma (50 stars, last pushed 14d ago), licensed MIT. It adds 372 tokens to every session, about $0.0019 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.