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/quantstack/jupyter-ui-tweak/agents-mdgit clone --depth 1 https://github.com/QuantStack/jupyter-ui-tweakWhat 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 | $0.04334 | $0.04334 |
| Opus 5 | $0.02167 | $0.02167 |
| Sonnet 5 | $0.00867 | $0.00867 |
| Haiku 4.5 | $0.00433 | $0.00433 |
Grade A, and why
jupyter-ui-tweak 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 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 — 588 lines — stays where its author put it; the contents beside it link to each section on GitHub.
JupyterLab Extension Development
This guide provides coding standards and best practices for developing JupyterLab extensions. Follow these rules to align with community standards and keep your extension maintainable.
Extension type: frontend
External Documentation and Resources
PRIORITY RESOURCE USAGE
When you encounter uncertainty, incomplete information, or need implementation examples, you MUST consult these external resources FIRST before attempting to implement features.
Use your available tools (web search, documentation search) to access and retrieve content from these resources when:
- You're unsure about API usage, method signatures, or interface requirements
- You need to verify the correct approach for a feature or pattern
- You're looking for existing implementation examples or best practices
- You're debugging unexpected behavior and need official documentation
- You're implementing a feature that likely exists in core JupyterLab or other extensions
Required External Resources
These resources are PRIORITY references. Always check them when you need external information:
-
JupyterLab Extension Developer Guide
- URL: https://jupyterlab.readthedocs.io/en/stable/extension/extension_dev.html
- Use for: Extension patterns, architecture overview, development workflow, and best practices
- Action: Use web search or documentation tools to retrieve specific sections when needed
-
JupyterLab API Reference (Frontend)
- URL: https://jupyterlab.readthedocs.io/en/latest/api/index.html
- Use for: Complete API reference for all JupyterLab frontend packages, interfaces, classes, and methods
- Action: Search for specific APIs when you need method signatures, interface definitions, or class documentation. For example, search "JupyterLab IRenderMime.IRenderer" or "JupyterLab ICommandPalette"
-
JupyterLab Extension Examples Repository
- URL: https://github.com/jupyterlab/extension-examples
- Use for: Working code examples, implementation patterns, complete working extensions
- Action: Search this repository for similar features before implementing from scratch
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 · 588 lines · 4,334 tokens per session scan A 0ca6df7c00bf
jupyter-ui-tweak AGENTS.md is an instructions file published in the GitHub repository QuantStack/jupyter-ui-tweak (2 stars, last pushed 5mo ago), licensed BSD-3-Clause. It adds 4,334 tokens to every session, about $0.0217 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 2026-08-31.
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.
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.
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).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.