Visual Studio Code is a code editor that supports editing, navigating, understanding, debugging, and extending software projects. Developers use it for the edit-build-debug cycle, and the catalogue add-ons provide skills, instructions, and agents for working within the editor.
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/microsoft/vscode/prompt-tsxgit clone --depth 1 https://github.com/microsoft/vscodeWrote 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/microsoft/vscode/prompt-tsx)<a href="https://agentmods.dev/instructions/microsoft/vscode/prompt-tsx"><img src="https://agentmods.dev/badge/instructions/microsoft/vscode/prompt-tsx.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 | $0.01008 | $0.01008 |
| Opus 5 | $0.00504 | $0.00504 |
| Sonnet 5 | $0.00202 | $0.00202 |
| Haiku 4.5 | $0.00101 | $0.00101 |
Grade A, and why
vscode prompt-tsx.instructions.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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- vscode-copilot-chat prompt-tsx.instructions.md — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guidelines for TSX files using prompt-tsx focusing on specific patterns and token budget management for AI prompt engineering.
Component Structure
Base Pattern
- Extend
PromptElement<Props>orPromptElement<Props, State>for all prompt components - Props interfaces must extend
BasePromptElementProps
interface MyPromptProps extends BasePromptElementProps {
readonly userQuery: string;
}
class MyPrompt extends PromptElement<MyPromptProps> {
render() {
return (
<>
<SystemMessage priority={1000}>...</SystemMessage>
<UserMessage priority={900}>{this.props.userQuery}</UserMessage>
</>
);
}
}
Async Components
- The
rendermethod can be async for components that need to perform async operations - All async work should be done directly in the
rendermethod
class FileContextPrompt extends PromptElement<FileContextProps> {
async render() {
const fileContent = await readFileAsync(this.props.filePath);
return (
<>
<SystemMessage priority={1000}>File content:</SystemMessage>
<UserMessage priority={900}>{fileContent}</UserMessage>
</>
);
}
}
Prompt-Specific JSX
Line Breaks
- CRITICAL: Use
<br />for line breaks - newlines are NOT preserved in JSX - Never rely on whitespace or string literal newlines
// ✅ Correct
<SystemMessage>
You are an AI assistant.<br />
Follow these guidelines.<br />
</SystemMessage>
// ❌ Wrong - newlines will be collapsed
<SystemMessage>
You are an AI assistant.
Follow these guidelines.
</SystemMessage>
Priority System
Priority Values
- Higher numbers = higher priority (like z-index)
- Use consistent ranges:
- System messages: 1000
- User queries: 900
- Recent history: 700-800
- Context/attachments: 600-700
- Background info: 0-500
<SystemMessage priority={1000}>...</SystemMessage>
<UserMessage priority={900}>{query}</UserMessage>
<HistoryMessages priority={700} />
<ContextualData priority={500} />
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.
- yesterday First seen · 168 lines · 1,008 tokens per session scan A 9801de07d128
vscode prompt-tsx.instructions.md is an instructions file published in the GitHub repository microsoft/vscode (190,863 stars, last pushed today), licensed MIT. It adds 1,008 tokens to every session, about $0.0050 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 2026-09-03.
Other instructions, from other repositories
apex-accelerator vendor-prompting.instructions.md
Vendor prompting best-practice rules for Anthropic Claude and OpenAI GPT-5.6-Terra agents and prompts. Each rule cites a rule ID in the vendor-prompting skill rules.json registry. Validator: npm run lint:vendor-prompting.
foundry-local cpp-download.instructions.md
Use when working on DownloadManager, model caching, model downloads, IsModelCached, or debugging why a model fails to download or loads incorrectly.
Awesome-Prompt-Engineering AGENTS.md
Instructions for natnew/Awesome-Prompt-Engineering, covering agents.md, repository north star, agent role, trust boundary and read order.
SkillOpt AGENTS.md
Instructions for mitkox/SkillOpt, covering agent instructions for skillopt, project identity, default example workflow, documentation expectations and repo hygiene.
ZipAI CLAUDE.md
Claude Code instructions for nickdesi/ZipAI, covering claude.md — zipai: ultra-dense token optimizer, rules, 1. zero filler, 2. ambiguity and 3. prompt caching.
prompt-engineering-skill GEMINI.md
Instructions for PhAlves23/prompt-engineering-skill, covering prompt engineering — gemini context, prompt engineering, operating principle, workflow and canonical structure (omit sections that don't add value).