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/model-promptsgit 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/model-prompts)<a href="https://agentmods.dev/instructions/microsoft/vscode/model-prompts"><img src="https://agentmods.dev/badge/instructions/microsoft/vscode/model-prompts.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 5.1 | $0.01987 | $0.01987 |
| Opus 5 | $0.00993 | $0.00993 |
| Sonnet 5 | $0.00397 | $0.00397 |
| Haiku 4.5 | $0.00199 | $0.00199 |
Grade A, and why
vscode model-prompts.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 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guidelines for creating, registering, and testing model-specific prompts via the PromptRegistry system.
Prompt Registry
The PromptRegistry in promptRegistry.ts maps AI models to their optimal prompt structures. Each model provider has a resolver class implementing IAgentPrompt that returns prompt customizations for that provider's models.
Resolution Order
When PromptRegistry.resolveAllCustomizations() is called:
- Phase 1 —
matchesModel(): Iterates registered resolvers in order, callingmatchesModel(). Firsttruewins. - Phase 2 —
familyPrefixes: If nomatchesModelmatched, checksendpoint.family.startsWith(prefix). First match wins. - Phase 3 — provider fallback: If neither phase matched, checks predicates registered through
registerFallbackPrompt(resolver, matchesModel). First match wins. - Defaults: If no resolver matches, defaults are used for all customizations.
Registration order matters within each phase. Register broad provider defaults through registerFallbackPrompt, not matchesModel or familyPrefixes, so they never shadow a model-specific resolver or a family-prefix registration.
OpenAI Default
Unrecognized GPT families and endpoints identified as OpenAI use the latest approved GPT prompt bundle. The single promotion point is the LatestOpenAIPromptResolver import in openai/latestOpenAIPrompt.ts, currently GPT-5.6. It reuses the entire resolver, including reminders, identity, safety, tool-reference hints, and user-query tag defaults, without copying prompt text.
- Do not add a prompt file just because a new OpenAI model ships. Add a dedicated resolver only when the model needs different instructions.
- Existing specialized GPT/Codex resolvers and the explicitly retained legacy families in
defaultOpenAIPrompt.tsxtake precedence. Version matchers must distinguishgpt-5.1and its hyphenated variants fromgpt-5.10. - OpenAI provider metadata can identify opaque preview names; an OpenAI-compatible API alone does not establish OpenAI model identity. Explicit family routing takes precedence over the provider fallback.
- Prompt inheritance must not alias the endpoint family or enable GPT-5.6-specific API/tool capabilities. Rendering still respects the actual endpoint's capabilities and available tools.
- To promote a new default, update the resolver import in
latestOpenAIPrompt.tsand the expected default intest/openAIPrompts.spec.ts. Run its routing/rendering tests and the existing agent prompt snapshots. Keep older specialized resolvers unchanged.
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 · 205 lines · 1,987 tokens per session scan A 5fad036783cc
vscode model-prompts.instructions.md is an instructions file published in the GitHub repository microsoft/vscode (190,919 stars, last pushed today), licensed MIT. It adds 1,987 tokens to every session, about $0.0099 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
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).
compact_prompt CLAUDE.md
Claude Code instructions for gtkcyber/compact_prompt, covering claude.md, commands, install for development, runnable tour of every strategy and interactive demo (prompt + files & skills tabs).