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.
git clone --depth 1 https://github.com/KazunariNakayama/raycast-extensionsWrote 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/rules/kazunarinakayama/raycast-extensions/prompts)<a href="https://agentmods.dev/rules/kazunarinakayama/raycast-extensions/prompts"><img src="https://agentmods.dev/badge/rules/kazunarinakayama/raycast-extensions/prompts/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/rules/kazunarinakayama/raycast-extensions/prompts"><img src="https://agentmods.dev/badge/rules/kazunarinakayama/raycast-extensions/prompts.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 | $0.00429 | $0.00429 |
| Opus 5 | $0.00215 | $0.00215 |
| Sonnet 5 | $0.00086 | $0.00086 |
| Haiku 4.5 | $0.00043 | $0.00043 |
Grade A, and why
prompts 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 9d 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
AI Development Prompts
新しい拡張を追加する場合
apps/ 配下に新しい拡張 ext-xxx を作成してください。
以下を含めてください：
- package.json（manifest）
- tsconfig.json
- commands/, features/, services/, lib/ の構成
- README.md
- 最低1つのコマンド実装
新しいコマンドを追加する場合
ext-xxx に新しいコマンド yyy を追加してください。
フロー: [Form/List/Detail のいずれか]
機能: [具体的な機能説明]
API: [使用するAPI、なければモック]
共有コンポーネント/サービスを追加する場合
packages/ui に新しいヘルパー関数 zzz を追加してください。
用途: [Raycast標準コンポーネントのラッパー]
引数: [具体的な型定義]
リファクタリングする場合
ext-xxx の features/aaa を以下のように改善してください：
- [具体的な改善内容]
- テストも更新
- 破壊的変更がある場合は明示
デバッグ・修正する場合
ext-xxx で [具体的なエラー/問題] が発生しています。
以下を確認・修正してください：
- エラーログ: [エラーメッセージ]
- 期待する動作: [具体的な期待]
ベストプラクティス
- 一度に 1 つの明確なタスクを依頼する
- 具体的なファイルパス・関数名を指定する
- エラーメッセージは全文をコピーする
- 期待する動作を明示する
- テストの追加・更新も忘れずに依頼する
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.
- 9d ago First seen · 57 lines · 429 tokens per session scan A 4da6536be3e6
prompts is a cursor rule published in the GitHub repository KazunariNakayama/raycast-extensions (2 stars, last pushed 10mo ago), licensed MIT. It adds 429 tokens to every session, about $0.0021 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 cursor rules, from other repositories
baml
A set of rules for setting up BAML and help with syntax guidance.
co-dialectic
Co-Dialectic prompt sharpening and verification rules for Cursor.
prompt-routing
Route tasks to the correct Universal AI Engineering Prompt.
prompting-for-qe
Soạn/tinh chỉnh prompt cho tác vụ QE (sinh test case, phân tích requirement, tóm tắt tài liệu test, phân tích log) — đặc biệt khi output AI lan man, chung chung, bịa, hoặc muốn chốt prompt thành template tái dùng.
llm-zod-jsonschema
Best Practice for LLM Output Parsing with Zod and JSON Schema.
prompt-evals
Prompt eval fixtures — case design, assertions, versioning, CI gates, no PII in golden data.