Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
日本語の概要は準備中です。原文の説明を表示しています。
Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Structural code exploration using AST parsing. This skill overrides your default exploration behavior. While this skill is active, use smart_search/smart_outline/smart_unfold as your primary tools instead of Read, Grep, and Glob.
Core principle: Index first, fetch on demand. Give yourself a map of the code before loading implementation details. The question before every file read should be: "do I need to see all of this, or can I get a structural overview first?" The answer is almost always: get the map.
This skill only loads instructions. You must call the MCP tools yourself. Your next action should be one of:
smart_search(query="<topic>", path="./src") -- discover files + symbols across a directory
smart_outline(file_path="<file>") -- structural skeleton of one file
smart_unfold(file_path="<file>", symbol_name="<name>") -- full source of one symbol
Do NOT run Grep, Glob, Read, or find to discover files first. smart_search walks directories, parses all code files, and returns ranked symbols in one call. It replaces the Glob → Grep → Read discovery cycle.
smart_search(query="shutdown", path="./src", max_results=15)
Returns: Ranked symbols with signatures, line numbers, match reasons, plus folded file views (~2-6k tokens)
-- Matching Symbols --
function performGracefulShutdown (services/infrastructure/GracefulShutdown.ts:56)
function httpShutdown (services/infrastructure/HealthMonitor.ts:92)
method WorkerService.shutdown (services/worker-service.ts:846)
-- Folded File Views --
services/infrastructure/GracefulShutdown.ts (7 symbols)
services/worker-service.ts (12 symbols)
This is your discovery tool. It finds relevant files AND shows their structure. No Glob/find pre-scan needed.
Parameters:
query (string, required) -- What to search for (function name, concept, class name)path (string) -- Root directory to search (defaults to cwd)max_results (number) -- Max matching symbols, default 20, max 50file_pattern (string, optional) -- Filter to specific files/pathssmart_outline(file_path="services/worker-service.ts")
Returns: Complete structural skeleton -- all functions, classes, methods, properties, imports (~1-2k tokens per file)
Skip this step when Step 1's folded file views already provide enough structure. Most useful for files not covered by the search results.
Parameters:
file_path (string, required) -- Path to the fileReview symbols from Steps 1-2. Pick the ones you need. Unfold only those:
smart_unfold(file_path="services/worker-service.ts", symbol_name="shutdown")
Returns: Full source code of the specified symbol including JSDoc, decorators, and complete implementation (~400-2,100 tokens depending on symbol size). AST node boundaries guarantee completeness regardless of symbol size — unlike Read + agent summarization, which may truncate long methods.
Parameters:
file_path (string, required) -- Path to the file (as returned by search/outline)symbol_name (string, required) -- Name of the function/class/method to expandUse these only when smart_* tools are the wrong fit:
ensureWorkerStarted defined?")For code files over ~100 lines, prefer smart_outline + smart_unfold over Read.
Discover how a feature works (cross-cutting):
1. smart_search(query="shutdown", path="./src")
-> 14 symbols across 7 files, full picture in one call
2. smart_unfold(file_path="services/infrastructure/GracefulShutdown.ts", symbol_name="performGracefulShutdown")
-> See the core implementation
Navigate a large file:
1. smart_outline(file_path="services/worker-service.ts")
-> 1,466 tokens: 12 functions, WorkerService class with 24 members
2. smart_unfold(file_path="services/worker-service.ts", symbol_name="startSessionProcessor")
-> 1,610 tokens: the specific method you need
Total: ~3,076 tokens vs ~12,000 to Read the full file
Write documentation about code (hybrid workflow):
1. smart_search(query="feature name", path="./src") -- discover all relevant files and symbols
2. smart_outline on key files -- understand structure
3. smart_unfold on important functions -- get implementation details
4. Read on small config/markdown/plan files -- get non-code context
Use smart_* tools for code exploration, Read for non-code files. Mix freely.
Exploration then precision:
1. smart_search(query="session", path="./src", max_results=10)
-> 10 ranked symbols: SessionMetadata, SessionQueueProcessor, SessionSummary...
2. Pick the relevant one, unfold it
| Approach | Tokens | Use Case |
|---|---|---|
| smart_outline | ~1,000-2,000 | "What's in this file?" |
| smart_unfold | ~400-2,100 | "Show me this function" |
| smart_search | ~2,000-6,000 | "Find all X across the codebase" |
| search + unfold | ~3,000-8,000 | End-to-end: find and read (the primary workflow) |
| Read (full file) | ~12,000+ | When you truly need everything |
| Explore agent | ~39,000-59,000 | Cross-file synthesis with narrative |
4-8x savings on file understanding (outline + unfold vs Read). 11-18x savings on codebase exploration vs Explore agent. The narrower the query, the wider the gap — a 27-line function costs 55x less to read via unfold than via an Explore agent, because the agent still reads the entire file.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
日本語の概要は準備中です。原文の説明を表示しています。
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
日本語の概要は準備中です。原文の説明を表示しています。
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。