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 generate, create, or improve documentation for code, APIs, libraries, repositories, or software projects. Supports README generation, API reference documentation, inline code comments, architecture documentation, changelog generation, and developer guides. Trigger on requests like "document this code", "create a README", "generate API docs", "write developer guide", or when analyzing codebases for documentation purposes.
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。
This skill generates professional, comprehensive documentation for software projects, codebases, libraries, and APIs. It follows industry best practices from projects like React, Django, Stripe, and Kubernetes to produce documentation that is accurate, well-structured, and useful for both new contributors and experienced developers.
The output ranges from single-file READMEs to multi-document developer guides, always matched to the project's complexity and the user's needs.
Always load this skill when:
Before writing any documentation, thoroughly understand the codebase.
Identify the project fundamentals:
| Field | How to Determine |
|---|---|
| Language(s) | Check file extensions, package.json, pyproject.toml, go.mod, Cargo.toml, etc. |
| Framework | Look at dependencies for known frameworks (React, Django, Express, Spring, etc.) |
| Build System | Check for Makefile, CMakeLists.txt, webpack.config.js, build.gradle, etc. |
| Package Manager | npm/yarn/pnpm, pip/uv/poetry, cargo, go modules, etc. |
| Project Structure | Map out the directory tree to understand the architecture |
| Entry Points | Find main files, CLI entry points, exported modules |
| Existing Docs | Check for existing README, docs/, wiki, or inline documentation |
Use sandbox tools to explore the codebase:
# Get directory structure
ls /mnt/user-data/uploads/project-dir/
# Read key files
read_file /mnt/user-data/uploads/project-dir/package.json
read_file /mnt/user-data/uploads/project-dir/pyproject.toml
# Search for public API surfaces
grep -r "export " /mnt/user-data/uploads/project-dir/src/
grep -r "def " /mnt/user-data/uploads/project-dir/src/ --include="*.py"
grep -r "func " /mnt/user-data/uploads/project-dir/ --include="*.go"
Based on analysis, determine what documentation to produce:
| Project Size | Recommended Documentation |
|---|---|
| Single file / script | Inline comments + usage header |
| Small library | README with API reference |
| Medium project | README + API docs + examples |
| Large project | README + Architecture + API + Contributing + Changelog |
Every project needs a README. Follow this structure:
# Project Name
[One-line project description — what it does and why it matters]
[](#) [](#)
## Features
- [Key feature 1 — brief description]
- [Key feature 2 — brief description]
- [Key feature 3 — brief description]
## Quick Start
### Prerequisites
- [Prerequisite 1 with version requirement]
- [Prerequisite 2 with version requirement]
### Installation
[Installation commands with copy-paste-ready code blocks]
### Basic Usage
[Minimal working example that demonstrates core functionality]
## Documentation
- [Link to full API reference if separate]
- [Link to architecture docs if separate]
- [Link to examples directory if applicable]
## API Reference
[Inline API reference for smaller projects OR link to generated docs]
## Configuration
[Environment variables, config files, or runtime options]
## Examples
[2-3 practical examples covering common use cases]
## Development
### Setup
[How to set up a development environment]
### Testing
[How to run tests]
### Building
[How to build the project]
## Contributing
[Contribution guidelines or link to CONTRIBUTING.md]
## License
[License information]
For each public API surface, document:
Function / Method Documentation:
### `functionName(param1, param2, options?)`
Brief description of what this function does.
**Parameters:**
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `param1` | `string` | Yes | — | Description of param1 |
| `param2` | `number` | Yes | — | Description of param2 |
| `options` | `Object` | No | `{}` | Configuration options |
| `options.timeout` | `number` | No | `5000` | Timeout in milliseconds |
**Returns:** `Promise<Result>` — Description of return value
**Throws:**
- `ValidationError` — When param1 is empty
- `TimeoutError` — When the operation exceeds the timeout
**Example:**
\`\`\`javascript
const result = await functionName("hello", 42, { timeout: 10000 });
console.log(result.data);
\`\`\`
Class Documentation:
### `ClassName`
Brief description of the class and its purpose.
**Constructor:**
\`\`\`javascript
new ClassName(config)
\`\`\`
| Parameter | Type | Description |
|-----------|------|-------------|
| `config.option1` | `string` | Description |
| `config.option2` | `boolean` | Description |
**Methods:**
- [`method1()`](#method1) — Brief description
- [`method2(param)`](#method2) — Brief description
**Properties:**
| Property | Type | Description |
|----------|------|-------------|
| `property1` | `string` | Description |
| `property2` | `number` | Read-only. Description |
For medium-to-large projects, include architecture documentation:
# Architecture Overview
## System Diagram
[Include a Mermaid diagram showing the high-level architecture]
\`\`\`mermaid
graph TD
A[Client] --> B[API Gateway]
B --> C[Service A]
B --> D[Service B]
C --> E[(Database)]
D --> E
\`\`\`
## Component Overview
### Component Name
- **Purpose**: What this component does
- **Location**: `src/components/name/`
- **Dependencies**: What it depends on
- **Public API**: Key exports or interfaces
## Data Flow
[Describe how data flows through the system for key operations]
## Design Decisions
### Decision Title
- **Context**: What situation led to this decision
- **Decision**: What was decided
- **Rationale**: Why this approach was chosen
- **Trade-offs**: What was sacrificed
Generate language-appropriate inline documentation:
Python (Docstrings — Google style):
def process_data(input_path: str, options: dict | None = None) -> ProcessResult:
"""Process data from the given file path.
Reads the input file, applies transformations based on the provided
options, and returns a structured result object.
Args:
input_path: Absolute path to the input data file.
Supports CSV, JSON, and Parquet formats.
options: Optional configuration dictionary.
- "validate" (bool): Enable input validation. Defaults to True.
- "format" (str): Output format ("json" or "csv"). Defaults to "json".
Returns:
A ProcessResult containing the transformed data and metadata.
Raises:
FileNotFoundError: If input_path does not exist.
ValidationError: If validation is enabled and data is malformed.
Example:
>>> result = process_data("/data/input.csv", {"validate": True})
>>> print(result.row_count)
1500
"""
TypeScript (JSDoc / TSDoc):
/**
* Fetches user data from the API and transforms it for display.
*
* @param userId - The unique identifier of the user
* @param options - Configuration options for the fetch operation
* @param options.includeProfile - Whether to include the full profile. Defaults to `false`.
* @param options.cache - Cache duration in seconds. Set to `0` to disable.
* @returns The transformed user data ready for rendering
* @throws {NotFoundError} When the user ID does not exist
* @throws {NetworkError} When the API is unreachable
*
* @example
* ```ts
* const user = await fetchUser("usr_123", { includeProfile: true });
* console.log(user.displayName);
* ```
*/
Go (GoDoc):
// ProcessData reads the input file at the given path, applies the specified
// transformations, and returns the processed result.
//
// The input path must be an absolute path to a CSV or JSON file.
// If options is nil, default options are used.
//
// ProcessData returns an error if the file does not exist or cannot be parsed.
func ProcessData(inputPath string, options *ProcessOptions) (*Result, error) {
Verify the documentation covers:
| Standard | Check |
|---|---|
| Accuracy | Every code example must actually work with the described API |
| Completeness | No public API surface left undocumented |
| Consistency | Same formatting and structure throughout |
| Freshness | Documentation matches the current code, not an older version |
| Accessibility | No jargon without explanation, acronyms defined on first use |
| Examples | Every complex concept has at least one practical example |
Ensure:
#, ##, ###)```python, ```bash)code formatting for function names, file paths, variable names, and CLI commands| Language | Doc Format | Style Guide |
|---|---|---|
| Python | Google-style docstrings | PEP 257 |
| TypeScript/JavaScript | TSDoc / JSDoc | TypeDoc conventions |
| Go | GoDoc comments | Effective Go |
| Rust | Rustdoc (///) | Rust API Guidelines |
| Java | Javadoc | Oracle Javadoc Guide |
| C/C++ | Doxygen | Doxygen manual |
After generation:
/mnt/user-data/outputs/present_files tooldeep-research skill for documenting third-party integrations or dependencies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。