manus
Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.
Best use case
manus is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.
Teams using manus should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/manus/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How manus Compares
| Feature / Agent | manus | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
AI Agent for SaaS Idea Validation
Use AI agent skills for SaaS idea validation, market research, customer discovery, competitor analysis, and documenting startup hypotheses.
SKILL.md Source
# Manus
Manus is an autonomous AI agent that handles complex tasks asynchronously. Particularly strong at **deep research** with parallel processing, web browsing, and generating comprehensive reports with data visualizations.
## When to Use Manus
- **Deep research** - Market analysis, competitive landscaping, product comparisons
- **Stock/financial analysis** - Company research, technical analysis, price charts
- **Product research** - Feature comparisons across brands, specs analysis
- **Report generation** - Creates markdown reports, CSVs, charts, and visualizations
- **Multi-source synthesis** - Gathers and combines information from multiple websites
- **Long-running tasks** - Anything taking 1-10+ minutes of autonomous work
## Research Prompt Examples
Effective research prompts are specific about scope, sources, and desired output:
**Product comparison:**
```
Find the best 4K monitors for Mac with Thunderbolt connectivity and 120Hz+ refresh rate.
Focus on BenQ, Samsung, Dell, LG. Only include models released in the last year.
Compare specs, prices, and Mac-specific features. Output a comparison table.
```
**Stock/company analysis:**
```
Analyze [TICKER] stock: company profile, recent performance, technical indicators,
valuation metrics, and insider activity. Include a price chart for the past year.
```
**Market research:**
```
Research the [industry] market: key players, market size, growth trends,
recent developments, and competitive landscape. Focus on [region/segment].
```
**Competitive analysis:**
```
Compare [Product A] vs [Product B] vs [Product C]: features, pricing,
user reviews, pros/cons. Create a decision matrix for [use case].
```
## Environment Setup
Requires `MANUS_API_KEY` environment variable. Base URL: `https://api.manus.ai`
## Core Workflow
1. **Create task** → Returns `task_id` immediately
2. **Poll status** → Check until `status` is `completed` or `failed`
3. **Return results** → Extract output text and file attachments
## Creating a Task
```bash
curl -s -X POST "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"prompt":"<task description>","agentProfile":"manus-1.6"}'
```
**Agent Profiles:**
- `manus-1.6-lite` - Fast, simple tasks (quick lookups, simple questions)
- `manus-1.6` - Standard (default, good for most research)
- `manus-1.6-max` - Complex reasoning (deep research, multi-source analysis, detailed reports)
**Response:**
```json
{"task_id":"abc123","task_title":"...","task_url":"https://manus.im/app/abc123"}
```
## Checking Task Status
```bash
curl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY"
```
**Status values:** `pending`, `running`, `completed`, `failed`
Poll every 5-10 seconds until completed.
**Extract text output:**
```bash
curl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY" | jq -r '.output[] | select(.role=="assistant") | .content[] | select(.type=="output_text") | .text'
```
**Extract file attachments:**
```bash
curl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY" | jq -r '.output[] | select(.role=="assistant") | .content[] | select(.type=="output_file") | "\(.fileName): \(.fileUrl)"'
```
## Listing Tasks
```bash
curl -s -X GET "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" | jq '.data[] | {id, status, title: .metadata.task_title}'
```
## Multi-turn Conversations
Continue an existing task by including `taskId`:
```bash
curl -s -X POST "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"prompt":"follow-up question","taskId":"abc123","agentProfile":"manus-1.6"}'
```
## Delete a Task
```bash
curl -s -X DELETE "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY"
```
## Advanced Options
| Parameter | Description |
|-----------|-------------|
| `taskMode` | `chat`, `adaptive`, or `agent` |
| `projectId` | Associate with project for shared instructions |
| `attachments` | Array of file objects (see below) |
| `connectors` | Pre-configured connector IDs (Gmail, Calendar, Notion) |
| `createShareableLink` | Enable public access URL |
## File Attachments
Attach files using one of three formats:
**URL attachment:**
```json
{"prompt":"Analyze this","attachments":[{"type":"url","url":"https://example.com/doc.pdf"}]}
```
**Base64 attachment:**
```json
{"prompt":"What's in this image?","attachments":[{"type":"base64","data":"<base64>","mime_type":"image/png"}]}
```
**File ID (after upload):**
```json
{"prompt":"Review this file","attachments":[{"type":"file","file_id":"file-xxx"}]}
```
## Projects
Create a project with shared instructions:
```bash
curl -s -X POST "https://api.manus.ai/v1/projects" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"name":"My Project","instruction":"Always respond concisely"}'
```
Use project in task:
```bash
curl -s -X POST "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"prompt":"...","projectId":"proj_xxx","agentProfile":"manus-1.6"}'
```
## Best Practices
- Use `manus-1.6-lite` for simple queries (faster, cheaper)
- Use `manus-1.6` or `manus-1.6-max` for research tasks
- Always report `task_url` so user can view progress in browser
- Poll status with reasonable intervals (5-10s for simple, 15-30s for research)
- Check `credit_usage` field to track consumption
- For research: be specific about scope, timeframe, sources, and desired output format
- Research tasks typically produce multiple output files (reports, CSVs, charts) - extract all attachments
- Manus uses parallel processing for multi-faceted research - one prompt can cover multiple angles
## Error Handling
Check for failed tasks:
```bash
curl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY" | jq '{status, error}'
```
If `status` is `failed`, the `error` field contains the reason. Common issues:
- Invalid API key → Check `MANUS_API_KEY` is set
- Task timeout → Simplify prompt or use `manus-1.6-max`
- Rate limited → Wait and retry
## API Reference
For complete endpoint documentation, see [references/api.md](references/api.md).Related Skills
genderapi-io-automation
Automate Genderapi IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
gender-api-automation
Automate Gender API tasks via Rube MCP (Composio). Always search tools first for current schemas.
fred-economic-data
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
fidel-api-automation
Automate Fidel API tasks via Rube MCP (Composio). Always search tools first for current schemas.
fastapi-templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
fastapi-router-py
Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or add...
fastapi-pro
Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns.
expo-api-routes
Guidelines for creating API routes in Expo Router with EAS Hosting
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
eodhd-apis-automation
Automate Eodhd Apis tasks via Rube MCP (Composio). Always search tools first for current schemas.
dotnet-backend
Build ASP.NET Core 8+ backend services with EF Core, auth, background jobs, and production API patterns.
dotnet-backend-patterns
Master C#/.NET backend development patterns for building robust APIs, MCP servers, and enterprise applications. Covers async/await, dependency injection, Entity Framework Core, Dapper, configuratio...