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Get Started Free →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.
.claude/skills/sanjay3290-manus/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 59% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 76% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 163% | 0% |
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.
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].Requires MANUS_API_KEY environment variable. Base URL: https://api.manus.ai
task_id immediatelystatus is completed or failedbashcurl -s -X POST "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"prompt":"<task description>","agentProfile":"manus-1.6"}'
Agent Profiles:
manus-1.6-lite - Fast, simple tasks (quick lookups, simple questions)manus-1.6 - Standard (default, good for most research)manus-1.6-max - Complex reasoning (deep research, multi-source analysis, detailed reports)Response:
json{"task_id":"abc123","task_title":"...","task_url":"https://manus.im/app/abc123"}
bashcurl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY"
Status values: pending, running, completed, failed
Poll every 5-10 seconds until completed.
Extract text output:
bashcurl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY" | jq -r '.output[] | select(.role=="assistant") | .content[] | select(.type=="output_text") | .text'
Extract file attachments:
bashcurl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY" | jq -r '.output[] | select(.role=="assistant") | .content[] | select(.type=="output_file") | "\(.fileName): \(.fileUrl)"'
bashcurl -s -X GET "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" | jq '.data[] | {id, status, title: .metadata.task_title}'
Continue an existing task by including taskId:
bashcurl -s -X POST "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"prompt":"follow-up question","taskId":"abc123","agentProfile":"manus-1.6"}'
bashcurl -s -X DELETE "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY"
| Parameter | Description | |-----------|-------------| | taskMode | chat, adaptive, or agent | | projectId | Associate with project for shared instructions | | attachments | Array of file objects (see below) | | connectors | Pre-configured connector IDs (Gmail, Calendar, Notion) | | createShareableLink | Enable public access URL |
Attach files using one of three formats:
URL attachment:
json{"prompt":"Analyze this","attachments":[{"type":"url","url":"https://example.com/doc.pdf"}]}
Base64 attachment:
json{"prompt":"What's in this image?","attachments":[{"type":"base64","data":"<base64>","mime_type":"image/png"}]}
File ID (after upload):
json{"prompt":"Review this file","attachments":[{"type":"file","file_id":"file-xxx"}]}
Create a project with shared instructions:
bashcurl -s -X POST "https://api.manus.ai/v1/projects" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"name":"My Project","instruction":"Always respond concisely"}'
Use project in task:
bashcurl -s -X POST "https://api.manus.ai/v1/tasks" -H "API_KEY:$MANUS_API_KEY" -H "Content-Type:application/json" -d '{"prompt":"...","projectId":"proj_xxx","agentProfile":"manus-1.6"}'
manus-1.6-lite for simple queries (faster, cheaper)manus-1.6 or manus-1.6-max for research taskstask_url so user can view progress in browsercredit_usage field to track consumptionCheck for failed tasks:
bashcurl -s -X GET "https://api.manus.ai/v1/tasks/{task_id}" -H "API_KEY:$MANUS_API_KEY" | jq '{status, error}'
If status is failed, the error field contains the reason. Common issues:
MANUS_API_KEY is setmanus-1.6-maxFor complete endpoint documentation, see references/api.md.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 29,674 | 32,115 | +8% | 1 | 1 | 0% | 5,341 | 2,105 | -61% | 0 | 0 | — |
case-02 | fail→pass | 14,663 | 11,696 | -20% | 1 | 1 | 0% | 2,728 | 4,327 | +59% | 0 | 0 | — |
case-03 | fail→pass | 18,306 | 10,789 | -41% | 1 | 1 | 0% | 3,500 | 4,309 | +23% | 0 | 0 | — |
case-04 | fail→pass | 7,211 | 2,833 | -61% | 1 | 1 | 0% | 1,371 | 2,417 | +76% | 0 | 0 | — |
case-05 | fail→pass | 9,378 | 4,232 | -55% | 1 | 1 | 0% | 1,889 | 2,757 | +46% | 0 | 0 | — |
case-06 | pass→pass | 6,392 | 3,334 | -48% | 1 | 1 | 0% | 1,222 | 2,468 | +102% | 0 | 0 | — |
case-07 | pass→pass | 3,983 | 2,462 | -38% | 1 | 1 | 0% | 775 | 2,311 | +198% | 0 | 0 | — |
case-08 | fail→pass | 4,414 | 2,192 | -50% | 1 | 1 | 0% | 850 | 2,234 | +163% | 0 | 0 | — |
case-09 | fail→pass | 3,619 | 1,884 | -48% | 1 | 1 | 0% | 639 | 2,150 | +236% | 0 | 0 | — |
case-10 | fail→pass | 4,968 | 1,986 | -60% | 1 | 1 | 0% | 769 | 2,138 | +178% | 0 | 0 | — |
case-11 | fail→pass | 4,897 | 2,126 | -57% | 1 | 1 | 0% | 939 | 2,198 | +134% | 0 | 0 | — |
case-12 | fail→pass | 4,761 | 2,288 | -52% | 1 | 1 | 0% | 961 | 2,280 | +137% | 0 | 0 | — |
case-13 | fail→pass | 3,290 | 1,519 | -54% | 1 | 1 | 0% | 622 | 2,104 | +238% | 0 | 0 | — |
case-14 | fail→pass | 6,738 | 3,270 | -51% | 1 | 1 | 0% | 1,276 | 2,375 | +86% | 0 | 0 | — |
case-15 | fail→pass | 7,177 | 2,223 | -69% | 1 | 1 | 0% | 1,322 | 2,230 | +69% | 0 | 0 | — |
case-16 | fail→pass | 9,359 | 3,485 | -63% | 1 | 1 | 0% | 1,711 | 2,377 | +39% | 0 | 0 | — |
case-17 | fail→pass | 8,887 | 4,531 | -49% | 1 | 1 | 0% | 1,668 | 2,729 | +64% | 0 | 0 | — |
case-18 | fail→pass | 12,549 | 4,286 | -66% | 1 | 1 | 0% | 2,264 | 2,598 | +15% | 0 | 0 | — |
case-19 | pass→pass | 5,139 | 2,828 | -45% | 1 | 1 | 0% | 967 | 2,351 | +143% | 0 | 0 | — |
case-20 | fail→pass | 4,974 | 1,163 | -77% | 1 | 1 | 0% | 920 | 2,015 | +119% | 0 | 0 | — |
case-21 | fail→pass | 3,898 | 2,708 | -31% | 1 | 1 | 0% | 748 | 2,334 | +212% | 0 | 0 | — |
case-22 | pass→pass | 2,786 | 2,380 | -15% | 1 | 1 | 0% | 502 | 2,293 | +357% | 0 | 0 | — |
case-23 | pass→pass | 2,728 | 2,088 | -23% | 1 | 1 | 0% | 658 | 2,255 | +243% | 0 | 0 | — |
case-24 | pass→pass | 3,637 | 3,011 | -17% | 1 | 1 | 0% | 663 | 2,375 | +258% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted, and 23 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +71 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.