Delegate complex tasks to Amplifier's multi-agent framework. Use when: (1) research/comparison needing multiple perspectives, (2) multi-file code projects, (3) architecture/design reviews, (4) user asks for deep/thorough work. NOT for: simple Q&A, quick edits, casual chat, anything needing <5s response. CLI: amplifier-openclaw.
npx skills add https://github.com/microsoft/amplifier-app-openclaw --skill amplifier-openclaw
Amplifier is a multi-agent AI framework. Delegate tasks that benefit from specialist agents, structured workflows, or parallel investigation.
High confidence → delegate immediately:
Medium confidence → offer the choice:
Low confidence → handle yourself:
exec command:"amplifier-openclaw run 'Research the top 3 Python web frameworks' --bundle foundation" background:true timeout:600
Pass --model to override the default model:
exec command:"amplifier-openclaw run --model your-preferred-model 'Deep code review' --bundle foundation" background:true timeout:600
Tip: Pass --model matching the model from your OpenClaw Runtime line so Amplifier uses the same one.
amplifier-openclaw bundles list
| Bundle | Best For |
|--------|----------|
| foundation | General: research, analysis, planning (default) |
| superpowers | Multi-agent brainstorm, deep investigation |
| coder | Code generation, refactoring, debugging |
# Start a named session
exec command:"amplifier-openclaw run --session-name my-project 'Start building the auth module' --bundle foundation" background:true
# Resume later
exec command:"amplifier-openclaw run --resume --session-name my-project 'Now add unit tests'" background:true
Amplifier supports slash-command modes in prompts. Modes do not carry over between runs — include the mode at the start of each prompt:
# Brainstorm mode (uses all agents)
exec command:"amplifier-openclaw run --bundle superpowers '/brainstorm How should we architect the new API?'" background:true
# Research mode
exec command:"amplifier-openclaw run --bundle foundation '/research Latest advances in RAG'" background:true
{
"response": "The analysis found...",
"usage": {
"input_tokens": 28566,
"output_tokens": 1800,
"estimated_cost": 0.12,
"tool_invocations": 3
},
"status": "completed"
}
exec command:"amplifier-openclaw cost --period week"
Report costs only when asked or when notable (>$1).
response: Present to the user (the main output)error: Report in plain language, don't dump raw JSONusage.estimated_cost: May be 0.0 — don't alarm about zerostatus: "completed", "cancelled", or error stateIf not already installed:
uv tool install "amplifier-app-openclaw @ git+https://github.com/microsoft/[email protected]"
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
A practical, jargon-free guide to fp-ts functional programming - the 80/20 approach that gets results without the academic overhead. Use when writing TypeScript with fp-ts library.
Take microsoft/amplifier-openclaw from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.