create-agent
Scaffold a new custom agent configuration with Claude Agent SDK patterns. Use when adding a new specialized subagent.
What this skill does
# Create Agent
Scaffold a new custom agent configuration.
## Arguments
- `$1`: Agent name (kebab-case)
- `$ARGUMENTS`: High-level purpose description (after name)
## Instructions
You are creating a new custom agent scaffold using Claude Agent SDK patterns.
### Step 1: Parse Arguments
Extract:
- Agent name from `$1` (required)
- Purpose from remaining arguments
If no name provided, STOP and ask for agent name.
If no purpose provided, STOP and ask for purpose description.
### Step 2: Design Agent
Based on the purpose, determine:
**Model Selection:**
- Simple transformations -> Haiku
- Balanced tasks -> Sonnet
- Complex reasoning -> Opus
**System Prompt Architecture:**
- Building new product -> Override
- Extending Claude Code -> Append
**Tool Requirements:**
- What tools needed?
- What tools should be blocked?
- Any custom tools required?
### Step 3: Generate System Prompt
Create system prompt following structure:
```markdown
# [Agent Name]
## Purpose
[Identity and role - 2-3 sentences]
## Instructions
[Core behaviors - bullet list]
## Constraints
[What agent must NOT do]
## Examples (if needed)
[Input/Output pairs]
```
### Step 4: Generate Configuration
Create ClaudeAgentOptions configuration:
```python
from claude_agent_sdk import ClaudeAgentOptions
def load_system_prompt() -> str:
prompt_file = Path(__file__).parent / "prompts" / "[agent]_system.md"
with open(prompt_file, "r") as f:
return f.read().strip()
options = ClaudeAgentOptions(
system_prompt=load_system_prompt(),
model="claude-[model]-...",
allowed_tools=[...],
disallowed_tools=[...],
)
```
### Step 5: Generate Entry Point
Create basic agent script:
```python
import asyncio
from pathlib import Path
from claude_agent_sdk import (
query,
ClaudeAgentOptions,
AssistantMessage,
TextBlock,
ResultMessage,
)
async def main():
options = ClaudeAgentOptions(...)
prompt = input("Enter prompt: ")
async for message in query(prompt=prompt, options=options):
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(block.text)
elif isinstance(message, ResultMessage):
print(f"Cost: ${message.total_cost_usd:.6f}")
if __name__ == "__main__":
asyncio.run(main())
```
## Output
```markdown
## Agent Created
**Name:** [agent-name]
**Model:** [haiku/sonnet/opus]
**Architecture:** [override/append]
### Files to Create
1. `[agent-name]/prompts/[agent]_system.md` - System prompt
2. `[agent-name]/[agent]_agent.py` - Agent implementation
3. `[agent-name]/README.md` - Documentation
### System Prompt
```markdown
[Generated system prompt]
```
### Configuration
```python
[Generated configuration]
```
### Entry Point
```python
[Generated script]
```
### Next Steps
1. Create the directory structure
2. Save the system prompt
3. Save the agent script
4. Test with simple prompt
5. Add custom tools if needed
6. Add governance hooks if needed
## Notes
- See @custom-agent-design skill for design workflow
- See @core-four-custom.md for configuration options
- See @system-prompt-architecture.md for prompt patterns
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.