At a glance
What it does
A conversational skill for creating and configuring managed agents on OpenMA.
Before you choose it
Use this SKILL.md when someone asks to create an agent, bot, or task automation on OpenMA. It directs the conversation from task definition through model selection, a bounded system prompt, default tools, agent creation, and suggested next steps such as sessions, skills, and model cards.
Best for
People operating an OpenMA instance who want a structured starting point for creating an AI agent.
Common tasks
- Turn a vague request for an agent or bot into an OpenMA agent configuration.
- Choose an initial model and write a focused system prompt.
- Create an agent with the default OpenMA toolset, then start a session or configure related resources.
Permissions and data
This is instruction content; it does not itself execute API calls or access data.
Permissions- When followed, it may guide creation of agents, sessions, model cards, API keys, skills, or vault resources in OpenMA.
- The skill says OpenMA uses vaults for credentials and scoped agent access, but this record does not independently verify a deployment's data handling.
- An OpenMA instance is needed to carry out the described API or CLI workflow.
- Optional model providers and MCP services depend on the configuration you choose.
- Creating model cards may require an LLM provider API key.
- Programmatic OpenMA access uses an API key according to the skill.
Limitations
- It is guidance for OpenMA-specific agent setup, not a standalone agent runtime.
- The listed model defaults and CLI/API examples are repository claims; they were not run for this curation.
- Actual tool access, model availability, and connected-service permissions depend on the target OpenMA deployment.
What DSHub checked
- The pinned source declares this as the create-agent skill and describes its conversational triggers and six-step creation flow.
- The repository identifies OpenMA as a platform for managed agents and documents OpenMA resources such as sessions, environments, model cards, and vaults.
What DSHub did not check
- The skill was not installed or exercised.
- No OpenMA instance, model provider, vault, or external MCP connection was tested.
- No Harness version range is declared in the supplied evidence.
Pinned install
Primary action
This standalone skill does not have a DSH Plugin install action. Use its source documentation for the delivery method.
Maintainer source
Skill instructions
name: create-agent description: > Help users create and configure openma managed agents through conversation. Trigger when users say "create an agent", "I need an agent that...", "set up an agent for", "build me a bot", or describe a task to automate. Also trigger for "Create with AI" from Dashboard. Also use when users ask about the openma platform, what it can do, how to use the CLI, or how to configure resources.
openma Agent Creator
What is openma?
openma is an open-source platform for building, deploying, and managing AI agents. Think of it as a managed runtime — you define an agent (model + system prompt + tools), the platform handles sandboxed execution, credential management, and session state.
What you can do with it:
- Build agents for any task: coding, research, data analysis, customer support, automation
- Run agents in sandboxes — each session gets an isolated container with file system, shell, and network
- Connect external services via MCP servers (GitHub, Slack, Linear, Notion, etc.) with OAuth
- Use any LLM — Anthropic, OpenAI, DeepSeek, or any OpenAI-compatible provider
- Install community skills from ClawHub to extend agent capabilities
- Manage credentials securely in vaults — agents get scoped access, secrets never leak
- Collaborate — multi-user workspace with API key access for CLI/SDK integration
Creating an Agent
Flow
Understand the goal — ask what the agent should do. If vague, one question: "What's the main task?" Two rounds max, then build.
Pick the model — check
/v1/model_cardsfirst. Defaults:- Complex/coding:
claude-opus-4-6 - General (default):
claude-sonnet-4-6 - Simple/fast:
claude-haiku-4-5-20251001 - OpenAI:
gpt-4o,o3
- Complex/coding:
Write system prompt — specific, actionable, bounded. Not generic.
Select tools — default
agent_toolset_20260401(file ops, bash, web) covers most cases.Create:
POST /v1/agents { "name", "model", "system", "tools": [{"type":"agent_toolset_20260401"}] }Next steps — offer to create session, configure skills, set up model card.
Platform Quick Ref
Agents need a session to run. Sessions need an environment (sandbox).
| Resource | What it is |
|---|---|
| Agent | Model + system prompt + tools config |
| Session | A conversation with an agent in a sandbox |
| Environment | Sandbox runtime (default works for most) |
| Model Card | API key + provider config for an LLM |
| Vault | Secure credential storage for MCP/CLI secrets |
| Skill | SKILL.md that gives agents domain expertise |
| API Key | Programmatic access token for CLI/SDK |
oma agents create <name> # create agent
oma sessions create --agent <id> --env <id> # start session
oma sessions message <id> <text> # send message
oma models create --name <n> --model-id <id> --api-key <key>
oma keys create # generate API key
oma skills install <slug> # install from ClawHub
oma --help # full command list
Model card providers: ant, oai, ant-compatible, oai-compatible.
Operate deliberately
Install and manage
Prerequisites and target Profile
Target: Openma Platform Users Profile, Agent Builders Profile
Delivery: Skill Files — https://raw.githubusercontent.com/openma-ai/open-managed-agents/6fb37bf73bff09b9b1ec70dd6af07191fa7402d7/skills/create-agent/SKILL.md。
Compatibility and access
For users configuring agents on an OpenMA instance: Not declared in supplied evidence。
Review compatibility evidence ↗
Risk facts
The guidance includes configuring model API keys and vault-backed credentials.
Evidence ↗Evidence and editorial reviewManifest, Bundle patch, distribution and freshness
Immutable evidence
Review status and source activity
Available under the repository's Apache-2.0 license; review its terms before redistribution or modification.
AI reviewed Sep 16, 2026, 2:25 PM UTC。GitHub facts last checked Sep 16, 2026, 2:25 PM UTC。
No material source change has been recorded since this evidence baseline.