build-omnigent

name	build-omnigent
description	Patterns and templates for generating valid Omnigent agent directories. Load when ready to create files.

Agent Generation

Use these patterns to generate a valid agent directory. Always generate the minimal set of files needed — don't over-engineer.

Every template below has been validated with the same parser/validator that omnigent server uses. If your environment exposes the validate_agent tool (the dedicated agent-authoring environment does), run it after generating files to confirm the spec loads. Load the omnigent-knowledge skill if you need the deeper field reference (executor types, os_env, guardrails, sandboxing).

Step 1: Choose a directory name

Use the agent name in kebab-case: my-research-agent/

Step 2: Generate config.yaml

Always include:

• spec_version: 1
• name (lowercase, hyphens OK)
• description (one sentence)
• instructions — path to a file (default AGENTS.md) or inline text. (prompt: is an accepted alias; instructions: wins if both are set.)
• executor — how the agent runs. See Step 2a.

Include if needed:

• tools.builtins — built-in tools. The current set is download_file, export_agent, list_files, search_conversations, upload_file, web_fetch, web_search. If the list_builtin_tools tool is available, call it for the authoritative live set rather than trusting this list.
• tools.agents — sub-agents (must match agents/ subdirectories).
• os_env — filesystem/shell access for harness agents (see the shell-capable template).
• interaction.modalities — if the agent handles images or files.
• guardrails — runtime policy gates (see omnigent-knowledge).

Step 2a: Choose an executor

executor.type must be one of claude_sdk, agents_sdk, or omnigent. There is no llm executor — do not use it.

Need	executor
A fresh, simple LLM agent (default)	claude_sdk (Anthropic) or agents_sdk (OpenAI), in-process
Existing Claude SDK / OpenAI Agents SDK code	claude_sdk / agents_sdk
A CLI/coding harness, shell + file tools, sub-agents	omnigent + a config.harness

When executor.type: omnigent, config.harness is required and must be one of: claude-native (Claude Code, full coding tools), claude-sdk, codex-native, codex, openai-agents, open-responses, pi. (claude is an alias for claude-native.)

Model selection is optional — if omitted, the executor resolves the provider's default model from the configured credentials (e.g. an Anthropic key, a Claude subscription, or a Databricks profile). Pin one only when asked; see omnigent-knowledge for executor.model / auth.

Step 3: Generate AGENTS.md

Write a focused system prompt:

• Identity: "You are a [role] that [does what]."
• Capabilities: what tools/skills are available
• Constraints: what NOT to do
• Style: how to communicate

Keep it under 500 words for a starter agent. The user can expand later.

Step 4: Generate skills (optional)

Only generate skills if the agent has distinct modes of operation. Each skill needs:

skills/<skill-name>/SKILL.md

With YAML frontmatter:

---
name: skill-name
description: One-line description of what this skill does.
---

Detailed instructions for when this skill is loaded...

Templates

Minimal agent (simplest — in-process SDK)

config.yaml:

spec_version: 1
name: {agent_name}
description: {description}
executor:
  type: claude_sdk      # or agents_sdk for OpenAI
instructions: AGENTS.md

AGENTS.md:

You are {agent_name}, {description}.

Answer questions clearly and concisely. If you don't know something,
say so rather than guessing.

Agent with web search

config.yaml:

spec_version: 1
name: {agent_name}
description: {description}
executor:
  type: claude_sdk
tools:
  builtins:
    - web_search        # one of the builtins listed in Step 2
interaction:
  modalities:
    input: [text]
    output: [text]
instructions: AGENTS.md

Harness agent with shell + filesystem access

Use the omnigent executor with a coding harness when the agent needs to run commands and read/write files. os_env grants OS access; the harness exposes sys_os_read / sys_os_write / sys_os_edit / sys_os_shell.

config.yaml:

spec_version: 1
name: {agent_name}
description: {description}
executor:
  type: omnigent
  config:
    harness: claude-native
    # Headless runs can't answer approval prompts — bypass them. Pair
    # with a read-only prompt and/or a blast_radius guardrail for safety.
    permission_mode: bypassPermissions   # codex-native uses `yolo: true`
os_env:
  type: caller_process
  cwd: .
  sandbox:
    type: none          # or linux_bwrap / darwin_seatbelt to sandbox
instructions: AGENTS.md

Agent with MCP server integration

Directory structure:

{agent_name}/
  config.yaml
  AGENTS.md
  tools/
    mcp/
      github.yaml

config.yaml:

spec_version: 1
name: {agent_name}
description: {description}
executor:
  type: claude_sdk
instructions: AGENTS.md

tools/mcp/github.yaml:

transport: http
url: https://your-mcp-server.example.com/sse
headers:
  Authorization: Bearer ${{{mcp_token_var}}}

Multi-agent system with sub-agents

Sub-agents need the omnigent executor (it provides the spawn tools).

Directory structure:

{agent_name}/
  config.yaml
  AGENTS.md
  agents/
    {sub_agent_1}/
      config.yaml
    {sub_agent_2}/
      config.yaml

Parent config.yaml:

spec_version: 1
name: {agent_name}
description: {description}
executor:
  type: omnigent
  config:
    harness: claude-sdk
tools:
  agents:
    - {sub_agent_1}
    - {sub_agent_2}
instructions: AGENTS.md

Sub-agent config (agents/{sub_agent_1}/config.yaml):

spec_version: 1
name: {sub_agent_1}
description: {sub_agent_1_description}
executor:
  type: omnigent
  config:
    harness: claude-sdk
instructions: |
  You are {sub_agent_1}. {sub_agent_1_instructions}

Parent AGENTS.md should reference sub-agents:

You have sub-agents you can delegate to:
- **{sub_agent_1}** — {sub_agent_1_description}
- **{sub_agent_2}** — {sub_agent_2_description}

Call `sys_session_send(type="<name>", input="<task>")` to dispatch a
declared sub-agent. Emit multiple `sys_session_send` tool calls in the
same response to run them in parallel; results arrive via the inbox.

Environment variable naming conventions

When pinning credentials with ${ENV_VAR}, map providers to their standard env var names:

• openai → OPENAI_API_KEY
• anthropic → ANTHROPIC_API_KEY
• gemini → GEMINI_API_KEY or GOOGLE_API_KEY
• groq → GROQ_API_KEY
• deepseek → DEEPSEEK_API_KEY
• xai → XAI_API_KEY
• mistral → MISTRAL_API_KEY
• databricks → DATABRICKS_TOKEN (or an auth.profile)

Validation checklist

Before presenting the generated files to the user, verify (and if validate_agent is available, run it to confirm):

• spec_version: 1 is present
• name is set and uses lowercase + hyphens
• executor.type is one of claude_sdk, agents_sdk, omnigent
• If executor.type: omnigent, executor.config.harness is set to a valid harness
• instructions (or prompt) points to a file that exists or is inline text
• When declaring tools.agents, the parent uses executor.type: omnigent, and each entry has a matching agents/ subdirectory (sub-agents may use any executor)
• tools.builtins names are from the known set (Step 2) — or, if list_builtin_tools is available, were confirmed against it
• Skill names match their directory names and use [a-z0-9-]+ pattern