agentmods.dev

create-agent

A conversational skill for creating and configuring managed AI agents in oma, an open-source platform for running agents with models, tools, isolated sessions, and stored credentials.

In plain words
What is it for?
It helps define an agent's task, choose a model, connect services such as GitHub or Slack, manage credentials, and prepare agents for coding, research, analysis, or automation.
Why use it?
It helps turn a described task into an agent setup without requiring the user to configure every platform detail manually.

Skill for Claude CodeCodex

5 13d ago A scan 88 tokens copy · 92% Apache-2.0
View the source file duyet/oma
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/duyet/oma/create-agent
Any agent
npx skills add duyet/oma --skill create-agent
Clone the repo
git clone --depth 1 https://github.com/duyet/oma

Made for: Claude Code, Codex.

Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 800 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 92% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00088 $0.00800
Opus 5 $0.00044 $0.00400
Sonnet 5 $0.00018 $0.00160
Haiku 4.5 $0.00009 $0.00080

Measured 2d ago against content hash 5b3970c5b84b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

create-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

92% identical to create-agent — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/create-agent/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

oma Agent Creator

What is oma?

oma 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

  1. Understand the goal — ask what the agent should do. If vague, one question: "What's the main task?" Two rounds max, then build.

  2. Pick the model — check /v1/model_cards first. Defaults:

    • Complex/coding: claude-opus-4-6
    • General (default): claude-sonnet-4-6
    • Simple/fast: claude-haiku-4-5-20251001
    • OpenAI: gpt-4o, o3

  3. Write system prompt — specific, actionable, bounded. Not generic.

  4. Select tools — default agent_toolset_20260401 (file ops, bash, web) covers most cases.

  5. Create:

    POST /v1/agents
{ "name", "model", "system", "tools": [{"type":"agent_toolset_20260401"}] }

  6. 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

Read the full file on GitHub · 78 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 78 lines · 88 tokens per session scan A 5b3970c5b84b

Subscribe to this mod's changes

create-agent is a skill published in the GitHub repository duyet/oma (5 stars, last pushed 13d ago), licensed Apache-2.0. It adds 88 tokens to every session and 800 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to create-agent, differing in 10 lines, and is treated as a copy.

Analytics and metricsData and AIResearch
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