Omnigent is an open-source orchestration layer for running and coordinating different AI coding agents through one system. It is for developers who want to combine agents, apply policies and sandboxing, and continue sessions across devices. The catalogue add-ons extend its agent workflows.
Borrowing it
Nothing to install: this file belongs to omnigent-ai/omnigent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/omnigent-ai/omnigent/main/.claude/skills/harness-integration-guide/SKILL.mdgit clone --depth 1 https://github.com/omnigent-ai/omnigentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/omnigent-ai/omnigent/harness-integration-guide)<a href="https://agentmods.dev/skills/omnigent-ai/omnigent/harness-integration-guide"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/harness-integration-guide.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.02544 |
| Opus 5 | $0.00023 | $0.01272 |
| Sonnet 5 | $0.00009 | $0.00509 |
| Haiku 4.5 | $0.00005 | $0.00254 |
Grade A, and why
harness-integration-guide 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harness integration guide
This skill describes the feature matrix every Omnigent harness must consider. Use it when planning, reviewing, or implementing a new harness.
Omnigent has two distinct harness tracks with different architectures and feature sets:
- SDK/subprocess harnesses — run the vendor model directly (in-process SDK, CLI subprocess, or ACP subprocess). They own the model lifecycle.
- Native harnesses — wrap a vendor's own TUI or server and mirror its output into Omnigent. They observe and relay, rather than drive.
Part 1 — SDK / subprocess harnesses
These harnesses run the vendor model directly and bridge Omnigent tools into the vendor's tool-calling interface.
Capability matrix
| Capability | What it means |
|---|---|
| Connects to Omnigent MCP | Harness exposes/consumes tools via the MCP protocol (in-proc SDK MCP server) |
| Model override | User can select a model via --model / config; some harnesses are vendor-locked (e.g. Claude-only, GPT-only, Gemini-only) |
| Auth | How credentials are obtained — API key, gateway token, vendor CLI login, OAuth, etc. |
| Streaming | Harness forwards token-level or delta-level streaming to the Omnigent forwarder |
| Omnigent policies | Harness enforces Omnigent-side tool policies — must support ALLOW, ASK, and DENY verdicts for both tool calls and tool results |
| Native elicitation | When a policy verdict is ASK, the harness surfaces the approval request in the Omnigent web UI so the user can approve or deny |
| Interrupt | User can cancel a running turn mid-stream |
| Live queue (concurrent) | Multiple turns can be queued and processed concurrently |
| Tool-boundary steer | Omnigent can inject steering text at tool-call boundaries |
| Resume/fork from Omnigent transcript | Rebuild a conversation from a stored Omnigent transcript (replay history, seed prompt, or vendor session ID) |
| Compaction | Long conversations are compacted; harness surfaces CompactionComplete events |
| Reasoning | Model reasoning/thinking tokens are forwarded |
| Images | Image content (screenshots, diagrams) is forwarded — full binary, path reference, or text-flattened |
| Cost tracking | Harness reports token usage and cost data back to Omnigent for each turn |
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.
- 7d ago First seen · 200 lines · 45 tokens per session scan A 46df66c35a76
harness-integration-guide is a skill published in the GitHub repository omnigent-ai/omnigent (9,727 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 2,544 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
Codomyrmex
Full-spectrum coding workspace skill providing 627 production MCP @mcptool lines across 130 top-level modules. USE WHEN user says 'verify codomyrmex', 'codomyrmexVerify', 'audit codomyrmex', 'trust codomyrmex', 'codomyrmexTrust', 'trust tools', 'enable destructive tools', 'check pai status', 'codomyrmex tools'…
hugging-face-tool-builder
Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
ai-engineer
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design…
data-scientist
!cat skills/shared/protocols/ux-protocol.md 2>/dev/null || true !cat skills/shared/protocols/input-validation.md 2>/dev/null || true !cat skills/shared/protocols/tool-efficiency.md 2>/dev/null || true !cat .production-grade.yaml 2>/dev/null || echo "No config — using defaults".
agent-hub-init
Scaffold the agent-hub project profile (.claude/agent-hub/profile.yml + CLAUDE.md) for the current project so the super-team can specialize to it. Use when a project has no profile yet, or when the user says "onboard", "set up the team here", or "create the profile".
decision-log
Record and read architectural decisions (ADRs) for a project so the team's choices persist across sessions. Use when the user says "decide", "record this decision", "why did we choose X", or when a significant technical choice is made.