OpenHarness is infrastructure for AI agents that provides tool use, skills, memory, and coordination between multiple agents. Its personal agent, ohmo, uses this infrastructure to work over long sessions through chat platforms and to write code, run tests, and open pull requests. The catalogue add-ons extend or configure workflows for agents running on OpenHarness.
Borrowing it
Nothing to install: this file belongs to HKUDS/OpenHarness. 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/HKUDS/OpenHarness/main/.claude/skills/harness-eval/SKILL.mdgit clone --depth 1 https://github.com/HKUDS/OpenHarnessWrote 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/hkuds/openharness/harness-eval)<a href="https://agentmods.dev/skills/hkuds/openharness/harness-eval"><img src="https://agentmods.dev/badge/skills/hkuds/openharness/harness-eval/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/openharness/harness-eval"><img src="https://agentmods.dev/badge/skills/hkuds/openharness/harness-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 33 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Privilege Escalation · line 45 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
- medium Privilege Escalation · line 46 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00069 | $0.01975 |
| Opus 5.5 | $0.00028 | $0.00790 |
| Sonnet 5.5 | $0.00014 | $0.00395 |
| Haiku 4.5 | $0.00007 | $0.00198 |
Grade B, and why
harness-eval scanned grade B with 1 finding 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 2mo 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt-get update How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harness Eval — End-to-End Feature Validation
Validate OpenHarness features by running real agent loops against an unfamiliar codebase with actual LLM API calls. Every test exercises the full stack: API client → model → tool calls → execution → result.
Core Principles
- Test on an unfamiliar project — never test on OpenHarness itself (the agent modifies its own code). Clone a real project as the workspace.
- Use real API calls — no mocks. Configure a real LLM endpoint.
- Multi-turn conversations — always test 2+ turns where the model needs prior context.
- Combine features — test hooks+skills+agent loop together, not in isolation.
- Verify tool execution — inspect tool call lists and output files, not just model text.
Workflow
1. Prepare Workspace
Clone an unfamiliar project (do not use OpenHarness):
git clone https://github.com/HKUDS/AutoAgent /tmp/eval-workspace
2. Configure Environment
export ANTHROPIC_API_KEY=sk-xxx
export ANTHROPIC_BASE_URL=https://api.moonshot.cn/anthropic # or any provider
export ANTHROPIC_MODEL=kimi-k2.5
For long-running real evals, do not artificially lower max_turns. Use the product default (200) unless the user explicitly wants a tighter bound.
3. Prepare Real Sandbox Runtime When Relevant
If the task is validating sandbox behavior, install and verify the actual runtime before running agent loops:
npm install -g @anthropic-ai/sandbox-runtime
sudo apt-get update
sudo apt-get install -y bubblewrap ripgrep
which srt
which bwrap
which rg
srt --version
Then run a minimal smoke check through OpenHarness, not just raw srt, so you verify the real adapter path:
from pathlib import Path
from openharness.config.settings import Settings, SandboxSettings, save_settings
from openharness.tools.bash_tool import BashTool
cfg = Path("/tmp/openharness-sandbox-settings.json")
save_settings(Settings(sandbox=SandboxSettings(enabled=True, fail_if_unavailable=True)), cfg)
# Point config loader at this file, then run BashTool on a tiny command such as `pwd`.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 194 lines · 69 tokens per session scan B c7e88107a6fd
harness-eval is a skill published in the GitHub repository HKUDS/OpenHarness (16,128 stars, last pushed 4mo ago), licensed MIT. It adds 69 tokens to every session and 1,975 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
verify-omnigent
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs.…
test-master
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation…
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
critique
Adversarial design critique of the current work. Render it, look at it, and argue for rejection. Run after the gates are green, never instead of them.