OpenHarness: Skill for Claude Code

.claude/skills/harness-eval/SKILL.md

harness-eval is a skill for Claude Code from HKUDS/OpenHarness. It costs 69 tokens per session (1,975 once invoked), scanned B, original, MIT.

A guide for testing an agent system from start to finish with real language-model calls. It runs the system on an unfamiliar codebase and checks multiple conversation turns and tool use.

In plain words
What is it for?
Use it for end-to-end validation of agent loops, real API integrations, hooks, skills, multi-turn context, and generated files.
Why use it?
Mocked or isolated tests may not reveal problems in the full path from the model request through tool execution and returned results.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is HKUDS/OpenHarness's own configuration. It tells Claude Code how to work on OpenHarness itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything OpenHarness configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python tests/test_merged_prs_on_autoagent.py # PR feature tests.

About the project

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.

HKUDS/OpenHarness · 16,128 stars · on GitHub

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/HKUDS/OpenHarness/main/.claude/skills/harness-eval/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/HKUDS/OpenHarness

Made for: Claude Code.

Wrote 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.

agentmods badge for harness-eval

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/openharness/harness-eval/github.svg)](https://agentmods.dev/skills/hkuds/openharness/harness-eval)
Your own site
<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.

agentmods 80×15 button for harness-eval

Your own site · 80×15
<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>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,975 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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.
How audits are shown
Origin original No closer match found 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 5.1 $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

Measured 2mo ago against content hash c7e88107a6fd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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
.claude/skills/harness-eval/SKILL.md · 194 lines

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

  1. Test on an unfamiliar project — never test on OpenHarness itself (the agent modifies its own code). Clone a real project as the workspace.
  2. Use real API calls — no mocks. Configure a real LLM endpoint.
  3. Multi-turn conversations — always test 2+ turns where the model needs prior context.
  4. Combine features — test hooks+skills+agent loop together, not in isolation.
  5. 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`.

Read the full file on GitHub · 194 lines

Files

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.

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. 2mo ago First seen · 194 lines · 69 tokens per session scan B c7e88107a6fd

Subscribe to this mod's changes

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.

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