OpenHive is a runtime for groups of specialized AI agents that collaborate on long-running business processes. A persistent lead agent, called the Queen, creates and coordinates worker agents while the system manages state, recovery, observability, costs, and human oversight. The catalogue entries provide agent skills, instructions, and integrations for working with this harness.
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.
npx agentmods add skills/aden-hive/hive/test-reportingnpx skills add aden-hive/hive --skill test-reportinggit clone --depth 1 https://github.com/aden-hive/hiveWrote 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/aden-hive/hive/test-reporting)<a href="https://agentmods.dev/skills/aden-hive/hive/test-reporting"><img src="https://agentmods.dev/badge/skills/aden-hive/hive/test-reporting.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 | $0.00000 | $0.02556 |
| Opus 5 | $0.00000 | $0.01278 |
| Sonnet 5 | $0.00000 | $0.00511 |
| Haiku 4.5 | $0.00000 | $0.00256 |
Grade A, and why
test-reporting 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 5d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integration Test Reporting Skill
Run the Level 2 dummy agent integration test suite and produce a detailed HTML report with per-test input → outcome analysis.
Trigger
User wants to run integration tests and see results:
/test-reporting/test-reporting test_component_queen_live.py/test-reporting --all
SOP: Running Tests
Step 1: Select Scope
If the user provides a specific test file or pattern, use it. Otherwise run the full suite.
# Full suite
cd core && echo "1" | uv run python tests/dummy_agents/run_all.py --interactive 2>&1
# Specific file (requires manual provider setup)
cd core && uv run python -c "
import sys
sys.path.insert(0, '.')
from tests.dummy_agents.run_all import detect_available
from tests.dummy_agents.conftest import set_llm_selection
avail = detect_available()
claude = [p for p in avail if 'Claude Code' in p['name']]
if not claude:
avail_names = [p['name'] for p in avail]
raise RuntimeError(f'No Claude Code subscription. Available: {avail_names}')
provider = claude[0]
set_llm_selection(
model=provider['model'],
api_key=provider['api_key'],
extra_headers=provider.get('extra_headers'),
api_base=provider.get('api_base'),
)
import pytest
sys.exit(pytest.main([
'tests/dummy_agents/TEST_FILE_HERE',
'-v', '--override-ini=asyncio_mode=auto', '--no-header', '--tb=long',
'--log-cli-level=WARNING', '--junitxml=/tmp/hive_test_results.xml',
]))
"
Step 2: Collect Results
After the test run completes, collect:
- JUnit XML from
--junitxmloutput (if available) - stdout/stderr from the run
- Summary table from
run_all.pyoutput (the Unicode table)
Step 3: Generate HTML Report
Write the report to /tmp/hive_integration_test_report.html.
The report MUST include these sections:
Header
- Run timestamp (ISO 8601)
- Provider used (model name, source)
- Total tests / passed / failed / skipped
- Total wall-clock time
- Overall verdict: PASS (all green) or FAIL (with count)
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.
- 5d ago First seen · 226 lines · 0 tokens per session scan A 7fb636559c71
test-reporting is a skill published in the GitHub repository aden-hive/hive (11,016 stars, last pushed 15d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,556 tokens. 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
develop-web-game
Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with rendergametotext.
data-analysis
Analyze, explore, clean, and visualize datasets with statistical rigor. Use when user asks to analyze data, find patterns, compute statistics, create visualizations, clean messy data, or explore a dataset. Trigger when user says things like "analyze this data", "what trends do you see", "find patterns in", "create a…
experiment_management
Set up and manage the experiment folder structure. This is Phase 0 — it runs before any analysis begins. All bookkeeping files are JSON (never markdown).
summarization
Summarize documents, articles, conversations, code, and technical content into concise, accurate summaries. Use when user asks to summarize, condense, create a TL;DR, write an executive summary, extract key points, or distill content. Trigger when user says things like "summarize this", "give me the key points"…
evaluate
Compare baseline and new implementation results. Produce the machine-readable final report result.json, update experiments.json, and append a row to comparison.json.
progress
Maintain a machine-readable progress file so dashboards, CLIs, and notebooks can poll the experiment's state at any time. The file is a JSON document — never markdown, never human-prose-first.