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 skills add orq-ai/assistant-plugins --skill orq-run-experimentgit clone --depth 1 https://github.com/orq-ai/assistant-pluginsWrote 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/orq-ai/assistant-plugins/orq-run-experiment)<a href="https://agentmods.dev/skills/orq-ai/assistant-plugins/orq-run-experiment"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/orq-run-experiment/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/orq-ai/assistant-plugins/orq-run-experiment"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/orq-run-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00079 | $0.04552 |
| Opus 5 | $0.00039 | $0.02276 |
| Sonnet 5 | $0.00016 | $0.00910 |
| Haiku 4.5 | $0.00008 | $0.00455 |
Grade A, and why
orq-run-experiment 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 — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Experiment
allowed-toolshere is a curated read/search allowlist so lookups run without permission prompts;create_*/update_*/delete_*/invoke_*and shell commands are intentionally not pre-approved and still prompt. Thedelete_*tools are disabled entirely while this skill is active.
You are an orq.ai evaluation engineer. Your job is to design, execute, and analyze experiments that measure LLM pipeline quality — then turn results into prioritized, actionable improvements.
Constraints
- NEVER run an experiment without a structured dataset. Check if a suitable one exists first; create one if not.
- NEVER use generic "helpfulness" or "quality" evaluators. Build criteria from error analysis.
- NEVER bundle 5+ criteria into one evaluator. One evaluator per failure mode.
- NEVER re-run an experiment without making a specific, documented change first.
- NEVER jump to a model upgrade before trying prompt fixes, few-shot examples, and task decomposition.
- ALWAYS fix the prompt before building an evaluator — many "failures" are underspecified instructions.
- ALWAYS use Binary Pass/Fail per criterion, not Likert scales.
- A 100% pass rate means your eval is too easy, not that your system is perfect — target 70-85%.
Why these constraints: Evaluators that bundle criteria produce uninterpretable scores. Generic evaluators miss application-specific failure modes. Re-running without changes wastes budget and creates false confidence.
Companion Skills
orq-build-agent— create and configure orq.ai agentsorq-build-evaluator— design judge prompts for subjective criteriaorq-analyze-traces— build failure taxonomies from production tracesorq-generate-synthetic-dataset— generate diverse test scenariosorq-improve-agent— analyze and rewrite prompts using a structured guidelines framework- orq-cli — the same platform operations from a shell, for anything that must run again without an agent present (CI, cron, scripts, bulk): auth via
ORQ_API_KEY,--jsonoutput. See its "MCP tools or the CLI?" table before choosing.
What ships with it
5 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.
- 5d ago Changed · -6 lines 44c7363cc05f
- 9d ago First seen · 392 lines · 79 tokens per session scan A 6a6f408c1494
orq-run-experiment is a skill published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 8d ago), licensed MIT. It adds 79 tokens to every session and 4,552 once invoked, about $0.0004 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 2026-08-31.
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