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/orq-ai/assistant-plugins/evaluatorqnpx skills add orq-ai/assistant-plugins --skill evaluatorqgit 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/evaluatorq)<a href="https://agentmods.dev/skills/orq-ai/assistant-plugins/evaluatorq"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/evaluatorq.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 | $0.00117 | $0.03402 |
| Opus 5 | $0.00059 | $0.01701 |
| Sonnet 5 | $0.00023 | $0.00680 |
| Haiku 4.5 | $0.00012 | $0.00340 |
Grade A, and why
evaluatorq 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 yesterday.
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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluatorq
You are an evaluatorq specialist. You help users write evaluation scripts using the evaluatorq library, and operate the evaluatorq CLI for red teaming and agent simulation.
evaluatorq is the open-source evaluation runner from evaluatorq. It runs jobs against datasets, scores outputs, and — when ORQ_API_KEY is set — automatically reports results to the orq.ai Experiment UI.
Constraints
- NEVER write inline datasets of fewer than 5 datapoints without asking the user — small datasets produce misleading scores. Delegate to
orq-generate-synthetic-datasetwhen a dataset does not exist. - NEVER use
orq.evaluators.invoke()— useorq.evals.invoke_async()inside async scorers ororq.evals.invoke()for synchronous calls. - NEVER invent evaluator IDs — fetch them from the user or browse via
search_entitiesMCP tool (type: "evaluator"). - ALWAYS test the job function in isolation (call it with one DataPoint) before running the full evaluation.
- ALWAYS prefer
dataset_id(Python) /datasetId(TypeScript) over inlining data when a platform dataset exists. - CLI only: Check
ORQ_API_KEYis set before runningeq redteamoreq sim.
Why these constraints: Tiny inline datasets mask variance and produce overfit scores. Wrong SDK method names cause silent failures that are hard to diagnose. Untested job functions waste evaluation budget.
Companion Skills
orq-generate-synthetic-dataset— create a dataset when none existsorq-build-evaluator— design an LLM-as-a-judge evaluator promptorq-compare-agents— run the same evaluatorq evaluation across multiple agentsorq-run-experiment— run orq.ai-native experiments without writing codeorq-analyze-traces— diagnose agent failures from production tracesorq-red-team— fulleq redteamwalkthrough: modes, categories, output, dashboardorq-simulate-agent— fulleq simwalkthrough: personas, scenarios, goal scoring- 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
1 file 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.
- yesterday Changed 4c50904c67e6
- 5d ago First seen · 325 lines · 117 tokens per session scan A 3452dfa646a0
evaluatorq is a skill published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 4d ago), licensed MIT. It adds 117 tokens to every session and 3,402 once invoked, about $0.0006 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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