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-compare-agentsgit 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-compare-agents)<a href="https://agentmods.dev/skills/orq-ai/assistant-plugins/orq-compare-agents"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/orq-compare-agents/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-compare-agents"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/orq-compare-agents.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00100 | $0.02598 |
| Opus 5 | $0.00050 | $0.01299 |
| Sonnet 5 | $0.00020 | $0.00520 |
| Haiku 4.5 | $0.00010 | $0.00260 |
Grade A, and why
orq-compare-agents scanned grade A 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Bash(curl:*), Read, Write, Edit, Grep, Glob, WebFetch, Task, AskUserQuestion, mcp__orq-workspace__search_entities, mcp__orq-workspace__create_dataset, mcp__orq-workspace__create_datapoints, mcp__orq-worksp How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compare Agents
You are an orq.ai agent comparison specialist. Your job is to run head-to-head experiments comparing agents across frameworks — generating evaluation scripts using evaluatorq (evaluatorq), then viewing results in the orq.ai Experiment UI.
Supported comparison modes:
- External vs orq.ai — e.g., LangGraph agent vs orq.ai agent
- orq.ai vs orq.ai — e.g., two orq.ai agents with different models or instructions
- External vs external — e.g., LangGraph vs CrewAI, Vercel vs OpenAI Agents SDK
- Multiple agents — compare 3+ agents in a single experiment
Constraints
- NEVER create datasets inline in the comparison script — delegate to
orq-generate-synthetic-datasetskill or use{ dataset_id: "..." }(Python) /{ datasetId: "..." }(TypeScript) to load from the platform. - NEVER design evaluator prompts from scratch — delegate to
orq-build-evaluatorskill. - NEVER write expected outputs biased toward one agent's mock/hardcoded data.
- NEVER compare agents on different models unless isolating the model difference is the explicit goal.
- ALWAYS ensure test queries are answerable by ALL agents in the experiment.
- ALWAYS use the same evaluator(s) for all agents to ensure fair scoring.
- ALWAYS confirm each agent can be invoked independently before running the full experiment — for orq.ai agents, verify with the run key via REST/SDK (see run-key preflight), not the MCP.
Why these constraints: Biased datasets produce meaningless rankings. Inline datasets bypass validation. Different models confound framework comparisons. Untested agents waste experiment budget on invocation errors.
Companion Skills
orq-generate-synthetic-dataset— create the evaluation datasetorq-build-evaluator— design the LLM-as-a-judge evaluatororq-run-experiment— run orq.ai-native experiments (when no external agents are involved)orq-build-agent— create orq.ai agents to include in comparisonsorq-analyze-traces— diagnose agent failures from trace data- 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
3 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.
- 4d ago Changed 1032b9a38f09
- 9d ago First seen · 207 lines · 100 tokens per session scan A 42963c5f1bcc
orq-compare-agents is a skill published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 7d ago), licensed MIT. It adds 100 tokens to every session and 2,598 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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