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 danielrosehill/Claude-Open-Router-Model-Research-Plugin --skill or-evaluate-modelgit clone --depth 1 https://github.com/danielrosehill/Claude-Open-Router-Model-Research-PluginWrote 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/danielrosehill/claude-open-router-model-research-plugin/or-evaluate-model)<a href="https://agentmods.dev/skills/danielrosehill/claude-open-router-model-research-plugin/or-evaluate-model"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-open-router-model-research-plugin/or-evaluate-model/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/danielrosehill/claude-open-router-model-research-plugin/or-evaluate-model"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-open-router-model-research-plugin/or-evaluate-model.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 | $0.00096 | $0.00943 |
| Opus 5 | $0.00048 | $0.00472 |
| Sonnet 5 | $0.00019 | $0.00189 |
| Haiku 4.5 | $0.00010 | $0.00094 |
Grade A, and why
or-evaluate-model 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 10d 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.
curl -s https://openrouter.ai/api/v1/models -H "Accept: application/json" How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate an OpenRouter Model in Depth
Conduct a thorough evaluation of a single model the user is considering. Combine OpenRouter catalog data with external research — Hugging Face model card, original paper, license, benchmark coverage, community feedback — to give the user a confident go/no-go answer.
When to use
The user has shortlisted a model (often from or-recommend-model or or-compare-models) and wants to understand it deeply before committing — for a real workflow, a production deployment, or a comparison against incumbents.
Workflow
Step 1: Catalog snapshot
Fetch the OpenRouter catalog and extract the target model's full record:
curl -s https://openrouter.ai/api/v1/models -H "Accept: application/json"
Capture: id, context_length, modalities, pricing, supported_parameters, top_provider info, description, created date.
Step 2: External research
Go beyond the OR catalog. Use the available research tools (WebFetch, web search, Hugging Face MCP if available) to gather:
- Hugging Face model card — for open-weights models, fetch from
huggingface.co/<org>/<repo>. Look for: training data, training compute, licence, intended use, limitations, evaluation results. - Original paper — if the model has an arXiv paper, summarize key claims (architecture, training scale, headline benchmarks).
- Provider's own announcement / docs — for proprietary models (OpenAI, Anthropic, Google), pull from official pages.
- License — clearly state the licence and any commercial-use restrictions. This is especially important for Llama, Qwen, DeepSeek, Mistral families.
- Benchmark coverage — what public benchmarks has it been tested on? Headline scores on MMLU, HumanEval, GSM8K, SWE-bench, etc. — but only cite scores you can actually find, never from memory.
- Known limitations / failure modes — what is the model bad at? Reasoning depth, multilingual gaps, hallucination rates, refusal behavior?
- Community reception — recent discussion, reviews, or notable usage reports if findable.
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.
- 10d ago First seen · 95 lines · 96 tokens per session scan A b47178336df5
or-evaluate-model is a skill published in the GitHub repository danielrosehill/Claude-Open-Router-Model-Research-Plugin (2 stars, last pushed 4mo ago), licensed MIT. It adds 96 tokens to every session and 943 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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