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-recommend-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-recommend-model)<a href="https://agentmods.dev/skills/danielrosehill/claude-open-router-model-research-plugin/or-recommend-model"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-open-router-model-research-plugin/or-recommend-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-recommend-model"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-open-router-model-research-plugin/or-recommend-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 | $0.00093 | $0.00868 |
| Opus 5 | $0.00046 | $0.00434 |
| Sonnet 5 | $0.00019 | $0.00174 |
| Haiku 4.5 | $0.00009 | $0.00087 |
Grade A, and why
or-recommend-model 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 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.
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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recommend an OpenRouter Model
Interactive recommendation flow: clarify the user's requirements, query the catalog, propose 2–3 ranked picks with rationale.
When to use
The user wants guidance on choosing a model and has not given enough detail to filter directly. The task is open-ended ("help me pick" / "which model should I use for X").
Workflow
Step 1: Gather requirements
Ask the user a short set of clarifying questions — only the ones not already answered. Keep the round-trip tight:
- Use case — what's the workload? (chat, agentic tool use, document parsing, code generation, summarization, vision, audio, structured extraction, etc.)
- Modalities — text only? Need image input? Audio input? Image generation?
- Budget — soft ceiling on $/1M tokens (prompt and completion), or "cheapest possible", or "quality matters more than cost"?
- Context window — minimum context required? (typical buckets: 8K, 32K, 128K, 200K+, 1M+)
- Throughput / latency — does response speed matter? Any provider preference?
- Tool use / structured output — needs function calling? JSON mode? Strict structured outputs?
- Open-source preference — does the user want only open-weights models (DeepSeek, Llama, Qwen, Mistral) or are proprietary models (OpenAI, Anthropic, Google) fine?
Don't ask all seven if the user has already answered some. If you have enough to make a reasonable shortlist after 2–3 questions, proceed.
Step 2: Filter the catalog
Fetch https://openrouter.ai/api/v1/models and apply filters:
- Modality requirements →
architecture.input_modalities - Tool/structured output →
supported_parametersincludestools/structured_outputs - Context →
context_length >= user_minimum - Budget →
pricing.promptandpricing.completionwithin ceiling - Open-weights only → filter out
openai/,anthropic/,google/(proprietary), keepmeta-llama/,deepseek/,qwen/,mistralai/, etc.
Step 3: Rank and present
Score each candidate against the user's stated priorities (cost vs. capability vs. context). Present 2–3 recommendations, not a long list:
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 · 65 lines · 93 tokens per session scan A f5a6c9fe4d42
or-recommend-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 93 tokens to every session and 868 once invoked, about $0.0005 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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