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 commands/orq-ai/assistant-plugins/modelsgit clone --depth 1 https://github.com/orq-ai/assistant-pluginsWhat 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 | $0.00009 | $0.01069 |
| Opus 5 | $0.00005 | $0.00535 |
| Sonnet 5 | $0.00002 | $0.00214 |
| Haiku 4.5 | $0.00001 | $0.00107 |
Grade A, and why
models 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 2d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Models
List the AI models available in the user's orq.ai workspace. Optionally filter by a search term.
Instructions
1. Parse arguments
$ARGUMENTS is optional. If provided, use it as a search term to filter models (e.g., "gpt-4", "claude", "embedding", "anthropic").
The search should be case-insensitive and match against model name, provider, or capabilities.
If the search term matches a model type (e.g., "chat", "embedding", "image", "tts", "stt", "rerank", "ocr"), use it as the modelType parameter directly.
2. Fetch data
Use the list_models MCP tool to retrieve available models. Important: The modelType parameter is always required — never call list_models without it.
- If a model type was identified from the search term, pass it as
modelType. - Otherwise, fetch the default types by calling
list_modelsthree times in parallel with:modelType: "chat",modelType: "completion", andmodelType: "embedding".
3. Display models
Present a clean summary using native markdown formatting (bold, headers, horizontal rules) — not inside a code block. This renders well in Claude Code's monospace terminal.
Output the models in this format:
# Orq.ai AI Router — Active Models
**12** chat · **3** embedding · **2** image
Models enabled in your workspace. Add providers or enable more models at **[AI Router → Models](https://my.orq.ai/)**.
---
### OpenAI (6)
- **gpt-5** — chat · 1M context
- **gpt-5-mini** — chat · 1M context
- **gpt-4.1** — chat · 1M context
- **text-embedding-3-large** — embedding · 3072 dims
- **text-embedding-3-small** — embedding · 1536 dims
- ... and 1 more
### Anthropic (3)
- **claude-sonnet-4-20250514** — chat · 200k context
- **claude-haiku-4-5-20251001** — chat · 200k context
- **claude-opus-4-1-20250805** — chat · 200k context
### Google (2)
- **gemini-2.5-pro** — chat · 1M context
- **gemini-2.5-flash** — chat · 1M context
If a search term was provided, filter and show only matching models:
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.
- 2d ago First seen · 102 lines · 9 tokens per session scan A 93de0aad7764
models is a command published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 5d ago), licensed MIT. It adds 9 tokens to every session and 1,069 once invoked, about $0.0000 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.