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/quickstartgit 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 | $0.00023 | $0.01380 |
| Opus 5 | $0.00012 | $0.00690 |
| Sonnet 5 | $0.00005 | $0.00276 |
| Haiku 4.5 | $0.00002 | $0.00138 |
Grade A, and why
quickstart 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quickstart
Interactive onboarding guide for the orq.ai plugin. Walks the user through credential setup, connection verification, and a tour of all commands and skills.
Instructions
1. Welcome & Orientation
Greet the user and give a brief overview:
Welcome to the orq.ai plugin for Claude Code!
This plugin gives you a set of commands (quick actions) and skills (multi-step workflows) for building, evaluating, and improving LLM pipelines on the orq.ai platform.
The lifecycle: Build → Deploy → Monitor → Evaluate → Optimize
Then use AskUserQuestion to determine the user's setup state:
- "Brand new to orq.ai" → Go to Section 2
- "I have an API key but haven't set up the plugin" → Go to Section 3
- "I'm all set up" → Go to Section 4
2. Account & API Key Setup
Direct the user to create an account and generate an API key:
- Go to my.orq.ai and create an account (or sign in)
- Navigate to Settings → API Keys
- Click Create API Key and copy the key
Then continue to Section 3.
3. Environment Variable Setup
Security rule: Never ask the user to paste their API key in chat.
Tell the user:
To set your API key securely:
- Exit this session: type
/exit- Add the key to your shell profile so it persists across sessions:
- zsh (macOS default):
echo 'export ORQ_API_KEY=your-key-here' >> ~/.zshrc && source ~/.zshrc- bash:
echo 'export ORQ_API_KEY=your-key-here' >> ~/.bashrc && source ~/.bashrc- Restart Claude Code:
claude- Come back here:
/orq:quickstart
For brand-new users: Stop here after giving these instructions.
For returning users (who say they already have the key set): Verify with a non-leaking check:
if [ -z "$ORQ_API_KEY" ]; then echo "NOT_SET"; else echo "SET"; fi
If NOT_SET, repeat the setup instructions above. If SET, continue to Section 4.
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 · 118 lines · 23 tokens per session scan A ff517af38770
quickstart is a command published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 5d ago), licensed MIT. It adds 23 tokens to every session and 1,380 once invoked, about $0.0001 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.