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/workspacegit 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 | $0.00022 | $0.01205 |
| Opus 5 | $0.00011 | $0.00602 |
| Sonnet 5 | $0.00004 | $0.00241 |
| Haiku 4.5 | $0.00002 | $0.00120 |
Grade A, and why
workspace 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workspace
Show a quick overview of the user's orq.ai workspace — agents, deployments, prompts, datasets, experiments, projects, and knowledge bases. Optionally filter to a specific section.
Instructions
1. Parse arguments
$ARGUMENTS is optional. If provided, it narrows the output to a specific section:
agents— show only agentsdeployments— show only deploymentsprompts— show only promptsdatasets— show only datasetsexperiments— show only experimentsprojects— show only projectsknowledge— show only knowledge basesevaluator— show only evaluators
If empty, show all sections.
2. Fetch data
Use the search_entities MCP tool and get_analytics_overview MCP tool to fetch data. Run all applicable calls in parallel.
- Analytics overview:
get_analytics_overview— fetches request volume, cost, error rate for the summary line - Agents:
search_entitieswithtype: "agent" - Deployments:
search_entitieswithtype: "deployment" - Prompts:
search_entitieswithtype: "prompt" - Datasets:
search_entitieswithtype: "dataset" - Experiments:
search_entitieswithtype: "experiment" - Projects:
search_entitieswithtype: "project" - Knowledge:
search_entitieswithtype: "knowledge" - Evaluator:
search_entitieswithtype: "evaluator"
Fetch only the sections needed based on arguments. Always fetch analytics overview regardless of section filter.
3. Display the overview
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 overview in this format:
# Orq.ai Workspace — Overview
**3** agents · **5** deployments · **8** prompts · **2** datasets · **1** experiment · **2** projects · **2** knowledge bases
**Last 24h:** 12,450 requests · $8.42 cost · 0.03% error rate
Manage your workspace at **[Workspace → my.orq.ai](https://my.orq.ai/)**.
---
### Projects (2)
- **customer-support** — 3 agents, 5 prompts
- **internal-tools** — 1 agent, 2 prompts
### Agents (3)
- **customer-support-bot** — gpt-5-mini · 2 tools · 1 KB
- **onboarding-agent** — claude-sonnet · 3 tools
- **internal-qa** — gpt-4.1-mini
### Deployments (5)
- **summarizer** v2.1 — active
- **classifier** v1.0 — active
- ... and 3 more
### Prompts (8)
- **support-response** — 4 versions, latest: v4
- **extract-entities** — 2 versions, latest: v2
- ... and 6 more
### Datasets (2)
- **eval-customer-queries** — 150 datapoints
- **edge-cases** — 42 datapoints
### Experiments (1)
- **prompt-comparison-mar** — completed · 3 evaluators
### Knowledge Bases (2)
- **product-docs** — 120 documents
- **faq-database** — 45 documents
### Evaluators (2)
- **coherence** — active
- **toxicity** — active
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 · 122 lines · 22 tokens per session scan A 3030f4084b86
workspace is a command published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 6d ago), licensed MIT. It adds 22 tokens to every session and 1,205 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
git
Git operations with intelligent commit messages and workflow optimization.
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.