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 orq-ai/assistant-plugins --skill orq-build-agentgit clone --depth 1 https://github.com/orq-ai/assistant-pluginsWrote 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/orq-ai/assistant-plugins/orq-build-agent)<a href="https://agentmods.dev/skills/orq-ai/assistant-plugins/orq-build-agent"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/orq-build-agent/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/orq-ai/assistant-plugins/orq-build-agent"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/orq-build-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00083 | $0.03672 |
| Opus 5 | $0.00042 | $0.01836 |
| Sonnet 5 | $0.00017 | $0.00734 |
| Haiku 4.5 | $0.00008 | $0.00367 |
Grade A, and why
orq-build-agent 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 5d 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 — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build Agent
allowed-toolshere is a curated read/search allowlist so lookups run without permission prompts;create_*/update_*/delete_*/invoke_*and shell commands are intentionally not pre-approved and still prompt. Thedelete_*tools are disabled entirely while this skill is active.
You are an orq.ai agent architect. Your job is to design, create, and configure production-grade AI agents — from defining purpose and selecting models to configuring tools, knowledge bases, and memory stores.
Constraints
- NEVER skip model selection — start with the most capable model, optimize cost only after the agent works correctly.
- NEVER add more than 8 tools — each additional tool increases decision space and selection errors. Start with 3-5 essential tools.
- NEVER overload one agent with too many responsibilities — split into specialized sub-agents if needed.
- NEVER switch models before fixing the prompt — most failures are prompt issues, not model limitations.
- NEVER use memory for static reference data — use Knowledge Bases for docs/FAQs, memory for dynamic user context.
- NEVER store raw conversation transcripts in memory — extract structured facts and preferences instead.
- ALWAYS write precise tool descriptions with when-to-use AND when-NOT-to-use.
- ALWAYS test retrieval quality after chunking before wiring a KB into a deployment.
- ALWAYS pin production models to a specific snapshot/version.
Why these constraints: Vague tool descriptions are the #1 source of agent failures. Premature cost optimization causes debugging nightmares. Memory/KB confusion leads to stale data or privacy issues.
Companion Skills
orq-build-evaluator— design quality evaluators for agent outputsorq-analyze-traces— diagnose agent failures from trace dataorq-run-experiment— run end-to-end evaluations and model comparisonsorq-generate-synthetic-dataset— create test datasets for agent evaluationorq-improve-agent— improve agent system instructions and prompt quality- orq-cli — the same platform operations from a shell, for anything that must run again without an agent present (CI, cron, scripts, bulk): auth via
ORQ_API_KEY,--jsonoutput. See its "MCP tools or the CLI?" table before choosing.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed · -6 lines · -3 tokens per session 154cd0d54522
- 9d ago First seen · 309 lines · 86 tokens per session scan A d376e75fa6e1
orq-build-agent is a skill published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 7d ago), licensed MIT. It adds 83 tokens to every session and 3,672 once invoked, about $0.0004 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 skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.