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 Array-Ventures/coworker --skill mastragit clone --depth 1 https://github.com/Array-Ventures/coworkerWrote 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/array-ventures/coworker/mastra)<a href="https://agentmods.dev/skills/array-ventures/coworker/mastra"><img src="https://agentmods.dev/badge/skills/array-ventures/coworker/mastra/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/array-ventures/coworker/mastra"><img src="https://agentmods.dev/badge/skills/array-ventures/coworker/mastra.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 | $0.00083 | $0.01321 |
| Opus 5.5 | $0.00033 | $0.00528 |
| Sonnet 5.5 | $0.00017 | $0.00264 |
| Haiku 4.5 | $0.00008 | $0.00132 |
Grade A, and why
mastra 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 1mo 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mastra Framework Guide
Build AI applications with Mastra. This skill teaches you how to find current documentation and build agents and workflows.
⚠️ Critical: Do not trust internal knowledge
Everything you know about Mastra is likely outdated or wrong. Never rely on memory. Always verify against current documentation.
Your training data contains obsolete APIs, deprecated patterns, and incorrect usage. Mastra evolves rapidly - APIs change between versions, constructor signatures shift, and patterns get refactored.
Prerequisites
Before writing any Mastra code, check if packages are installed:
ls node_modules/@mastra/
- If packages exist: Use embedded docs first (most reliable)
- If no packages: Install first or use remote docs
Documentation lookup guide
Quick Reference
| User Question | First Check | How To |
|---|---|---|
| "Create/install Mastra project" | references/create-mastra.md |
Setup guide with CLI and manual steps |
| "How do I use Agent/Workflow/Tool?" | references/embedded-docs.md |
Look up in node_modules/@mastra/*/dist/docs/ |
| "How do I use X?" (no packages) | references/remote-docs.md |
Fetch from https://mastra.ai/llms.txt |
| "I'm getting an error..." | references/common-errors.md |
Common errors and solutions |
| "Upgrade from v0.x to v1.x" | references/migration-guide.md |
Version upgrade workflows |
Priority order for writing code
⚠️ Never write code without checking current docs first
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.
- 1mo ago First seen · 160 lines · 83 tokens per session scan A 6401fb1a6299
mastra is a skill published in the GitHub repository Array-Ventures/coworker (43 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,321 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
duckbug-knowledge
Answer questions about the owner's services and domain using the approved DuckBug knowledge base through configured MCP tools.
rag-knowledge-base
A local document search system that lets you add PDFs, Word files, Excel files, Markdown, and text, then ask questions about their contents. It finds relevant passages using both meaning-based and keyword search, then prepares context for an AI model.
document-qa
A document question-and-answer tool for PDFs, Word files, text, Markdown, CSV, and Excel files. It finds relevant passages in one file or a whole folder and uses a language model to answer questions.
aio-rag-kit
Set up vector database collections, index content, and run semantic search via RAG pipelines using Qdrant and rag-kit (auto-installs if missing). Use for retrieval-augmented generation, similarity search over a knowledge base, or embedding content for meaning-based retrieval.
AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
AgentDB Advanced Features
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.