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 skills/blocunited-llc/mozaiks/setupnpx skills add BlocUnited-LLC/mozaiks --skill setupgit clone --depth 1 https://github.com/BlocUnited-LLC/mozaiksWrote 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/blocunited-llc/mozaiks/setup)<a href="https://agentmods.dev/skills/blocunited-llc/mozaiks/setup"><img src="https://agentmods.dev/badge/skills/blocunited-llc/mozaiks/setup.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.00834 |
| Opus 5 | $0.00013 | $0.00417 |
| Sonnet 5 | $0.00005 | $0.00167 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
setup 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 4d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Help the user set up Mozaiks from a fresh clone.
Tier System
Mozaiks supports four development tiers:
| Tier | Use Case | What's Included |
|---|---|---|
| engine | Headless AI API | Workflows only, no UI |
| chat | Chatbot builders | Workflows + chat UI |
| integrated | SaaS builders | + modules + event bus + auth |
| full | Product builders | + admin portal + full management surfaces |
For more info on tiers: /init-project or /add-feature skills.
Prerequisites to Check
Run these commands and verify versions:
- Docker 24+ and Compose v2+
- Python 3.11+
- Node 18+, npm 9+
If anything is missing, help them install it first.
Canonical Local Contributor Path
For this repo, the default local contributor experience is:
- Studio host backend on
http://localhost:8000 web_shell/frontend onhttp://localhost:3000factory_app/appas the first-party builder/reference app bundle when no external app workspace is selected
Setup Steps
1. Environment Variables
# Copy .env.example to .env
cp .env.example .env # or Copy-Item on Windows
Set OPENAI_API_KEY=sk-... in .env. If $ARGUMENTS contains an API key, use it.
2. Python + Repo Dependencies
python -m venv .venv
# Activate: .\.venv\Scripts\Activate.ps1 (Windows) or source .venv/bin/activate (Unix)
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
3. Frontend Dependencies
npm --prefix web_shell install
4. Start the Default Local Dev Stack
Preferred path in this repo:
.\scripts\run-studio.ps1
That starts the Studio host backend and the web_shell frontend together.
5. Verify
- Open
http://localhost:3000/apps - Check backend health at
http://localhost:8000/health - Run
.\scripts\smoke-studio-local.ps1for the full local Studio smoke - Confirm the frontend is loading the first-party builder/reference app bundle
from
factory_app/app
6. Optional Split Mode
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.
- 4d ago First seen · 111 lines · 25 tokens per session scan A 4b3636eeba92
setup is a skill published in the GitHub repository BlocUnited-LLC/mozaiks (25 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 834 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 2026-08-30.
Other skills, from other repositories
add-new-model
Add support for a newly-released LLM model in pydantic-ai (e.g. openai:gpt-5.6, anthropic:claude-sonnet-5). Use when a provider ships a new model id and you need to wire literals, profile flags, and tests to recognize it. Handles SDK-lag, gateway list conventions, and capability probing.
building-pydantic-ai-agents
Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydanticai, or asks to build an AI agent, add tools/capabilities, defer capability loading, stream output, define agents…
complete-partial-pr
Evaluate and complete an issue or PR where the submitted patch fixes only a narrow symptom of the reported pain point. Use when a contribution may miss adjacent integration surfaces, provider/spec semantics, roundtrip behavior, tests, docs, or historical maintainer decisions.
testing-skill
Record, rewrite, and debug VCR cassettes for HTTP recordings. Use when running tests with --record-mode, verifying cassette playback, or inspecting request/response bodies in YAML cassettes.
pre-push-review
Run a high-judgment local review of the current branch before pushing, both before a PR exists and between PR iterations.
adding-a-provider-api-feature
Add a new provider API capability (prompt caching, strict/structured tool calling, thinking/reasoning effort, service tier, safety settings, logprobs, etc.) to Pydantic AI. Use when wiring a provider feature through the library — it enforces reasoning from the existing cross-provider abstraction before designing…