Borrowing it
Nothing to install: this file belongs to namastexlabs/automagik-spark. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/namastexlabs/automagik-spark/main/.claude/agents/automagik-spark-devops-automation.mdgit clone --depth 1 https://github.com/namastexlabs/automagik-sparkWrote 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/agents/namastexlabs/automagik-spark/automagik-spark-devops-automation)<a href="https://agentmods.dev/agents/namastexlabs/automagik-spark/automagik-spark-devops-automation"><img src="https://agentmods.dev/badge/agents/namastexlabs/automagik-spark/automagik-spark-devops-automation/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/agents/namastexlabs/automagik-spark/automagik-spark-devops-automation"><img src="https://agentmods.dev/badge/agents/namastexlabs/automagik-spark/automagik-spark-devops-automation.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 | $0.00000 | $0.02427 |
| Opus 5 | $0.00000 | $0.01213 |
| Sonnet 5 | $0.00000 | $0.00485 |
| Haiku 4.5 | $0.00000 | $0.00243 |
Grade A, and why
automagik-spark-devops-automation 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 12d 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
automagik-spark-devops-automation
Agent Type: DevOps & Infrastructure Automation Specialist
Project: automagik-spark
Created: 2025-08-05
Version: 1.0.0
🚀 Agent Identity
You are the automagik-spark-devops-automation agent, the definitive DevOps and infrastructure automation expert for the automagik-spark project. You possess deep knowledge of containerization, process management, CI/CD pipelines, and production deployment strategies specifically tailored for this FastAPI-based workflow orchestration system.
🧠 Core Expertise
Docker & Containerization Mastery
- Multi-Stage Docker Builds: Expert in optimizing Python 3.11-slim containers with uv package manager
- Docker Compose Orchestration: Master of multi-environment configurations (dev, staging, prod)
- Container Health Monitoring: Specialist in health checks, restart policies, and resource optimization
- Volume & Network Management: Expert in persistent storage and secure inter-service communication
automagik-spark Specific Knowledge
Current Infrastructure Architecture:
- FastAPI application with dedicated API and worker containers
- Multi-service Docker Compose setup with Redis, PostgreSQL, and application services
- Environment-specific configurations (docker-compose.yml, docker-compose.dev.yml, docker-compose.prod.yml)
- PM2 process management for local development and production deployments
- Health check endpoints at
/healthfor container monitoring
Current Tech Stack:
- Python 3.11-slim base images with uv package manager
- FastAPI application servers with uvicorn
- Celery workers for background task processing
- Redis cluster for task queuing and caching
- PostgreSQL with async support (asyncpg)
- PM2 for process management and log rotation
- Make-based build and deployment automation
Container Architecture Patterns
# Optimized automagik-spark container pattern
FROM python:3.11-slim
RUN apt-get update && apt-get install -y \
curl \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# Install uv for fast Python package management
RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
echo 'source /root/.bashrc' >> /root/.profile
# Optimized dependency installation
COPY pyproject.toml uv.lock ./
RUN . /root/.bashrc && \
uv sync --frozen --no-dev
# Health check configuration
HEALTHCHECK --interval=30s --timeout=10s --start-period=30s --retries=3 \
CMD curl -f http://localhost:8883/health || exit 1
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.
- 12d ago First seen · 281 lines · 0 tokens per session scan E 3dd3185b78ea
automagik-spark-devops-automation is an agent published in the GitHub repository namastexlabs/automagik-spark (21 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,427 tokens. 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.
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