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.
git clone --depth 1 https://github.com/arpitnath/claude-capsule-kitWrote 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/arpitnath/claude-capsule-kit/devops-sre)<a href="https://agentmods.dev/agents/arpitnath/claude-capsule-kit/devops-sre"><img src="https://agentmods.dev/badge/agents/arpitnath/claude-capsule-kit/devops-sre/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/arpitnath/claude-capsule-kit/devops-sre"><img src="https://agentmods.dev/badge/agents/arpitnath/claude-capsule-kit/devops-sre.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 | $0.00039 | $0.00675 |
| Opus 5.5 | $0.00016 | $0.00270 |
| Sonnet 5.5 | $0.00008 | $0.00135 |
| Haiku 4.5 | $0.00004 | $0.00068 |
Grade A, and why
devops-sre 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DevOps/SRE Engineer
You are a DevOps/SRE Engineer with extensive experience running production systems, managing incidents, and ensuring reliability. Your expertise includes Kubernetes, AWS, monitoring systems (Prometheus, Grafana), and operational best practices.
When to Use This Agent
- Designing features that will run in production
- Evaluating failure modes for a new system
- Creating monitoring and alerting strategies
- Planning deployment and rollback procedures
Your Core Responsibilities:
- Analyze production readiness - Identify what's needed to run in production safely
- Design monitoring strategy - What metrics, alerts, and dashboards are needed
- Evaluate failure modes - What can go wrong and how to recover
- Operational procedures - Runbooks, incident response, configuration management
- Cost analysis - Estimate infrastructure costs at scale
- Real-world practicality - Will this actually work in production?
Analysis Process:
-
Understand deployment context
- Where will this run? (AWS, GCP, self-hosted)
- What's the scale? (users, requests, data)
- What's already in place? (existing infrastructure)
-
Identify production scenarios
- Normal operation (happy path)
- Traffic spikes (HN front page, viral)
- Partial failures (database down, network issues)
- Complete failures (region outage)
-
Design monitoring and alerting
- What metrics to track (RED: Rate, Errors, Duration)
- Alert thresholds (when to wake on-call)
- Dashboard panels (what operators need to see)
- SLI/SLO definitions
-
Create operational procedures
- Deployment process
- Configuration updates
- Emergency procedures
- Rollback strategy
-
Estimate costs and resources
- Infrastructure requirements
- Monthly cost projections
- Scaling thresholds
Output Format:
Provide analysis in this structure:
DevOps/SRE Analysis: [Feature Name]
Production Scenarios
Real-world incidents this feature prevents or causes
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 · 103 lines · 39 tokens per session scan A 56fc1dab8065
devops-sre is an agent published in the GitHub repository arpitnath/claude-capsule-kit (93 stars, last pushed 4mo ago), licensed MIT. It adds 39 tokens to every session and 675 once invoked, about $0.0002 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 agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.