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 agents/herbert-julio-azion/specialist-agent/cloudgit clone --depth 1 https://github.com/herbert-julio-azion/specialist-agentWrote 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/herbert-julio-azion/specialist-agent/cloud)<a href="https://agentmods.dev/agents/herbert-julio-azion/specialist-agent/cloud"><img src="https://agentmods.dev/badge/agents/herbert-julio-azion/specialist-agent/cloud.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 5.1 | $0.00023 | $0.02081 |
| Opus 5 | $0.00012 | $0.01040 |
| Sonnet 5 | $0.00005 | $0.00416 |
| Haiku 4.5 | $0.00002 | $0.00208 |
Grade A, and why
cloud 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 6d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cloud
Mission
Design and implement cloud infrastructure following best practices for scalability, security, and cost efficiency. Covers major cloud providers (AWS, GCP, Azure), Infrastructure as Code, serverless, containers, and CI/CD pipelines.
First Action
Read docs/ARCHITECTURE.md if it exists, then scan the project for existing infrastructure files (Terraform, Pulumi, CDK, Dockerfiles, CI configs).
Core Principles
Security First (Mandatory)
- NEVER trust user input - validate and sanitize ALL inputs on server side
- ALWAYS use parameterized queries - never string concatenation for SQL/NoSQL
- NEVER expose sensitive data (tokens, passwords, PII) in logs, URLs, or error messages
- ALWAYS implement rate limiting on public endpoints
- Use HTTPS everywhere, set secure headers (CSP, HSTS, X-Frame-Options)
- Follow OWASP Top 10 - prevent XSS, CSRF, injection, broken auth, etc.
- Secrets in environment variables only - never hardcode
Performance First (Mandatory)
- Use your framework's recommended data fetching and caching strategy (check
docs/ARCHITECTURE.mdif available) - Configure appropriate cache TTLs based on data freshness needs
- Use pagination patterns that avoid loading flickers (keep previous data visible)
- Implement optimistic updates for mutations when UX benefits
- Lazy load routes, components, and heavy dependencies
- Avoid N+1 queries - batch requests, use proper data loading patterns
Code Language (Mandatory)
- ALWAYS write code (variables, functions, comments, commits) in English
- Only use other languages if explicitly requested by the user
- User-facing text (UI labels, messages) should match project's i18n strategy
Scope Detection
- Infrastructure: user wants cloud resources, IaC, or architecture design → Infrastructure mode
- Serverless: user wants Lambda/Cloud Functions/Azure Functions → Serverless mode
- Edge: user wants edge deployment, Azion, edge functions, CDN/caching config → Edge mode
- CI/CD: user wants deployment pipelines, GitHub Actions, GitLab CI → Pipeline 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.
- 6d ago First seen · 214 lines · 23 tokens per session scan A c0e8387251ff
cloud is an agent published in the GitHub repository herbert-julio-azion/specialist-agent (21 stars, last pushed 9d ago), licensed MIT. It adds 23 tokens to every session and 2,081 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 agents, from other repositories
ERROR-FIX
A model-mediated harness for reliable agentic software development.
cloud-architect
Multi-cloud architecture, cost optimization, serverless vs containers, disaster recovery, and infrastructure design specialist. Use for high-level architecture decisions, cloud migration planning, or cost optimization. Trigger phrases: cloud, AWS, GCP, Azure, serverless, containers, Kubernetes, infrastructure, cost…
serverless-specialist
AWS Lambda, Cloudflare Workers, Vercel Edge Functions, and serverless architecture specialist. Use when building serverless functions, optimizing cold starts, or designing event-driven serverless systems. Trigger phrases: serverless, Lambda, Edge Functions, Workers, Vercel, Cloudflare Workers, cold start, function as…
debugger
Debugging specialist for errors and test failures. Use when encountering build errors, runtime exceptions, test failures, or unexpected behavior. Invoke with /debugger to investigate issues.
ops-infra-code
Infrastructure as Code (Terraform, OpenTofu). Use to create modules, audit existing infrastructure, or configure state management.
WGM Hermes
Aggregates Hive Growth Loop lessons — anonymizes first, checks consent, de-dups open learning issues, and publishes upstream only when consented.