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 selvarajmurugesan90/ops-engineering-skills --skill complete-ai-agent-stack-deployment-cloud-managed-from-scratchgit clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-skillsWrote 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/selvarajmurugesan90/ops-engineering-skills/complete-ai-agent-stack-deployment-cloud-managed-from-scratch)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/complete-ai-agent-stack-deployment-cloud-managed-from-scratch"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/complete-ai-agent-stack-deployment-cloud-managed-from-scratch/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/selvarajmurugesan90/ops-engineering-skills/complete-ai-agent-stack-deployment-cloud-managed-from-scratch"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/complete-ai-agent-stack-deployment-cloud-managed-from-scratch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 69 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 310 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 70 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 310 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 72 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 310 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00206 | $0.04088 |
| Opus 5 | $0.00103 | $0.02044 |
| Sonnet 5 | $0.00041 | $0.00818 |
| Haiku 4.5 | $0.00021 | $0.00409 |
Grade A, and why
complete-ai-agent-stack-deployment-cloud-managed-from-scratch 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 10d 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Complete AI Agent Stack Deployment (Cloud-Managed) From Scratch
Purpose
A production AI agent is not one component — it's a chain of dependent layers (account guardrails, an agent control loop, LLM provider access, a knowledge-retrieval layer, tool access, a safety net, and cost/latency visibility) that only work as a coherent, safe system if wired up in the right order. Skip or mis-sequence a layer and the failure shows up somewhere confusing: an agent architecture designed before an LLM gateway exists hardcodes a single provider's SDK directly into agent code, making the later "add a fallback provider" phase a rewrite instead of a config change; or an MCP server is connected to a live agent before an evaluation harness exists, so the first prompt-injection-via-tool-output regression is discovered by a user, not a test suite. This skill sequences the cloud-managed version of that whole path — landing zone through cost monitoring — deliberately using vendor-neutral language for the LLM provider and vector database layers, since the managed-service choice at each of those layers is a real decision this skill defers to the reader rather than assumes.
When to use
- Standing up a new production AI agent for a team or organization that wants to build on managed cloud services (LLM provider APIs, a managed vector database, cloud landing-zone guardrails) rather than self-hosting the model-serving and vector-database layers.
- Deciding the right order to introduce an LLM gateway, RAG/vector search, MCP tool access, an evaluation harness, and cost monitoring into an agent that currently has none of them.
- Auditing an existing agent deployment for a skipped or out-of-order phase (e.g. MCP tools connected before an evaluation harness existed, or cost monitoring added only after a surprising invoice).
- Rebuilding a reference agent platform (a second product line, a second team) that should follow the same proven sequence as a known-good first deployment.
- Explaining to a team the full dependency chain for a cloud-managed agent stack build-out, phase by phase.
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.
- 10d ago First seen · 318 lines · 206 tokens per session scan A c74d9d51739f
complete-ai-agent-stack-deployment-cloud-managed-from-scratch is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (38 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 206 tokens to every session and 4,088 once invoked, about $0.0010 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.
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