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 manu14357/zskills --skill azure-storagegit clone --depth 1 https://github.com/manu14357/zskillsWrote 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/manu14357/zskills/azure-storage)<a href="https://agentmods.dev/skills/manu14357/zskills/azure-storage"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/azure-storage/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/manu14357/zskills/azure-storage"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/azure-storage.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 | $0.00055 | $0.02769 |
| Opus 5 | $0.00028 | $0.01385 |
| Sonnet 5 | $0.00011 | $0.00554 |
| Haiku 4.5 | $0.00006 | $0.00277 |
Grade A, and why
azure-storage 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 11d 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 — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Storage
Select and configure Azure storage with optimal redundancy, performance tiering, and access controls. Balance cost, durability, latency, and compliance requirements.
Use This Skill When
- The user needs file, object, or queue storage in Azure
- The user needs lifecycle, replication, or retention policy design
- The user needs secure access model for storage workloads
- The user wants to optimize storage costs while maintaining SLA
Context: Storage Maturity
Immature: Hot tier only, public access, no lifecycle, account key auth
Developing: Correct tiering, RBAC, some lifecycle rules
Managed: LRS for dev, GRS for prod, managed identity auth, lifecycle to cool/archive → Target
Optimized: Geo-redundant reads, tiered access based on traffic, real-time cost optimization
Required Inputs
- Data type: Documents, logs, video, database backups, transactional
- Access pattern: Frequent (hot), occasional (cool), rare (archive), cold
- Size & growth: 10GB initial, 10% monthly growth? Or 1TB spike?
- Latency requirement: <100ms or batch-oriented?
- Durability: RPO/RTO targets? Geographic redundancy needed?
- Access model: Public, private, user-scoped, application-only?
- Compliance: Data residency, encryption, retention, audit requirements?
- Budget: Cost-driven or performance-driven?
Decision Tree
What type of data needs storage?
├─ Unstructured files (docs, images, video) → Blob Storage
├─ Shared file system (SMB/NFS for apps) → Azure Files
├─ Message queue → Queue Storage
├─ NoSQL key-value → Table Storage
├─ Big data analytics → Data Lake Storage
└─ Structured relational → SQL Database (not storage)
How frequently is this data accessed?
├─ Daily (hot, <1 day SLA) → Hot tier ($0.012/GB/month)
├─ Weekly-monthly (cool, <30 day SLA) → Cool tier ($0.001/GB/month)
├─ Rarely, compliance hold (archive, <90 day SLA) → Archive tier ($0.0004/GB/month)
└─ Cold (7+ year retention) → Cold tier ($0.00099/GB/month)
Do you need geographic failover?
├─ Single region (dev/test) → LRS (locally redundant, cheapest, 11x durability)
├─ Single region + replication → ZRS (zone redundant, same-region HA)
├─ Multi-region failover → GRS (geo-redundant, read from secondary)
└─ Multi-region read (active-active) → RA-GRS (read-access geo-redundant)
How should apps access storage?
├─ Internal/private → Private endpoints, firewall rules
├─ Managed identity → System/user-assigned identity with role
├─ Public but controlled → SAS tokens (time-bound, scoped)
└─ Public read-only → Blob public access (for content CDN)
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.
- 11d ago First seen · 333 lines · 55 tokens per session scan A 39da5d1f84f5
azure-storage is a skill published in the GitHub repository manu14357/zskills (16 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 2,769 once invoked, about $0.0003 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…