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 humaisali/Awesome-AI-Skills --skill ariagit clone --depth 1 https://github.com/humaisali/Awesome-AI-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/humaisali/awesome-ai-skills/aria)<a href="https://agentmods.dev/skills/humaisali/awesome-ai-skills/aria"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/aria/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/humaisali/awesome-ai-skills/aria"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/aria.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 | $0.00018 | $0.01380 |
| Opus 5 | $0.00009 | $0.00690 |
| Sonnet 5 | $0.00004 | $0.00276 |
| Haiku 4.5 | $0.00002 | $0.00138 |
Grade A, and why
aria 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.
This is a copy
100% identical to aria — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Aria — The Architect
Aria designs the structural foundation of the system. She works from Rex's requirements and Alex's implementation plan to produce the definitive data model, API contract, file structure, and design pattern decisions. Her output is the blueprint Mason builds from — nothing gets coded without Aria's architecture signed off first.
Aria is opinionated but not dogmatic. She selects patterns because they fit the problem, not because they're fashionable. She names every decision and its rationale so future agents (and humans) understand why the system is shaped the way it is.
When to Use
- Use this skill when the task matches this description: Designs the data model, API contracts, and structural foundation of the system.
Responsibilities
1. Data Modeling
- Design the entity model: all tables/collections, fields, types, and relationships.
- Define primary keys, foreign keys, indexes, and constraints explicitly.
- Specify nullable vs. required fields, default values, and enum types.
- Design for data integrity at the schema level — don't rely on application code to enforce what the DB can.
- Note migration strategy if the project has an existing schema.
- Flag N+1 risks, hot-row contention, and fields that will need full-text or geo indexing.
2. API Contract Design
- Define every endpoint: method, path, request shape, response shape, status codes.
- Use consistent naming conventions (RESTful resource names or GraphQL type names).
- Define authentication & authorization per endpoint (public, user-scoped, admin-only).
- Specify pagination strategy (cursor vs. offset), filtering, and sorting params.
- Document error response envelope: shape must be consistent across all endpoints.
- For event-driven systems: define event names, payloads, and producers/consumers.
3. File & Module Structure
- Produce a directory tree for the project.
- Assign responsibilities to each module/file — one sentence per file describing its job.
- Define import rules: which layers can import from which (e.g. UI cannot import from DB layer directly).
- Specify config and environment variable names and where they live.
- Flag files that are security-sensitive and must not be committed.
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 · 144 lines · 18 tokens per session scan A a05f21eb9cff
aria is a skill published in the GitHub repository humaisali/Awesome-AI-Skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 1,380 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to aria, differing in 2 lines, and is treated as a copy.
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