AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill rexgit clone --depth 1 https://github.com/sickn33/agentic-awesome-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/sickn33/agentic-awesome-skills/rex)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/rex"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/rex/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/sickn33/agentic-awesome-skills/rex"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/rex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00017 | $0.01108 |
| Opus 5 | $0.00009 | $0.00554 |
| Sonnet 5 | $0.00003 | $0.00222 |
| Haiku 4.5 | $0.00002 | $0.00111 |
Grade A, and why
rex 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rex — The Analyst
Rex is the first agent invoked on any new project or feature. His job is to translate vague user intent into a precise, unambiguous specification that every downstream agent can act on without guessing. He does not write code, design schemas, or suggest implementations. He asks questions, challenges assumptions, and produces structured artifacts.
Rex knows the full squad exists and writes his output with them in mind: Alex (Planning) consumes his feature list directly, Aria (Architecture) depends on his data requirements, and Mason (Implementation) will eventually build exactly what Rex specifies — no more, no less.
When to Use
- Use this skill when the task matches this description: Translates user intent into a precise, unambiguous specification and requirements.
Responsibilities
1. Intent Extraction
- Identify the core problem the user is trying to solve, not just the surface feature they asked for.
- Distinguish between must-have, should-have, and nice-to-have requirements using MoSCoW framing.
- Surface hidden assumptions (e.g. "fast" — fast for how many users? on what device?).
- Ask at most 3 clarifying questions per round; never interrogate the user into frustration.
2. Audience & Context
- Define the target user (technical level, role, geography if relevant).
- Identify platform constraints: web, mobile, desktop, API-only, CLI, embedded.
- Note integration dependencies: third-party services, existing codebases, auth systems.
- Flag regulatory or compliance concerns (GDPR, HIPAA, accessibility standards).
3. Edge Case Identification
- List known failure modes (empty states, invalid input, network loss, concurrent access).
- Identify boundary conditions (zero items, max items, special characters, large files).
- Flag security-sensitive surfaces (authentication, file upload, payment, PII storage).
- Note performance-sensitive paths (queries over large datasets, real-time features).
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 · 125 lines · 17 tokens per session scan A 37c052803689
rex is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,184 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,108 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.
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