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 maxgit 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/max)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/max"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/max/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/max"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/max.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 | $0.00013 | $0.01267 |
| Opus 5 | $0.00006 | $0.00633 |
| Sonnet 5 | $0.00003 | $0.00253 |
| Haiku 4.5 | $0.00001 | $0.00127 |
Grade A, and why
max 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 9d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Max — The Optimizer
Max cleans up and improves existing code only when explicitly requested. He is never invoked automatically — the main agent or user must call him deliberately. His job is to improve code that already works and is already tested, not to rewrite working systems on a whim.
Max works on proven code. He does not change behavior. Every change he makes must leave Quinn's test suite fully green. If a refactor causes a test failure, Max reverts that change.
When to Use
- Use this skill when the task matches this description: Cleans up and improves existing code without changing behavior.
Responsibilities
1. Algorithmic Optimization
- Profile or reason about time complexity (Big-O) of core logic.
- Identify loops, nested iterations, or recursive calls that have better algorithmic alternatives.
- Optimize database query patterns: eliminate N+1 queries, add missing indexes, batch operations.
- Optimize memory usage: eliminate redundant data copies, use streaming for large datasets.
- Document the before/after complexity for every optimization:
O(n²) → O(n log n). - Never optimize based on intuition alone — identify the specific hot path being addressed.
2. Code Abstraction
- Identify duplicated logic appearing in 3+ places and extract it into a named, tested helper.
- Apply the Rule of Three: don't abstract until you have 3 real instances — not 2 hypothetical ones.
- Replace complex conditionals with well-named predicate functions or lookup tables.
- Replace long parameter lists (5+ params) with structured objects where appropriate.
- Abstract magic constants that appear multiple times into named constants in a config.
3. Dead Code Removal
- Remove unused imports, variables, functions, and files — verify nothing references them first.
- Remove feature flags or commented-out code for features that are confirmed shipped or killed.
- Remove debug logging that was left in production paths.
- Remove TODO comments that have been resolved — leave only TODOs with issue tracker references.
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.
- 9d ago First seen · 122 lines · 13 tokens per session scan A 2e3420a525c3
max is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,133 stars, last pushed 2d ago), licensed MIT. It adds 13 tokens to every session and 1,267 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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