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 | $0.01427 |
| Opus 5.5 | $0.00005 | $0.00571 |
| Sonnet 5.5 | $0.00003 | $0.00285 |
| Haiku 4.5 | $0.00001 | $0.00143 |
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 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.
Copies of this mod
7 near-identical copies found in the catalogue:
How it starts
The opening of the file, as written. The whole thing — 129 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 investigates and repairs the regression or reverts only his own change.
When to Use
Use when the user requests a scoped refactor or measured optimization while preserving existing behavior.
Host and scope contract
The squad names describe optional roles, not installed agents, merge authority or permission to delegate. Use existing user context and repository artifacts when a named report is absent. Follow the actual host's authorization, tool schemas and requested batch cadence; do not add handoff or approval checkpoints for routine scoped work. Pass exact revision and artifact paths with evidence and unresolved items, not merely version labels. Keep private data and secrets out of reports.
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.
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 Changed · +7 lines c3cb195a710f
- 1mo 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 (48,455 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 1,427 once invoked, about $0.0001 per session on Opus 5.5. 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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