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 ai-analyzergit 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/ai-analyzer)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-analyzer"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-analyzer/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/ai-analyzer"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-analyzer.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 | $0.00046 | $0.02729 |
| Opus 5.5 | $0.00018 | $0.01092 |
| Sonnet 5.5 | $0.00009 | $0.00546 |
| Haiku 4.5 | $0.00005 | $0.00273 |
Grade A, and why
ai-analyzer 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 4d 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
8 near-identical copies found in the catalogue:
- ai-analyzer — 100% identical, 36 lines differ
- ai-analyzer — 98% identical, 38 lines differ
- ai-analyzer — 98% identical, 38 lines differ
- ai-analyzer — 98% identical, 0 lines differ
- ai-analyzer — 98% identical, 38 lines differ
- ai-analyzer — 98% identical, 38 lines differ
- ai-analyzer — 98% identical, 38 lines differ
- ai-analyzer — 98% identical, 38 lines differ
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Supporting material availability
This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files:
<support-root>/scripts/generate_ai_report.py
Ask for the original supporting material or an existing installation before using a step that depends on these paths; this catalog does not identify a verified download source for them. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable.
AI健康分析器
用于整理已提供的健康数据、描述趋势并准备与医生讨论的问题；这份说明不是经过临床验证的预测系统。
When to Use
- The user wants AI-driven health analysis across multiple health datasets or lifestyle signals.
- You need anomaly detection, risk prediction, or personalized recommendations based on health inputs.
- You need generated health reports or question-answering over health metrics and trends.
输入与证据边界
这些方法是可选分析思路，不是已实现或验证的算法。统计异常不等于医学异常；CUSUM、Z分数和相关性需要足够、可比的样本，相关不证明因果。营养摄入低于参考量不直接证明缺乏，睡眠问卷不诊断睡眠障碍。紧急症状按当地急救流程处理，避免让报告生成延误就医。
核心功能
1. 智能健康分析
- 多维度数据整合: 整合基础指标、生活方式、心理健康、医疗历史等4类数据源
- 异常模式识别: 使用CUSUM、Z-score等算法检测异常值和变化点
- 相关性分析: 计算不同健康指标之间的相关性（皮尔逊、斯皮尔曼）
- 趋势预测: 基于历史数据进行趋势分析和预测
2. 健康风险预测
- 高血压风险: 基于Framingham风险评分模型
- 糖尿病风险: 基于ADA糖尿病风险评分标准
- 心血管疾病风险: 基于ACC/AHA ASCVD指南
- 营养缺乏风险: 基于RDA达成率和饮食模式分析
- 睡眠障碍风险: 基于PSQI和睡眠模式分析
3. 个性化建议引擎
- 基础个性化: 基于年龄、性别、BMI、活动水平等静态档案
- 建议分级: Level 1（一般性）、Level 2（参考性）、Level 3（医疗建议）
- 循证依据: 基于医学指南和循证医学证据
- 可操作性: 提供具体、可行的改进建议
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.
- 4d ago Changed 40f885e5e6e2
- 11d ago Changed · +15 lines 3694d23c94a4
- 1mo ago Changed · +1 lines abab5b9fc71a
- 1mo ago First seen · 231 lines · 46 tokens per session scan A 8c8b586dd333
ai-analyzer is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,503 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 2,729 once invoked, about $0.0002 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 2026-09-05.
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