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 | $0.02254 |
| Opus 5 | $0.00023 | $0.01127 |
| Sonnet 5 | $0.00009 | $0.00451 |
| Haiku 4.5 | $0.00005 | $0.00225 |
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 3d 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, 1 lines differ
- ai-analyzer — 98% identical, 3 lines differ
- ai-analyzer — 98% identical, 3 lines differ
- ai-analyzer — 98% identical, 3 lines differ
- ai-analyzer — 98% identical, 3 lines differ
- ai-analyzer — 98% identical, 3 lines differ
- ai-analyzer — 98% identical, 1 lines differ
- ai-analyzer — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI健康分析器
基于AI技术的综合健康分析系统,提供智能健康洞察、风险预测和个性化建议。
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.
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
4. 自然语言交互
- 智能问答: 支持健康数据查询、趋势分析、相关性查询等
- 上下文理解: 维护对话历史,支持多轮对话
- 意图识别: 识别用户查询意图,提供精准回复
5. AI健康报告生成
- 综合报告: 包含所有维度健康数据、AI洞察、风险评估
- 快速摘要: 关键指标概览、异常警示、主要建议
- 风险评估报告: 各类疾病风险、风险因素分析、预防措施
- 趋势分析报告: 多维度趋势、变化点识别、预测分析
- HTML交互式报告: ECharts图表、Tailwind CSS样式
使用说明
触发条件
当用户提到以下场景时,使用此技能:
通用询问:
- ✅ "AI分析我的健康状况"
- ✅ "我的健康有什么风险?"
- ✅ "生成AI健康报告"
- ✅ "AI分析所有数据"
风险预测:
- ✅ "预测我的高血压风险"
- ✅ "我有糖尿病风险吗?"
- ✅ "评估我的心血管风险"
- ✅ "AI预测健康风险"
智能问答:
- ✅ "我的睡眠怎么样?"
- ✅ "运动对我的健康有什么影响?"
- ✅ "我应该如何改善健康状况?"
- ✅ "AI健康助手问答"
报告生成:
- ✅ "生成AI健康报告"
- ✅ "创建综合分析报告"
- ✅ "AI风险评估报告"
执行步骤
步骤 1: 读取AI配置
const aiConfig = readFile('data/ai-config.json');
const aiHistory = readFile('data/ai-history.json');
检查AI功能是否启用,验证数据源配置。
步骤 2: 读取用户档案
const profile = readFile('data/profile.json');
获取基础信息:年龄、性别、身高、体重、BMI等。
步骤 3: 读取健康数据
根据配置的数据源读取相关数据:
// 基础健康指标
const indexData = readFile('data/index.json');
// 生活方式数据
const fitnessData = readFile('data-example/fitness-tracker.json');
const sleepData = readFile('data-example/sleep-tracker.json');
const nutritionData = readFile('data-example/nutrition-tracker.json');
// 心理健康数据
const mentalHealthData = readFile('data-example/mental-health-tracker.json');
// 医疗历史
const medications = exists('data/medications.json') ? readFile('data/medications.json') : null;
const allergies = exists('data/allergies.json') ? readFile('data/allergies.json') : null;
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.
- 3d ago Changed · +1 lines abab5b9fc71a
- 5d 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 (46,230 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 2,254 once invoked, about $0.0002 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 2026-09-05.
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