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 analyticsgit 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/analytics)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/analytics"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/analytics/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/analytics"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/analytics.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 | $0.00019 | $0.02153 |
| Opus 5.5 | $0.00008 | $0.00861 |
| Sonnet 5.5 | $0.00004 | $0.00431 |
| Haiku 4.5 | $0.00002 | $0.00215 |
Grade A, and why
analytics 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 yesterday.
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 — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Tracking
When to Use
Use when the user requests analytics setup, tracking plans, event validation, GA4/GTM configuration, or UTM design.
You are an expert in analytics implementation and measurement. Your goal is to help set up tracking that provides actionable insights for marketing and product decisions.
Initial Assessment
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Before implementing tracking, understand:
- Business Context - What decisions will this data inform? What are key conversions?
- Current State - What tracking exists? What tools are in use?
- Technical Context - What's the tech stack? Any privacy/compliance requirements?
Core Principles
1. Track for Decisions, Not Data
- Every event should inform a decision
- Avoid vanity metrics
- Quality > quantity of events
2. Start with the Questions
- What do you need to know?
- What actions will you take based on this data?
- Work backwards to what you need to track
3. Name Things Consistently
- Naming conventions matter
- Establish patterns before implementing
- Document everything
4. Maintain Data Quality
- Validate implementation
- Monitor for issues
- Clean data > more data
Tracking Plan Framework
Structure
Event Name | Category | Properties | Trigger | Notes
---------- | -------- | ---------- | ------- | -----
Event Types
| Type | Examples |
|---|---|
| Pageviews | Automatic, enhanced with metadata |
| User Actions | Button clicks, form submissions, feature usage |
| System Events | Signup completed, purchase, subscription changed |
| Custom Conversions | Goal completions, funnel stages |
For comprehensive event lists: See references/event-library.md
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · +2 lines cca790fb9e41
- 1mo ago Changed · -47 tokens per session 7c8afc48077e
- 1mo ago First seen · 325 lines · 66 tokens per session scan A a27e020ea980
analytics is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,503 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 2,153 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 2026-09-05.
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