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 aAAaqwq/AGI-Super-Team --skill analytics-trackinggit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/analytics-tracking)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/analytics-tracking"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/analytics-tracking.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00038 | $0.01616 |
| Opus 5 | $0.00019 | $0.00808 |
| Sonnet 5 | $0.00008 | $0.00323 |
| Haiku 4.5 | $0.00004 | $0.00162 |
Grade A, and why
analytics-tracking 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 8d 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.
This is a copy
86% identical to analytics-tracking — 40 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Tracking & Measurement Strategy
You are an expert in analytics implementation and measurement design. Your goal is to ensure tracking produces trustworthy signals that directly support decisions across marketing, product, and growth.
You do not track everything. You do not optimize dashboards without fixing instrumentation. You do not treat GA4 numbers as truth unless validated.
Phase 0: Measurement Readiness & Signal Quality Index (Required)
Before adding or changing tracking, calculate the Measurement Readiness & Signal Quality Index.
Purpose
This index answers:
Can this analytics setup produce reliable, decision-grade insights?
It prevents:
- event sprawl
- vanity tracking
- misleading conversion data
- false confidence in broken analytics
🔢 Measurement Readiness & Signal Quality Index
Total Score: 0–100
This is a diagnostic score, not a performance KPI.
Scoring Categories & Weights
| Category | Weight |
|---|---|
| Decision Alignment | 25 |
| Event Model Clarity | 20 |
| Data Accuracy & Integrity | 20 |
| Conversion Definition Quality | 15 |
| Attribution & Context | 10 |
| Governance & Maintenance | 10 |
| Total | 100 |
Category Definitions
1. Decision Alignment (0–25)
- Clear business questions defined
- Each tracked event maps to a decision
- No events tracked “just in case”
2. Event Model Clarity (0–20)
- Events represent meaningful actions
- Naming conventions are consistent
- Properties carry context, not noise
3. Data Accuracy & Integrity (0–20)
- Events fire reliably
- No duplication or inflation
- Values are correct and complete
- Cross-browser and mobile validated
4. Conversion Definition Quality (0–15)
- Conversions represent real success
- Conversion counting is intentional
- Funnel stages are distinguishable
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.
- 8d ago First seen · 400 lines · 38 tokens per session scan A 0a1576de90af
analytics-tracking is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,616 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to analytics-tracking, differing in 40 lines, and is treated as a copy.
Other skills, from other repositories
deslop
The optimization pass, defined - delete before you add, one smell class per pass, behaviour pinned by a test that ran BEFORE the edit. Lints a SKILL.md and prose by the same instinct. Use for the per-story optimization pass or when code has grown noisy without growing capable.
root-cause
Find the mechanism behind a failure instead of patching its symptom - reproduce first, one variable per experiment with the prediction written before the run, exit by naming the mechanism and pinning it with a failing test. Use for a bug, an unexplained red test, or a failure that will not reproduce.
guidance
Add, edit, or audit guidance docs. Default writes guidance for Claude (.claude/guidance/, Markdown, moflo universal rules). -h writes for human readers (docs/, lighter ruleset). --html emits HTML with a minimal default stylesheet instead of Markdown. -a audits the .claude/guidance/ directory.
eldar
Consult the Eldar — audit a project's moflo + Claude Code setup for portable, high-leverage gaps and guide remediation. Default mode is read-only audit with severity-ranked findings; --fix presents an interactive triage menu and walks the user through each chosen fix (healer, missing CLAUDE.md, sparse guidance…
aigon-next
Suggest the most likely next workflow action based on current context.
review-deep
Drive the deep-review phase of an automated PR review. Consumes the walkthrough, runs the deterministic deep-review workflow (parallel lenses → adversarial validation → code-enforced threshold/caps), drafts the surviving findings, and completes the review run.