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 agentmods add skills/jtrackingai/analytics-tracking-automation/tracking-groupnpx skills add jtrackingai/analytics-tracking-automation --skill tracking-groupgit clone --depth 1 https://github.com/jtrackingai/analytics-tracking-automationWrote 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/jtrackingai/analytics-tracking-automation/tracking-group)<a href="https://agentmods.dev/skills/jtrackingai/analytics-tracking-automation/tracking-group"><img src="https://agentmods.dev/badge/skills/jtrackingai/analytics-tracking-automation/tracking-group.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 | $0.00031 | $0.00427 |
| Opus 5 | $0.00015 | $0.00214 |
| Sonnet 5 | $0.00006 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
tracking-group 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 6d 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.
What it actually says
Tracking Group
Use this skill when the work starts from site-analysis.json and the goal is to produce approved pageGroups.
Inputs
<artifact-dir>/site-analysis.json
Workflow
- Read the current
pageGroupsand page inventory. - Group pages by business purpose, not just URL shape.
- Present a concise table with group name, content type, URL pattern, and pages.
- Default to a reviewable summary first: total groups, grouping logic, and a compact table. Do not dump raw URL lists unless the user explicitly asks for them.
- Ask the user to confirm or adjust the grouping.
- Record approval with:
./event-tracking confirm-page-groups <artifact-dir>/site-analysis.json
Required Output
Produce and share:
- updated
<artifact-dir>/site-analysis.json - updated
<artifact-dir>/workflow-state.json
Closeout Style
- default to a page-group summary first: group count, grouping logic, and a compact group table
- keep the summary reviewable in chat; do not dump full raw URL lists unless the user explicitly asks
- list files and the next command only after the summary
Stop Boundary
Stop after page-group approval unless the user explicitly asks to continue into schema work.
A broad request such as "full workflow" or "全流程" does not count as page-group approval.
Do not run ./event-tracking confirm-page-groups <artifact-dir>/site-analysis.json --yes on the user's behalf unless the user explicitly confirms the current page groups in the current turn.
Useful follow-up:
./event-tracking prepare-schema <artifact-dir>/site-analysis.json
References
What ships with it
1 file 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.
- 6d ago First seen · 57 lines · 31 tokens per session scan A 8020739cc8f9
tracking-group is a skill published in the GitHub repository jtrackingai/analytics-tracking-automation (135 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 427 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 2026-08-30.
Other skills, from other repositories
analytics
Use when instrumenting product or web analytics — GA4/PostHog SDK wiring, event taxonomy, funnels, double-counted events, consent gating, PII scrubbing. NOT charting that data (that is dashboard), NOT choosing which metrics matter (that is kpi-framework), NOT experiment math (that is ab-testing), NOT cookie-policy…
web-analytics
Analyze web traffic, SEO performance, and search engine trends using Google Search Console, Google Analytics 4, and Bing Webmaster Tools. Use when the user asks to analyze traffic, check SEO, or find keyword opportunities.
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
proxies
Oxylabs proxy networks: Residential, Mobile, shared Datacenter/ISP, and Dedicated Datacenter/ISP proxies with geo-targeting, IP rotation, session persistence, and port-based sticky IPs. Use when routing traffic through proxies, building scrapers with proxy auth, rotating or sticky sessions, whitelisting IPs, or…
agentsop-llamaindex
Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers…
offer-compare-skill
Offer 对比决策器。用户同时拿到两份（或多份）offer 纠结怎么选时，扮演 Senior Career Decision Advisor：结构化对比 comp / 成长 / AI 敞口 / 公司强度 / 团队风险 / 晋升速度 / 生活方式，识别 front-loaded vs long-term upside、resume value、switch-out 难度、hidden risks，最后给一条明确、有立场、不中性的推荐（并附「你是 X 类人选 A，你是 Y 类人选 B」的分叉判定）。三步：贴两份 offer → 补 priorities & 现状 → 自动生成并打开一份 HTML Offer Decision…