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Rank Tracker

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aaron-he-zhu
rank-tracker

Use when the user asks to "track rankings" or "查排名"; measures keyword and SERP-position deltas over time from provided exports or connected tools, including AI-response checks. Not for multi-metric stakeholder reports or setting alerts — use performance-monitor (report or alert mode). 排名追踪/SERP监控

Overview

Publisheraaron-he-zhu
Repositoryaaron-marketing-skills
Skill namerank-tracker
Stars
2.9K
Forks
373
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aaron-he-zhu on GitHub. Read the source before you install it.

Installation

Install the Rank Tracker AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/seo-geo/evaluate/rank-tracker
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git /tmp/aaron-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/aaron-marketing-skills/seo-geo/evaluate/rank-tracker .claude/skills/rank-tracker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rank Tracker in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rank Tracker on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rank Tracker is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Rank Tracker

Tracks keyword positions, SERP feature ownership, and AI visibility over time.

Quick Start

Set up rank tracking for [domain] targeting these keywords: [keyword list]
Analyze ranking changes for [domain] over the past [time period]

Skill Contract

Expected output: a ranking report or delta summary plus the standard handoff summary for memory/monitoring/.

  • Reads: current rankings, prior baselines, target keyword list, locale/language/device/engine, snapshot/source refs, observation times/windows, stable page/content bindings, and any user-provided or tool metrics.
  • Writes: a user-facing monitoring deliverable and reusable summary.
  • Promotes: significant changes, confirmed anomalies, follow-up actions, and pending decisions to memory/open-loops.md.
  • Done when: every tracked keyword shows a current, context-matched position vs baseline with a labeled delta, or Unknown/NEEDS_REFRESH with its reason; each observation cites its source ref, observed time, window, locale/language/device/engine, evidence label, and ranking page ref; conflicts remain visible; and biggest movers and likely causes are named.
  • Primary next skill: performance-monitor when recurring monitoring should become automated.

Handoff Summary

Emit the standard shape from skill-contract.md §Handoff Summary Format.

Data Sources

All integrations optional (see CONNECTORS.md). With tools, pull rankings from ~~SEO tool, impressions from ~~search console, traffic from ~~analytics, and AI citations from ~~AI monitor. Without tools, ask for positions, volumes, competitor data, and SERP feature status.

Zero-dependency measurement loop (no paid tool needed): never narrate a ranking movement you did not measure — record each check as a snapshot and let the ledger compute the delta. python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/ledger.py" record <domain> --source rank --data '{"<keyword>": <position>, ...}', then ledger.py diff <domain> --source rank for the change since the last check and ledger.py trend <domain> --source rank --field "<keyword>" for the series. Record real Search Console positions when connected, or the positions the user provides at Tier 1. A ranking delta is a week-scale, confounded outcome — attribute it against a control (a holdout keyword/competitor), not against zero; see references/measurement-protocol.md. See scripts/connectors/README.md.

Decision Gates

Stop and ask the user when:

  • No target keywords are provided and none can be inferred from CLAUDE.md or prior monitoring records — offer: (1) supply a keyword list, (2) track the domain's top known terms, (3) cancel.

Continue silently (never stop for):

  • No prior baseline exists — record the current run as the baseline, label all positions as the first snapshot, and proceed (do not invent a "previous" position).
  • Missing optional tool data (SERP features, AI citations) — mark N/A and proceed.

Instructions

When a user requests rank tracking or analysis:

  1. Set Up Keyword Tracking — configure domain, market/locale, device, language, engine, update frequency, priorities, competitor watchlist, and stable page refs. Apply the SEO/GEO Evidence and Cycle Control Profile.
  2. Record Current Rankings — output a position table where every row cites its source (tool export / user-provided / estimated), with position ranges, ranking URLs, feature ownership, and movement vs baseline.
  3. Analyze Ranking Changes — highlight biggest wins, declines, stable terms, new rankings, lost rankings, likely causes, and recovery ideas; each delta labeled against its baseline.
  4. Track SERP Features — compare ownership of snippets, PAA, image/video packs, local packs, and related feature shifts.
  5. Track GEO / AI Visibility — monitor AI Overview presence, citation rate, citation position, and trend; mark each value Measured (from an ~~AI monitor) or N/A if unobserved.
  6. Compare Against Competitors — report share of voice, head-to-head comparisons, and threat levels.
  7. Generate Ranking Report — output overall trend, key wins, concerns, opportunities, SERP feature changes, GEO visibility, and recommendations, with each metric carrying its source tag.

Label every metric Measured, User-provided, Calculated, Estimated, Proxy, or Unknown; never present an estimate or proxy as measured. An applicable missing metric is Unknown with its reason. A stale or locale/language/device/engine-mismatched position is NEEDS_REFRESH, not a current ranking; N/A is only for a genuinely non-applicable field.

Reference: See Ranking Analysis Templates for the complete output templates for all seven steps.

Example

A ranking report names the biggest winners, biggest drops, and next actions, with each position carrying its source tag and labeled delta against baseline.

Rank Change Quick Reference

Response Protocol

ChangeTimeframeAction
Drop 1-3 positionsWait 1-2 weeksMonitor — may be normal fluctuation
Drop 3-5 positionsInvestigate within 1 weekCheck technical issues and competitor changes
Drop 5-10 positionsInvestigate immediatelyRun a full diagnostic: technical, content, links
Drop off page 1Emergency responseComprehensive audit + recovery plan
Position gainedDocument and learnIdentify what worked and replicate

Reference: See Tracking Setup Guide for tracking setup, root-cause taxonomy, CTR benchmarks, SERP feature impact, and algorithm-update assessment.

Save Results

Ask "Save these results?" If yes, write to memory/monitoring/ — see Skill Contract §Save Results Template.

Reference Materials

Next Best Skill

Initial setup (no baseline) → performance-monitor. Subsequent runs (baseline exists) → Terminal. Visited-set rule applies per skill-contract.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Rank Tracker AI skill do?

Use when the user asks to "track rankings" or "查排名"; measures keyword and SERP-position deltas over time from provided exports or connected tools, including AI-response checks. Not for multi-metric stakeholder reports or setting alerts — use performance-monitor (report or alert mode). 排名追踪/SERP监控

Why use Rank Tracker on TypingMind?

Because you install it once and use it with any model. Rank Tracker is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rank Tracker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/seo-geo/evaluate/rank-tracker. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Rank Tracker?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rank Tracker?

As many as you like. As long as a model supports skills, you can use Rank Tracker with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rank Tracker AI skill free?

Yes. It is published on GitHub by aaron-he-zhu under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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