Monitor keyword rankings with drop alerts and SERP features.

MITAuto-check passedMarketing & SEO

Install Rank Monitor

skills CLI
$ npx skills add indranilbanerjee/digital-marketing-pro --skill rank-monitor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install indranilbanerjee/digital-marketing-pro rank-monitor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/rank-monitor .claude/skills/rank-monitor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rank-monitor
GitHub stars
862
Used in
1 other repo
Token cost
~2.8k tokens
SKILL.md length
1,365 words
Files
1
Skills in repo
162
Repo updated
First seen
Licence
MIT

At a glance

Monitor keyword rankings with drop alerts and SERP features.

  • Works in 6 steps: Load brand context: Read… → Capture current rankings baseline: Query… → Configure monitoring schedule: Save the… → …
  • Tasks that involve AI search optimization
  • SKILL.md covers Purpose, Input Required, Process and SERP-feature tracking…, plus 4 more sections
  • Calls npm

What it does

Rank Monitor is an agent skill from indranilbanerjee/digital-marketing-pro. Monitor keyword rankings with drop alerts and SERP features. Two-snapshot compare → seo-drift. "track our keyword rankings"

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering AI search optimization. It works with Model Context Protocol. The repository describes itself as: An open-source AI marketing operating system for strategy, SEO, AEO/GEO, paid media, content, CRM, and analytics - grounded in brand context, human approval, and verifiable… The licence is MIT.

When your agent uses it

  • Tasks that involve AI search optimization

Example prompts

  • “track our keyword rankings”
  • “/rank-monitor”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load…
  2. Capture current rankings baseline: Query the connected rank sources (GSC MCP, plus any rank-tracker / Moz MCP available) for the current…
  3. Configure monitoring schedule: Save the keyword list, mode (rankings or rankings+features), monitoring frequency, alert thresholds…
  4. On each monitoring check: query and compare: Pull current positions for all tracked keywords. Compare each keyword's current position to…
  5. Detect significant changes: Identify keywords that crossed alert thresholds — drops exceeding the configured position threshold, keywords…
  6. Generate alert if thresholds are breached: Categorize alerts by severity — minor for 3-5 position drops (monitor), major for 5-10 position…

What it can do on your machine

Read from SKILL.md and the folder at commit 9e949f3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Rank Monitor loads about 2.8k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 1,365 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from indranilbanerjee/digital-marketing-pro at commit 9e949f3, republished under its MIT licence (© indranilbanerjee). 1,365 words, ~2,772 tokens.

Download SKILL.mdSave it as .claude/skills/rank-monitor/SKILL.md (or your agent's skills folder).
name
rank-monitor
description
Monitor keyword rankings with drop alerts and SERP features. Two-snapshot compare → seo-drift. "track our keyword rankings"
argument-hint
[brand-name] [--features]

/digital-marketing-pro:rank-monitor

Purpose

Set up and manage keyword ranking monitoring — and, with --features, SERP-feature tracking in the same run. Track target keyword positions across Google, establish baselines, detect drops greater than 5 positions, and generate alerts when rankings change significantly. In --features mode, also track which SERP features appear for each query (AI Overviews, Featured Snippets, People Also Ask, Knowledge Panels, Local Pack, Image Pack, Video Carousel, Shopping) and whether the brand owns them. This gives ongoing visibility into organic performance — catching ranking declines early, spotting upward trends, and tracking the increasingly feature-rich results page.

Merged skill (was rank-monitor + serp-tracker). SERP-feature tracking is now the --features mode of this one skill. The old /digital-marketing-pro:serp-tracker is a deprecation pointer to here.

Data sources (read this before configuring)
  • Google Search Console MCP is the authoritative position + impressions source for verified properties. GSC returns per-query/per-page positions, impressions, clicks, and CTR — it does not return the full per-query SERP-feature layout or AI Overview citation lists. Do not claim otherwise.
  • Rank-tracker MCPs (Ahrefs / Semrush / SE Ranking, if connected) fill in positions for keywords/competitors GSC can't see and provide their own SERP-feature flags.
  • Moz MCP (mcp-moz) is optional — verify the package exists on npm before use (npm view mcp-moz); npx executes remote code, so don't wire an unverified package. If Moz isn't connected, use GSC + whichever rank-tracker MCP the brand already has.
  • AI Overview presence in --features mode records only whether an AI Overview appeared and whether the brand was cited in it (a binary SERP-feature signal). For scored AI-visibility measurement across the 6 canonical AI surfaces, use /digital-marketing-pro:geo-monitor / /digital-marketing-pro:aeo-audit (the canonical AI-visibility scoring standard) and, for actual impressions, /digital-marketing-pro:gsc-ai-performance. Do not re-implement AI-visibility scoring here.

Input Required

The user must provide (or will be prompted for):

  • Target keywords: A list of keywords to monitor — provided directly, imported from a CSV or Google Sheet, or pulled from the brand's existing keyword tracking list at ${CLAUDE_PLUGIN_DATA}/{brand}/seo/keywords.json. Keywords should include search intent classification (informational, navigational, transactional, commercial) if available
  • Mode: default is rankings-only. Pass --features to also build the SERP-feature presence matrix per query in the same run
  • Monitoring frequency: daily or weekly — daily for high-priority head terms and active-campaign keywords (and volatile feature sets), weekly for long-tail and lower-priority terms
  • Alert thresholds: Position change that triggers an alert — default is >5 position drop. Customizable per keyword group (e.g., >3 for brand terms, >5 for head terms, >10 for long-tail). Both drop and gain thresholds are supported
  • Competitor domains (optional): Domains to track alongside the brand for the same keywords / features — up to 10
  • Device type: mobile, desktop, or both
  • Target country: The Google locale to check rankings in — e.g., US, UK, AU, CA, IN

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
  2. Capture current rankings baseline: Query the connected rank sources (GSC MCP, plus any rank-tracker / Moz MCP available) for the current ranking position of each target keyword. Record position, ranking URL, click-through rate and impressions from GSC where available. In --features mode, also record which SERP features are present for the query (from the rank-tracker's feature flags or manual observation) and the owning domain per feature. For competitor domains, capture their positions (and feature ownership) for the same keywords.
  3. Configure monitoring schedule: Save the keyword list, mode (rankings or rankings+features), monitoring frequency, alert thresholds, competitor domains, device type, and target country to ${CLAUDE_PLUGIN_DATA}/{brand}/seo/rank-monitor/config.json. Create or update the baseline snapshot at ${CLAUDE_PLUGIN_DATA}/{brand}/seo/rank-monitor/baseline.json with the current positions (and, in --features mode, the feature matrix) as the reference point.
  4. On each monitoring check: query and compare: Pull current positions for all tracked keywords. Compare each keyword's current position to both the baseline (original position when monitoring started) and the previous check (last recorded position). Calculate absolute change from baseline, change since last check, rolling 7-day and 30-day trend direction, and average position across all tracked keywords. In --features mode, diff the feature matrix against the previous snapshot (features gained/lost, ownership changes, AI Overview appearance/citation changes).
  5. Detect significant changes: Identify keywords that crossed alert thresholds — drops exceeding the configured position threshold, keywords that fell from page 1 (positions 1-10) to page 2 or beyond, keywords that gained >5 positions (potential quick wins), and (in --features mode) new SERP-feature appearances or losses for the brand's ranking URLs, plus competitor rank/feature changes that moved them above or below the brand.
  6. Generate alert if thresholds are breached: Categorize alerts by severity — minor for 3-5 position drops (monitor), major for 5-10 position drops (investigate content freshness, technical issues, or competitor activity), critical for >10 position drops or page 1 → page 2 transitions (immediate investigation — check for algorithm updates, manual actions, technical errors, or content cannibalization). In --features mode, treat a lost owned Featured Snippet or a lost AI Overview citation as at least major. Include recommended next steps for each severity level.
Show full SKILL.md (514 more words)Show less

SERP-feature tracking (--features mode)

When --features is set, the run also builds a query-by-feature matrix. Tracked features and how to read them:

FeatureWhat "owned" meansOptimization signal
AI OverviewAn AI Overview appeared AND the brand's URL is one of its cited sourcesBinary citation-presence signal only. For scored AI visibility use /digital-marketing-pro:geo-monitor
Featured SnippetBrand holds position 0 for the queryFormat for extraction: paragraph (40-60 words), list (5-8 items), or table
People Also AskA brand URL answers a PAA question for the queryTarget PAA questions with FAQ-style H2/H3 content
Knowledge PanelPanel shows for the brand entityStrengthen entity signals (Wikidata, GBP, structured data) — see /digital-marketing-pro:entity-audit
Local PackBrand appears in the map 3-packGBP optimization + local schema — see /digital-marketing-pro:local-seo
Image / Video CarouselBrand asset appears in the carouselOptimize alt text / filenames (images) or titles, descriptions, transcripts (video)
Shopping / SitelinksBrand listing presentProduct schema / site structure

Feature opportunities are scored by achievability (how close the brand is to winning the feature given current position and content format) × traffic impact (estimated CTR impact given query volume and feature prominence).

Output

A structured ranking (and, in --features mode, SERP-feature) report containing:

  • Ranking snapshot: Current positions for all tracked keywords — position, ranking URL, device, country, date, comparison to baseline and previous check with directional indicators (up, down, stable)
  • Change report: Position changes since baseline and since last check — sorted by largest drops first, with 7-day and 30-day trend sparklines
  • Alert summary: Keywords needing attention — grouped by severity (critical, major, minor) with specific position changes, affected URLs, and recommended investigation steps
  • SERP-feature matrix (--features): Query-by-feature grid showing which features appear, who owns them (brand, competitor, or other), and change since last snapshot — including AI Overview appearance + brand-citation status
  • Feature opportunity list (--features): Ranked unowned features the brand could realistically target, with specific content/schema recommendations
  • Competitor comparison: Relative position (and feature-ownership) changes for tracked competitor domains — who gained, who lost, head-to-head per keyword, and competitive gap trends over time

Tips & caveats

  • GSC has no AI Overview citation export. The --features AI Overview signal is observational (did an AIO appear, is the brand cited). Reconcile true AI impressions via /digital-marketing-pro:gsc-ai-performance and scored AI visibility via /digital-marketing-pro:geo-monitor.
  • Position deltas are noisier than click/impression deltas — a keyword bouncing between positions 8 and 12 produces big percentage swings that mean little. Trust impression/click moves more for diagnosis.
  • GSC data lags ~3 days. When pulling "current" data, end the window 3 days ago.
  • Don't over-track. 50-150 high-value keywords tracked well beats 2,000 tracked as noise.

Agents Used

  • seo-specialist — Keyword ranking and SERP-feature analysis, feature-ownership attribution, ranking-change diagnosis (algorithm update vs. technical issue vs. competitive displacement vs. content decay), baseline establishment and trend calculation, and recommended actions per severity level
  • performance-monitor-agent — Alert generation with severity classification, monitoring-schedule management, threshold-breach detection with rolling-window comparison, trend tracking with 7-day and 30-day directional analysis, and notification formatting

See also

  • /digital-marketing-pro:geo-monitor — scored AI visibility across the 6 canonical AI surfaces (the AI-visibility scoring standard)
  • /digital-marketing-pro:gsc-ai-performance — actual AI Overview / AI Mode impressions from GSC
  • /digital-marketing-pro:seo-drift — compare two ranking snapshots and surface top movers

© indranilbanerjee, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/rank-monitor of indranilbanerjee/digital-marketing-pro.

Open the folder on GitHubat commit 9e949f3

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in indranilbanerjee/digital-marketing-pro, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Rank Monitor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Rank Monitor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Rank Monitor this skillindranilbanerjee/digital-marketing-pro8621 repos~2.8kAutomated safety check: PassMIT
SEO DataforseoAgriciDaniel/codex-seo7992 repos~4.6kAutomated safety check: PassMIT
AI VisibilityRyze-AI-Adgent/open-seo-mcp-skills4.7k—~611Automated safety check: PassMIT
Geo Scorejianruntech/geo-score582—~2.9kAutomated safety check: PassMIT
Competitor GapRyze-AI-Adgent/open-seo-mcp-skills4.7k—~560Automated safety check: PassMIT
Content BriefRyze-AI-Adgent/open-seo-mcp-skills4.7k—~610Automated safety check: PassMIT

Similar skills

  • SEO Dataforseo

    AgriciDaniel/codex-seo

    Live SEO data via DataForSEO MCP server. An agent skill from AgriciDaniel/codex-seo.

    799 GitHub starsUsed in 2 repos~4.6k tokens
    Marketing & SEOAuto-check passed
  • AI Visibility

    Ryze-AI-Adgent/open-seo-mcp-skills

    Measure real AI-engine visibility — traffic from ChatGPT, Perplexity, Claude, Gemini and which pages they cite — from actual GA4 referral data, not prompt sampling.

    4.7k GitHub stars~611 tokensUpdated 17 days ago
    Marketing & SEOAuto-check passed
  • Geo Score

    jianruntech/geo-score

    Score a website's AI answer-engine visibility 0–100 against the open AIV rubric, and, with the user's own API keys, check and track through the OpenAI, Perplexity, Gemini and Anthropic APIs whether…

    582 GitHub stars~2.9k tokensUpdated today
    Marketing & SEOAuto-check passed
  • Competitor Gap

    Ryze-AI-Adgent/open-seo-mcp-skills

    Find keywords a competitor ranks for that the user's site doesn't — the content gap, prioritized by volume and winnability.

    4.7k GitHub stars~560 tokensUpdated 17 days ago
    Marketing & SEOAuto-check passed
  • Content Brief

    Ryze-AI-Adgent/open-seo-mcp-skills

    SERP-driven content brief for a target keyword — what ranks, what to cover, headings, questions, internal links from your own data.

    4.7k GitHub stars~610 tokensUpdated 17 days ago
    Marketing & SEOAuto-check passed
  • Aero

    Canonry/canonry

    Diagnose AEO regressions and interpret Canonry AI visibility, Advanced multi-property portfolios, and Site Health evidence.

    171 GitHub stars~4.8k tokensUpdated yesterday
    Marketing & SEOAuto-check passed

More from indranilbanerjee/digital-marketing-pro

All 162 skills in this repo
  • Import Template

    indranilbanerjee/digital-marketing-pro

    Import a deliverable template as a reusable placeholder template per brand.

    862 GitHub starsUsed in 1 repo~1.6k tokens
    Auto-check passed
  • Ab Test Plan

    indranilbanerjee/digital-marketing-pro

    Plan an A/B test by script: sample size per variant, days to run, stopping rules.

    862 GitHub starsUsed in 1 repo~1.9k tokens
    Auto-check passed
  • Aeo Audit

    indranilbanerjee/digital-marketing-pro

    Run a one-time AEO audit of six AI answer engines, scored per surface.

    862 GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • Agent Readiness Audit

    indranilbanerjee/digital-marketing-pro

    Audit agent readiness by script: AI-crawler rules, product schema, no-JS HTML, feeds.

    862 GitHub starsUsed in 1 repo~3.7k tokens
    Auto-check passed
  • Backlink Gap

    indranilbanerjee/digital-marketing-pro

    Find backlink gap domains linking to competitors, not you, scored by script.

    862 GitHub starsUsed in 1 repo~2.6k tokens
    Auto-check passed
  • C2pa Metadata

    indranilbanerjee/digital-marketing-pro

    Embed C2PA provenance in AI-generated images, video or PDF by script.

    862 GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed

Categories

Questions about Rank Monitor

What does Rank Monitor do?

Monitor keyword rankings with drop alerts and SERP features. Rank Monitor is an agent skill from indranilbanerjee/digital-marketing-pro. Monitor keyword rankings with drop alerts and SERP features.

When should I use Rank Monitor?

Rank Monitor fits situations like: tasks that involve AI search optimization.

How do I install Rank Monitor in Claude Code?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill rank-monitor -a claude-code`. Or copy the skill folder (skills/rank-monitor in indranilbanerjee/digital-marketing-pro) into .claude/skills/rank-monitor in your project. Claude Code loads it when a task matches its description.

How do I install Rank Monitor in Codex?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill rank-monitor -a codex`. Or copy the skill folder (skills/rank-monitor in indranilbanerjee/digital-marketing-pro) into .agents/skills/rank-monitor in your project. Codex loads it when a task matches its description.

Can I use Rank Monitor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add indranilbanerjee/digital-marketing-pro --skill rank-monitor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rank-monitor, .gemini/skills/rank-monitor, .github/skills/rank-monitor and .opencode/skills/rank-monitor in your project.

What does Rank Monitor need to run?

Going by SKILL.md and its folder, Rank Monitor needs the command-line tools its instructions call (npm).

Does Rank Monitor access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Rank Monitor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Rank Monitor use?

Rank Monitor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rank Monitor use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Rank Monitor?

Skills that share tags, products or a category with Rank Monitor: SEO Dataforseo (AgriciDaniel/codex-seo, 799 stars), AI Visibility (Ryze-AI-Adgent/open-seo-mcp-skills, 4.7k stars), Geo Score (jianruntech/geo-score, 582 stars) and Competitor Gap (Ryze-AI-Adgent/open-seo-mcp-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rank Monitor?

indranilbanerjee (a GitHub user) maintains it in indranilbanerjee/digital-marketing-pro, which has 862 GitHub stars. The repository holds 162 skills in this directory. The repository was last updated on October 9, 2026.

Source: indranilbanerjee/digital-marketing-pro on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.