Agent skill

Competitor Monitor

by indranilbanerjee in indranilbanerjee/digital-marketing-pro

Monitor competitors over time: baseline sites, pricing and ads by script, flag changes.

MITAuto-check passedMarketing & SEO

Install Competitor Monitor

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

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

GitHub CLI
$ gh skill install indranilbanerjee/digital-marketing-pro competitor-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/competitor-monitor .claude/skills/competitor-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
competitor-monitor
GitHub stars
862
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
1,542 words
Files
1
Skills in repo
162
Repo updated
First seen
Licence
MIT

At a glance

Monitor competitors over time: baseline sites, pricing and ads by script, flag changes.

  • Works in 6 steps: Load brand context: Read… → Validate competitor URLs and collect… → Save baselines via… → …
  • Marketing & SEO work in your project
  • SKILL.md covers Purpose, Input Required, Process and Output, plus 1 more section
  • Calls python

What it does

Competitor Monitor is an agent skill from indranilbanerjee/digital-marketing-pro. Monitor competitors over time: baseline sites, pricing and ads by script, flag changes. "track our competitors over time"

Its SKILL.md is about 3.1k 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. 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

  • Marketing & SEO work in your project

Example prompts

  • “track our competitors over time”
  • “/competitor-monitor”

Requirements

  • Python 3

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. Validate competitor URLs and collect initial baseline data: For each competitor, verify the provided URL resolves and identify the correct…
  3. Save baselines via competitor-tracker.py: Execute python "${CLAUDE_PLUGIN_ROOT}/scripts/competitor-tracker.py" --brand {slug} --action…
  4. Configure monitoring schedule per dimension: Set up the recurring scan schedule based on user-specified or default frequencies. Daily…
  5. Set up alert rules: Define what constitutes a significant change per dimension and configure the notification routing for each alert type…
  6. Create initial competitive intelligence brief: Synthesize the collected baseline data into a structured competitive intelligence summary…

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:

    • python

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

  • Network

    No URLs in SKILL.md.

    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

Competitor Monitor loads about 3.1k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 1,542 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~35
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

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). 1,542 words, ~3,057 tokens.

Download SKILL.mdSave it as .claude/skills/competitor-monitor/SKILL.md (or your agent's skills folder).
name
competitor-monitor
description
Monitor competitors over time: baseline sites, pricing and ads by script, flag changes. "track our competitors over time"

/digital-marketing-pro:competitor-monitor

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/.

Purpose

Set up and manage ongoing competitor monitoring. Define which competitors to track, what to monitor (content changes, pricing updates, ad activity, social mentions, SEO rankings, SERP feature ownership), how often to scan each dimension, and what alerts to trigger when significant changes are detected. This command establishes competitive intelligence baselines by capturing the current state of each competitor across all monitored dimensions, then configures recurring scans to detect and surface changes over time. Baselines serve as the reference point for all future change detection — without them, alerts have no context for what constitutes a meaningful shift versus normal fluctuation. Supports per-dimension scan frequencies so high-velocity dimensions like pricing and ads can be checked daily while slower-moving dimensions like content strategy and SEO authority are reviewed weekly or monthly. The monitoring configuration persists across sessions and powers both the competitor-alerts notification system and the share-of-voice trend tracking.

Input Required

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

  • Competitors to track: A list of competitor names with their primary website URLs — e.g., "Acme Corp (acme.com), Beta Inc (beta.io), Gamma Labs (gammalabs.com)". Each competitor becomes a monitored entity with its own baseline profile and independent scan schedule. Minimum one competitor required, no upper limit but recommend 3-8 for manageable monitoring volume and meaningful competitive context without signal overload
  • Monitoring dimensions: Which competitive aspects to track for each competitor — content (new pages, blog posts, significant page edits, messaging changes on key pages), pricing (pricing page changes, plan restructuring, discount offers, free trial modifications), ads (new Google Ads campaigns, Meta Ad Library activity, ad copy and creative changes, new platform presence), social (mention volume, sentiment shifts, follower growth, posting frequency and engagement rates), seo (organic keyword rankings on tracked terms, domain authority changes, backlink profile shifts, new content indexation), serp (featured snippet ownership, People Also Ask presence, knowledge panel changes, AI overview citations). Select all for comprehensive coverage or choose a subset per competitor based on competitive relevance
  • Scan frequency per dimension: How often to check each dimension — daily, weekly, or monthly. Recommended defaults: daily for pricing and ads (high-velocity, time-sensitive competitive signals requiring fast response), weekly for content and SEO (meaningful changes accumulate over days, not hours), monthly for full strategic review (positioning, messaging, market stance, and competitive narrative evolution). Custom frequencies can be set per competitor per dimension for asymmetric monitoring
  • Alert thresholds and notification channel: What constitutes a significant change worth alerting on per dimension, and where to send alerts — Slack channel name (e.g., #competitor-intel) or email address. Thresholds can be qualitative ("any pricing change") or quantitative ("ranking drop of more than 5 positions on any tracked keyword", "social mention volume exceeding 2x baseline"). If not specified, sensible defaults are applied per dimension based on typical competitive volatility patterns
  • Tracked keywords (optional): Specific keywords to monitor for SEO and SERP dimension tracking — brand terms, category head terms, product-specific terms, and high-intent commercial queries where competitor visibility matters most. If omitted, keywords are inferred from brand context, competitor content overlap analysis, and any existing keyword research data

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 positioning, competitive landscape context, target market definitions, and industry vertical. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load competitive sensitivity rules and any competitor-specific monitoring preferences. 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. Validate competitor URLs and collect initial baseline data: For each competitor, verify the provided URL resolves and identify the correct root domain with any relevant subdomains. Capture the current state across all selected monitoring dimensions — website key pages (homepage, pricing, product, about, blog) with full meta tag snapshots, page titles, H1 headings, and core messaging blocks; pricing page structure including plan names, price points, feature lists, and tier differentiation; social media profiles across LinkedIn, Twitter/X, Facebook, Instagram, and YouTube with current follower counts, posting frequency, and recent engagement metrics; current ad activity from Google Ads Transparency Center and Meta Ad Library including active campaigns, ad copy samples, and creative formats; and organic search visibility on tracked keywords with current positions, estimated traffic, and SERP feature ownership. This comprehensive baseline snapshot becomes the reference point against which all future changes are measured.
  3. Save baselines via competitor-tracker.py: Execute python "${CLAUDE_PLUGIN_ROOT}/scripts/competitor-tracker.py" --brand {slug} --action save-baseline --competitor {name} --url {url} --data '{baseline_json}' for each competitor. Each baseline record includes the competitor name, primary URL, dimension-specific data snapshots with structured fields for comparison, collection timestamp, data source references, and data completeness indicators (marking any dimensions where data collection was partial or unavailable). Baselines are stored per-brand at ~/.claude-marketing/brands/{slug}/competitors/ so multiple brands can track overlapping competitors with fully independent monitoring contexts and separate change detection histories.
  4. Configure monitoring schedule per dimension: Set up the recurring scan schedule based on user-specified or default frequencies. Daily scans for pricing and ad activity — changes in these dimensions are time-sensitive and may require immediate competitive response such as counter-offers or bid adjustments. Weekly scans for content changes and SEO rankings — meaningful shifts accumulate over several days and weekly cadence provides sufficient detection speed without excessive scanning overhead. Monthly full strategic reviews covering competitive positioning analysis, messaging evolution, market stance assessment, and competitive narrative trajectory. Each schedule entry records the competitor name, dimension, frequency, next scan date, data collection method, and expected scan duration. Schedule persists across sessions and powers automated scanning when integrated with cron or scheduling services.
  5. Set up alert rules: Define what constitutes a significant change per dimension and configure the notification routing for each alert type. Content alerts trigger on new page publication or significant edits to key pages (homepage, pricing, product pages) where the content diff exceeds the similarity threshold — minor copy tweaks are filtered out while messaging pivots and new feature announcements surface. Pricing alerts trigger on any detectable change to pricing page content, plan names, price points, or tier structure — pricing is binary-sensitive so any change is noteworthy. Ad alerts trigger on new campaign detection in Google Ads Transparency Center or Meta Ad Library, or major creative rotation exceeding 50% new creatives. Social alerts trigger when mention volume exceeds 2x the rolling 30-day baseline average or when sentiment score shifts more than 0.3 points. SEO alerts trigger on ranking changes greater than 5 positions on any tracked keyword. SERP alerts trigger on featured snippet ownership changes, knowledge panel modifications, or People Also Ask presence shifts on tracked queries. Route all alerts to the specified Slack channel via send-notification or to email, with urgency tiering based on competitive impact assessment.
  6. Create initial competitive intelligence brief: Synthesize the collected baseline data into a structured competitive intelligence summary for each monitored competitor. Cover current market positioning and brand narrative, key messaging themes and value proposition differentiation, pricing strategy with tier structure and competitive pricing gaps, content focus areas with publishing cadence and topic coverage map, social media presence with platform-specific strengths and engagement benchmarks, SEO authority with domain metrics and keyword overlap heat map showing where competitors directly contest the brand's visibility, and areas of direct competitive overlap where monitoring should be most vigilant. Include a competitive threat assessment ranking each competitor by overall threat level (primary, secondary, emerging) with reasoning, and recommended watch priorities based on the brand's strategic objectives and market position.
Show full SKILL.md (299 more words)Show less

Output

A structured competitor monitoring setup containing:

  • Competitor profiles with baselines saved: Per-competitor profile cards showing name, URL, all captured baseline metrics across every monitored dimension, data completeness indicators, data collection timestamp, and storage confirmation with file path from competitor-tracker.py
  • Monitoring schedule configuration: Table of all scheduled scans — competitor name, dimension, frequency (daily/weekly/monthly), next scheduled scan date, data collection method, and estimated scan duration for each entry. Total scan volume summary showing scans per day, per week, and per month across all competitors
  • Alert rules defined: Per-dimension alert trigger conditions with specific thresholds (quantitative where applicable, qualitative otherwise), urgency tier assignment (critical/warning/info), notification channel and routing configuration, and estimated alert frequency per dimension based on historical competitive activity patterns observed during baseline collection
  • Initial competitive intelligence brief: Structured narrative summary per competitor covering market positioning, pricing strategy, content approach, social presence, SEO authority, and competitive overlap areas — with competitive threat ranking (primary/secondary/emerging), strategic watch recommendations, and key questions the monitoring should help answer over time
  • Next scan dates per competitor per dimension: Calendar view of all upcoming scans organized by date, so the user knows exactly when the first change detection pass will run for each competitor-dimension combination and when to expect the first monitoring results

Agents Used

  • competitive-intel — Competitor analysis and research across all monitoring dimensions including website auditing, pricing intelligence, ad activity scanning, and social profile benchmarking. Initial baseline data collection from public websites, ad libraries, and social platforms. Monitoring schedule configuration with per-dimension frequency optimization based on competitive volatility assessment. Alert rule definition with threshold calibration informed by competitive activity patterns and dimension-specific noise levels. Change detection framework setup with significance criteria tuned to minimize false positives while catching meaningful competitive shifts. Competitive intelligence brief synthesis with threat assessment, strategic watch recommendations, and competitive narrative interpretation

© 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/competitor-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

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Referralscoreyhaines31/marketingskills54k2 repos~2.6kAutomated safety check: PassMIT
SEO GeoReScienceLab/opc-skills1.8k4 repos~2.1kAutomated safety check: PassApache-2.0

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Categories

Questions about Competitor Monitor

What does Competitor Monitor do?

Monitor competitors over time: baseline sites, pricing and ads by script, flag changes. Competitor Monitor is an agent skill from indranilbanerjee/digital-marketing-pro. Monitor competitors over time: baseline sites, pricing and ads by script, flag changes.

When should I use Competitor Monitor?

Competitor Monitor fits situations like: marketing & SEO work in your project.

How do I install Competitor Monitor in Claude Code?

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

How do I install Competitor Monitor in Codex?

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

Can I use Competitor 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 competitor-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/competitor-monitor, .gemini/skills/competitor-monitor, .github/skills/competitor-monitor and .opencode/skills/competitor-monitor in your project.

What does Competitor Monitor need to run?

Going by SKILL.md and its folder, Competitor Monitor needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Competitor Monitor access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Competitor 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 Competitor Monitor use?

Competitor 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 Competitor Monitor use?

About 3.1k tokens (SKILL.md is roughly 12k 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 Competitor Monitor?

Skills that share tags, products or a category with Competitor Monitor: Geo Fundamentals (wasp-lang/wasp, 19k stars), Ab Testing (coreyhaines31/marketingskills, 54k stars), Hreflang and International SEO (AgriciDaniel/claude-seo, 19k stars) and Referrals (coreyhaines31/marketingskills, 54k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Competitor 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.