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Cookieless Alternatives

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mukul975
cookieless-alternatives

Evaluating and implementing cookie-less tracking alternatives for a post-cookie era. Covers the Privacy Sandbox APIs (Topics, Attribution Reporting, Protected Audiences), server-side analytics, and privacy-preserving measurement techniques.

Overview

Publishermukul975
RepositoryPrivacy-Data-Protection-Skills
Skill namecookieless-alternatives
Stars
279
Forks
59
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Cookieless Alternatives 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.
  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/mukul975/Privacy-Data-Protection-Skills.git /tmp/Privacy-Data-Protection-Skills
mkdir -p .claude/skills
cp -r /tmp/Privacy-Data-Protection-Skills/plugins/cookie-consent-skills/skills/cookieless-alternatives .claude/skills/cookieless-alternatives
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cookieless Alternatives 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 Cookieless Alternatives 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 Cookieless Alternatives 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.

Cookie-less Tracking Alternatives

Overview

The deprecation of third-party cookies by browsers and increasing consent-driven restrictions on first-party cookies are driving the advertising and analytics industries toward alternative measurement and targeting approaches. Google's Privacy Sandbox initiative, launched in 2019 and iteratively developed through 2024-2026, provides a suite of browser APIs designed to replace cross-site tracking functionality with privacy-preserving alternatives. Alongside browser-based solutions, server-side analytics and probabilistic measurement methods offer additional paths. This skill evaluates each alternative's privacy properties, technical requirements, and readiness for production use.

Privacy Sandbox APIs

Topics API

Purpose: Interest-based advertising without cross-site tracking.

How It Works:

  1. Chrome observes the user's browsing activity (top-level page visits) locally on-device
  2. Each week, the browser classifies visited sites into topics from a taxonomy of approximately 470 advertising-relevant categories
  3. When an ad tech company calls the Topics API on a publisher's site, the browser returns up to 3 topics (one from each of the last 3 weeks)
  4. Topics are selected with 5% random noise (a random topic is returned instead of the true one)
  5. A caller only receives topics that it has also observed the user being interested in (on other sites where the caller was present)

Topics Taxonomy Examples:

Topic IDCategoryLevel
1Arts & Entertainment1
57Arts & Entertainment / Movies2
86Business & Industrial / E-Commerce2
243Shopping / Apparel2
332Sports / Team Sports / Football3

Implementation for Pinnacle E-Commerce Ltd:

javascript
// Check if Topics API is available
if ('browsingTopics' in document) {
  // Observe: register this page visit for topic classification
  // The fetch call with {browsingTopics: true} both observes and retrieves
  const response = await fetch('https://ads.pinnacle-ecommerce.com/topics', {
    browsingTopics: true
  });
  const topics = await response.json();
  // topics = [{ topic: 86, version: "chrome.2:2:1", configVersion: "chrome.2", taxonomyVersion: "2", modelVersion: "1" }]
}

Privacy Properties:

  • No cross-site identifier shared
  • Topics are coarse-grained (470 categories, not granular interests)
  • 5% noise prevents deterministic profiling
  • Users can view and clear their topics in Chrome settings
  • Sites can opt out via Permissions-Policy: browsing-topics=()

Limitations:

  • Chrome-only (no Safari, Firefox support)
  • Coarse targeting compared to cookie-based audiences
  • Adoption depends on ad tech ecosystem support

Attribution Reporting API

Purpose: Measuring ad conversions without cross-site tracking.

Two Modes:

ModeReport TypeDataNoiseDelay
Event-levelPer-conversionSource: 64-bit ID; Trigger: 3 bits (8 values) for clicks, 1 bit for viewsYes (noise applied to trigger data)2 days (click), 1 day (view)
AggregatableAggregate summarySource + trigger combined into histogram bucketsAdditive Laplace noise10-60 minutes (batched)

Event-Level Attribution Flow:

1. User clicks ad on publisher.com
   → Browser stores: source_event_id=12345, destination=pinnacle-ecommerce.com

2. User converts on pinnacle-ecommerce.com
   → Browser matches: trigger_data=2 (e.g., "purchase")

3. After 2-day delay, browser sends report:
   {
     "source_event_id": "12345",
     "trigger_data": "2",
     "source_type": "navigation",
     "randomized_trigger_rate": 0.0024
   }
   → Sent to reporting origin (ads.pinnacle-ecommerce.com)

Aggregatable Attribution Flow:

1. Ad impression/click registered with source keys
2. Conversion triggers aggregation keys
3. Browser generates encrypted aggregatable report
4. Reports sent to aggregation service (Trusted Execution Environment)
5. Aggregation service applies differential privacy noise
6. Summary report returned: { "campaign_A_purchases": 1247 ± 23 }

Implementation for Pinnacle E-Commerce Ltd:

Source registration (on ad click):

html
<a href="https://www.pinnacle-ecommerce.com/product/widget-pro"
   attributionsrc="https://ads.pinnacle-ecommerce.com/register-source">
  Buy Widget Pro
</a>

Source registration response header:

Attribution-Reporting-Register-Source: {
  "destination": "https://www.pinnacle-ecommerce.com",
  "source_event_id": "12345678",
  "expiry": "604800",
  "aggregation_keys": {
    "campaignCounts": "0x159",
    "geoValue": "0x5"
  }
}

Trigger registration (on conversion):

html
<img src="https://ads.pinnacle-ecommerce.com/register-trigger" attributionsrc>

Trigger registration response header:

Attribution-Reporting-Register-Trigger: {
  "event_trigger_data": [
    { "trigger_data": "2", "priority": "100" }
  ],
  "aggregatable_trigger_data": [
    { "key_piece": "0x400", "source_keys": ["campaignCounts"] }
  ],
  "aggregatable_values": { "campaignCounts": 32768 }
}

Protected Audiences API (formerly FLEDGE)

Purpose: Remarketing and custom audience targeting without third-party cookies.

How It Works:

  1. Advertiser adds users to interest groups via JavaScript on their site
  2. Interest group data (ad creative URLs, bidding logic) stored locally in the browser
  3. When a publisher's ad slot runs an auction, the browser executes on-device bidding
  4. Winning ad is rendered in a fenced frame (isolated from the publisher page)
  5. No cross-site data leaves the browser; auction happens locally

Interest Group Join (on pinnacle-ecommerce.com):

javascript
navigator.joinAdInterestGroup({
  name: "widget-pro-viewers",
  owner: "https://ads.pinnacle-ecommerce.com",
  biddingLogicUrl: "https://ads.pinnacle-ecommerce.com/bidding.js",
  ads: [
    {
      renderUrl: "https://ads.pinnacle-ecommerce.com/ads/widget-pro-retarget.html",
      metadata: { "product": "widget-pro", "discount": "10%" }
    }
  ],
  userBiddingSignals: { "viewed_product": "widget-pro", "visit_count": 3 },
  trustedBiddingSignalsUrl: "https://ads.pinnacle-ecommerce.com/signals",
  trustedBiddingSignalsKeys: ["widget-pro-bid"],
  dailyUpdateUrl: "https://ads.pinnacle-ecommerce.com/update-ig",
  lifetimeMs: 2592000000  // 30 days
}, 2592000000);

Privacy Properties:

  • User profile data never leaves the device
  • Auction runs in browser, not on ad server
  • Fenced frames prevent information leakage to publisher
  • K-anonymity requirements on ad creatives (must be shown to sufficient users)
  • Users can view and leave interest groups in Chrome settings

Shared Storage API

Purpose: Cross-site data access with restricted output gates.

Allows writing data from multiple sites into a shared storage partition, but reading is restricted to privacy-preserving output gates:

  • Select URL (choose from a predefined list)
  • Private Aggregation (contribute to aggregate histograms)
  • Fenced Frames (render content without leaking data)

Use Case for Pinnacle E-Commerce Ltd: Frequency capping across sites — count how many times a user has seen an ad without cross-site cookies:

javascript
// On publisher site, inside shared storage worklet
class FrequencyCapOperation {
  async run(data) {
    const count = await this.sharedStorage.get('pinnacle-widget-pro-views') || 0;
    if (count >= 3) {
      // User has seen this ad 3 times — show fallback
      return 1; // Index of fallback URL
    }
    await this.sharedStorage.set('pinnacle-widget-pro-views', count + 1);
    return 0; // Index of ad URL
  }
}

Server-Side Analytics Alternatives

Cookieless Server-Side Measurement

SolutionCookiesPII CollectionHostingConsent Required
Server log analysisNoneIP address (anonymize)Self-hostedNo (operational necessity)
Plausible AnalyticsNoneNo PIIEU cloud or self-hostedVaries by jurisdiction
Fathom AnalyticsNoneNo PIICanadian/EU hostingVaries by jurisdiction
UmamiNone (optional)No PIISelf-hostedVaries by jurisdiction
Matomo (cookieless mode)NoneFingerprint hash (session only)Self-hostedCheck with local DPA

Plausible Analytics Configuration for Pinnacle E-Commerce Ltd

html
<!-- Single script, no cookies, ~1KB -->
<script defer data-domain="pinnacle-ecommerce.com"
  src="https://plausible.io/js/script.js"></script>

What Plausible Collects (per pageview):

  • Page URL (path only)
  • Referrer (domain only)
  • Browser (derived from User-Agent)
  • Operating system
  • Device type (desktop/mobile/tablet)
  • Country (from IP, IP discarded immediately)

What Plausible Does NOT Collect:

  • Cookies
  • Persistent identifiers
  • IP addresses (hashed for daily unique counting, hash rotated daily)
  • Cross-session tracking
  • Personal data

Comparison Matrix

CapabilityThird-Party CookiesTopics APIAttribution ReportingProtected AudiencesServer-Side Analytics
Interest-based targetingFull granularity~470 topicsNoRetargeting onlyNo
Conversion measurementDeterministicNoYes (with noise/delay)LimitedServer-side only
Cross-site trackingYesNoNoNoNo
Works in SafariNo (blocked)NoNoNoYes
Works in FirefoxNo (blocked)NoNoNoYes
Consent requiredYesEvolvingEvolvingEvolvingDepends on config
Production-readyLegacyYes (Chrome)Yes (Chrome)Yes (Chrome)Yes

Key Legal and Technical References

  • Google Privacy Sandbox Documentation — Topics API, Attribution Reporting, Protected Audiences specifications
  • W3C Private Advertising Technology Community Group — Standards development for privacy-preserving advertising
  • ePrivacy Directive 2002/58/EC, Article 5(3) — Legal basis for consent requirements; exemptions may apply to some alternatives
  • GDPR Recital 47 — Legitimate interest considerations for analytics
  • UK ICO Guidance on Analytics Cookies (2019, updated 2023) — Analytics cookies require consent under PECR
  • CNIL Topics API Analysis (2023) — French DPA assessment of Privacy Sandbox proposals
  • CMA (UK Competition and Markets Authority) Privacy Sandbox Commitments — Google's commitments regarding Privacy Sandbox development
  • IAB Tech Lab Privacy Sandbox Task Force — Industry evaluation of Privacy Sandbox APIs

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 Cookieless Alternatives AI skill do?

Evaluating and implementing cookie-less tracking alternatives for a post-cookie era. Covers the Privacy Sandbox APIs (Topics, Attribution Reporting, Protected Audiences), server-side analytics, and privacy-preserving measurement techniques.

Why use Cookieless Alternatives on TypingMind?

Because you install it once and use it with any model. Cookieless Alternatives 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 Cookieless Alternatives in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/plugins/cookie-consent-skills/skills/cookieless-alternatives. 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 Cookieless Alternatives?

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 Cookieless Alternatives?

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

Is the Cookieless Alternatives AI skill free?

Yes. It is published on GitHub by mukul975 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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