AI Recipe Library

[OVERVIEW]When you upgrade from Adobe Analytics to Customer Journey Analytics, Adobe CX Enterprise Coworker can compare the data in both systems for you in a single conversation. You pick a report suite and a data view, choose whether to check one metric, one dimension, or everything at once, and Coworker reports how closely the two match, with insights and suggested next steps. You do not need to know how your implementation is architected, and there is no need to build queries or export data by hand.[IMPACT]Validating an upgrade comes down to knowing how closely your Customer Journey Analytics data matches your Adobe Analytics data, and where it does not. The output is an overall matching rate, key insights, a summary of totals and variance, and, depending on what you validate, a daily trend, date-level detail, or a scorecard, followed by likely causes and suggested actions. For example, in a scenario where you compare page views over the past 30 days and Customer Journey Analytics r

[OVERVIEW]You can check the quality of the data in your Adobe Experience Platform datasets with a plain-language prompt in Adobe CX Enterprise Coworker. It gives you a quick read on whether a dataset or a single field can be trusted, without writing SQL queries or digging through complex schema hierarchies. Coworker uses its Data Validation skill to run statistical and semantic checks on a sample of your data and returns the results in the same conversation. The skill is read-only, so it flags potential issues without changing your data, schemas, or mappings.[IMPACT]Use it for a fast quality check on a dataset or a field, for example after a new implementation or an implementation update, or as an ongoing check on a critical dataset to catch regressions early. The output is a results table with one row per field showing the share of valid, distinct, and null values, the most common values, the most common invalid values with an explanation, and a short note on the field's quality, foll

[OVERVIEW]Adobe CX Enterprise Coworker can take you from a full operational view of your Adobe Experience Platform environment to a newly built audience in a single conversation. This is a way around the manual, time-consuming audit of how your audiences, journeys, datasets, and destinations connect. Coworker generates that view on demand, benchmarks it against an industry blueprint from Experience League, and then builds a new audience from a plain-language description, with you confirming the choices along the way.[IMPACT]You get your bearings in an Experience Platform environment, see where the gaps are, and act on them without writing queries. The output is a report on how audiences and journeys connect, with a health summary and recommended next steps, a benchmark showing where you are doing well and where the gaps are, and a new audience built from a plain-language description. For example, in a scenario where the report shows journeys with no audiences attached, that is a gap wo

[OVERVIEW]This workflow shows how to use Adobe CX Enterprise Coworker to go from a simple question about your web analytics data to an investigated root cause in a single conversation. You skip the jumping between reports, building queries, and manually slicing data to find patterns. It starts with a broad exploration question, narrows to a trend on whatever stands out, and then asks Coworker to explain a pattern in that trend, all using Customer Journey Analytics data.[IMPACT]The goal is to move from a basic data question to an explained pattern without building queries by hand. The output is a visualization and table with key takeaways, a trended graph of the metric you chose, and an explanation of a recurring pattern in it. For example, in a scenario where traffic to a page shows recurring dips, the investigation could point to a repeating three-day cycle that lines up with a coordinated marketing campaign cadence, not a day-of-week effect.[LESSONS]Starting with a broad question giv

[OVERVIEW]Four plain-language questions in Adobe CX Enterprise Coworker can show where customers drop off before purchase. In practice, you describe what you want to know, such as where people are lost on the way to purchase, and Coworker picks the funnel, runs it on your Customer Journey Analytics data view or Adobe Analytics report suite, and suggests where to look next. You do not need to know which visualization to use. Each answer leads into the next question: find the biggest drop-off, check whether marketing channel explains it, check device type, then ask what to do about it.[IMPACT]What you are after is where in the purchase path customers are lost and which segment accounts for it, without building a fallout visualization by hand. The output is a funnel showing drop-off at each step, channel and device breakdowns of the biggest leak, and a recommendation on what to prioritize. Adobe's worked example, which uses sample data for a fictional retailer, follows this same path. It

[OVERVIEW]I have created a skill for creating Junit tests in AEM.[IMPACT]This should help to generate the good quality junit tests that are referenced from the official Adobe sources and the open source projects such as WCM.io.[LESSONS]The skill creator within Codex has been great at pointing the issues and best practices. Originally I had built this using Claude Code and has revamped it based on the feedback from Codex.[TRYABLE]```bash# Show helpnpx aem-junit-skill help# Show full guidenpx aem-junit-skill guide# Get Maven dependenciesnpx aem-junit-skill deps# Generate test templatenpx aem-junit-skill template modelnpx aem-junit-skill template servicenpx aem-junit-skill template componentnpx aem-junit-skill template servlet```[SETUP]This can be installed in Codex, Claude or Github Copilot.[SAMPLE_OUTPUT]https://github.com/narendragandhi/aem-junit-skill

[PROBLEM]Getting honest, challenging feedback on an idea before you take it to leadership or your team.[OVERVIEW]I've learned this from Jay Schwedelson's Eventastic 2026 event, where he shared the best prompt he's used for gathering feedback. It turns an AI tool into a live Board of Directors that debates your idea, challenges weak thinking, and ends every response with a direct question for you.[TRYABLE]Act as my live Board of Directors made up of:[Insert 4-5 names]This should feel like a REAL ongoing boardroom conversation that I am actively part of. Do NOT give polished summaries or long essays.Instead:- Make the conversation feel live, messy, opinionated, and unscripted- Let board members interrupt, disagree, react emotionally, joke, and challenge weak thinking- Only let 1-3 people respond naturally at a time- Each person should sound distinct and think differentlyMOST IMPORTANT: The conversation should NEVER fully end.At the end of EVERY response:- ONE specific board member must a

[OVERVIEW]I built a five-layer context system to stop AI coding assistants from guessing their way through AEM work. Copilot, Claude Code, Cursor - they're all capable, but without knowing how a specific project is put together they fall back on generic training data, and you end up reworking half of what they produce in review. So instead of repeating context in every prompt, I layered it: an AGENTS.md file at the repo root (generated with Adobe's ensure-agents-md skill via npx skills --plugin github-copilot) covering the project's purpose, structure, and build/test commands; smaller scoped instruction files for specific concerns, like .agents/instructions/testing.junit5.md for test conventions; Adobe's own vendor skills (Cloud Service, 6.5 LTS, or EDS depending on the project) for baseline best practices around components, dispatcher config, and OSGi; custom skills for the corrections I found myself making over and over; and a shared, versioned prompt library so the team isn't reinve

[OVERVIEW]I built this prompt to help turn a complex business or technical requirement into a clear Adobe solution design.The idea is to avoid jumping straight into a product feature or implementation. The prompt first breaks down what the business is actually trying to achieve, then looks at the data, platform responsibilities, constraints, and the best way to structure the solution across the relevant Adobe capabilities.It also asks the AI to explain why the most obvious approach may not work, use official Adobe documentation to support the analysis, and challenge its own recommendation before producing the final design.The output can then be used as the basis for a technical discussion or turned into a concise, slide-ready solution.[IMPACT]The main goal was to make solution design more structured, especially when a requirement involves several Adobe products or capabilities and it is not immediately clear where each responsibility should lie in it.I wanted the prompt to help move fr

[OVERVIEW]I built a reusable prompt to support technical architecture and integration assessments using AI.The prompt helps evaluate whether a proposed integration is not only technically possible, but also the right architectural choice. It compares direct integration, middleware, specialized services, and other alternatives while considering security, scalability, maintenance, ownership, and separation of responsibilities.A key part of the recipe is the double challenge: the AI must first question the initial technical assumption and then challenge its own preferred recommendation before reaching a conclusion.The analysis is also grounded primarily in official vendor documentation, clearly separating documented capabilities from architectural recommendations and assumptions that still require validation.The prompt is platform-agnostic and can be reused for different enterprise integration scenarios.[IMPACT]The goal was to use AI as a critical architecture assistant, rather than simpl

[OVERVIEW]Adobe's open-source AEMCS CDN Log Analysis Tooling ships an ELK stack and four prebuilt Kibana dashboards for analyzing AEM as a Cloud Service CDN logs. The dashboards answer the questions they were designed for - cache hit ratio, traffic, WAF. Everything else means hand-writing Elasticsearch Query DSL or building a new visualization, which is slow even if you know the syntax and a hard stop if you don't.I added an optional Elasticsearch MCP server to that stack, so you can point Claude Code, OpenAI Codex or GitHub Copilot at your indexed logs and just ask: "which client IPs came closest to the X requests/second rate limit, and when were the peaks?" The agent writes the query, runs it, and shows you the query it used so you can check it.It runs as one more container behind a Docker Compose profile, so docker compose up -d behaves exactly as before and nobody who doesn't want it is affected. Enable it with docker compose --profile mcp up -d. The endpoint binds to localhost onl

[OVERVIEW]I built an AI-powered framework that helps review and improve strategy recommendations before stakeholder meetings. Instead of relying only on my own perspective, I use AI to challenge assumptions, identify risks, suggest alternative approaches, and highlight potential stakeholder concerns. This helps create stronger recommendations, improves presentation quality, and increases confidence when making important decisions.[IMPACT]My goal was to improve the quality of strategy recommendations before stakeholder discussions. By using AI to review ideas, identify risks, and suggest improvements, I was able to prepare more confidently and reduce the time spent on manual analysis. The framework helped create more structured recommendations, identify potential concerns early, and improve overall presentation quality and decision-making.[LESSONS]Using AI works best when the context and goals are clearly defined. I learned that detailed prompts produce more accurate and actionable feed

[OVERVIEW] I built a repeatable process for creating AEM code base AI skills (Bootstrap, Execute, Refine, Retrofit) which a tech lead follows to not only create AI skills for their own code base, but also helps them to make their code base more AI-ready. I covered this process in my recent Skill Exchange talk, Beyond the Prompt, and can provide examples for some of the initial AEM skills that every AEM code base will need. [TRYABLE] Bootstrap: draft the initial skill Execute: test the skill Refine: perfect the skill Retrofit: align the codebase [SETUP] Just their AEM code base and AI coding companion. [IMPACT] Help AEM skills be purpose-built for every single unique AEM code base out there, since a set of generic skills provides nowhere near the same benefit. The result is better enablement of developers to work in AEM both efficiently and correctly, with fewer mistakes and omissions, resulting in less code review/refactoring burden on architects and technical leads. [LESSONS] AI

[OVERVIEW]I made a reusable moodboard template powered by AI that turns a creative brief into several visual directions and ready-to-use prompts for image generation. It helps with the problem of starting creative ideas from nothing and gives a clear structure for exploring different visual concepts before you begin production.The workflow accepts inputs like the campaign goal, audience, product, brand personality, wanted mood, required elements, and channel. The AI then creates several different creative directions, covering visual style, mood, color direction, composition, lighting, background, and an image-generation prompt. You can then refine the chosen direction and use it in whichever AI image-generation tool you prefer.[IMPACT]Goals:- Make it faster to go from a written brief to visual ideas.- Quickly produce several creative directions rather than working on just one idea.- Build a steady format for showing moodboards to stakeholders.- Make AI image prompts simpler to write an

OVERVIEW]An AI-assisted workflow to ensure parity in derived field components across data views/connections[IMPACT]Eliminate variance in derived field components across data views/connections.[LESSONS]Not included[TRYABLE]CJA derived fields require logic to define desired metrics and dimensions. This logic must be translated from business requirements and small variance in logic can result in unexpected variances in data.Additionally, derived fields are specific to each connection. This means they must be recreated in other connections. For example, a derived field in a prod connection is completely separate from one in a dev connection. This can easily lead to variances in the derived field definition and/or component settings which can also cause variance in data.Ensure Derived Field/Component Meets Business Requirements1. Build out the derived field2. Add to a data view and configure appropriately3. Use the Auto CJA SDR tool to download the components JSON definition4. Use a company

[OVERVIEW]This guide explains how to add repository-level agent hooks that run Semgrep after GitHub Copilot Agent performs a tool action. It gives AEM development teams a repeatable way to scan source modules quickly, review findings in advisory mode, and align local feedback with pull-request enforcement after the rules are tuned.[IMPACT]This should help to enforce and catch the security vulnerabilities and any other anti patterns that we would want to avoid in the AEM implementation. Which should definitely help in the overall quality of the code being delivered.[LESSONS]The Semgrep rules included here are intended as a starter pack and should be reviewed, refined, and expanded to align with your project's requirements and security standards. This exercise also highlights the power of GitHub Copilot Hooks and how they can be used alongside GitHub Copilot Skills to automate project-specific validation, security checks, and development workflows.[TRYABLE]1. Copilot Agent creates or edi

[OVERVIEW]Offline Python tool that replays 23 known attack requests against your Dispatcher /filter rules and shows which ones get through, and which rule to fix.It reads dispatcher.any, a farm file or filters.any (including $include) and replays 20 well-known dangerous requests (CRXDE, Package Manager, OSGi console, QueryBuilder, .infinity.json, .tidy.infinity.json, numeric selectors, .query.json, sysview/docview, /libs, /apps, /etc/packages, /home, /var, unrestricted POST, and the "* *.css *" query-string bypass) plus 3 that must keep working (a page, a DAM image, /etc.clientlibs) so over-blocking is noticed.For every probe it shows ALLOWED/BLOCKED, the deciding rule with file and line, why it matters, and a fix. Evaluation follows Adobe's documented semantics: last matching rule wins, /glob matches the whole request line, /query rules only match requests that have a query string. Most Dispatcher leaks are rule-ORDER problems (a broad allow placed after a deny backstop) or selector g

[OVERVIEW]Built an AI-assisted prototype-to-production workflow for AEM Edge Delivery Services. It enables teams to rapidly prototype experiences with AI, then convert them into governed, reusable, authorable EDS components with the right design system, content models, Universal Editor support, and QA.[IMPACT]Key outcomes:- Faster transition from an idea or design concept to a working experience.- Less rework between design/prototyping teams and AEM engineering teams.- Greater reuse of existing Edge Delivery components instead of continuously generating new ones.- More consistent application of the design system and AEM authoring patterns.- Earlier identification of accessibility, SEO, performance, and component-design issues.- A repeatable handoff model that allows teams to benefit from AI-generated code without treating that code as production-ready by default.[LESSONS]Treat AI-generated code as an accelerator, not the architecture. Map prototypes to existing EDS components first, ke

[OVERVIEW] I used Copilot enabled with Work IQ to output list of my work priorities in excel. This was hugely helpful in getting my current, ongoing tasks, identifying time-saving opportunities. I then discussed this with my manager who assigned priority labels and increased my efficiency & time management. [TRYABLE] Role You are my Chief of Staff. You are rigorous, sceptical of noise, and allergic to invented detail. Your job is to give me an honest picture of what I am actually accountable for right now — not a flattering one, and not a list of everything that has touched my inbox. Step 1 - Retrieve Search my emails, chat messages, calendar meetings and documents from the last [30] days. Search all four sources before drawing any conclusions. Prefer recent and high-signal items: threads where I am a named owner, meetings where I hold an action, and documents I have authored or been assigned in. Step 2 - Consolidate Where the same piece of work appears across multiple sources,

[OVERVIEW] Build a customizable, shareable multi-project or multi-account status dashboard Artifact — letting the user define their own accounts/projects, role-based section visibility, MCP connector data sources, and custom keywords/non-MCP inputs. [TRYABLE] 1. Describe the scope up front Tell Claude: "I want a single-page HTML dashboard tracking [account name] across these projects: [list them]. It needs to work for these roles: [list roles], each seeing different sections." 2. Name your data sources Say what's connected (Slack, Jira, Google Calendar, Zoom, etc.) and give Claude the specifics it needs to search correctly — channel names, ticket prefixes, project codenames. Without this, Claude can't tag anything to the right project. 3. Specify what "sections" you want List the actual tiles: task list, today's meetings, health scorecard, stakeholder pulse, hidden patterns, whatever matters to you. Otherwise Claude guesses generically. 4. Say explicitly you want it built as a shar

[OVERVIEW] Agent created in Copilot Studio that connects to Adobe Analytics and Adobe Target via MCP. Give it a URL, a date range and the page's goal, it pulls performance data, benchmarks every metric against the site average and prior period, and returns an executive summary, ranked insights with confidence levels, an optimisation backlog, and an AI Content Retrieval Score (a 0–100 heuristic for how cleanly an AI assistant could parse and cite the page). It then designs the right Target activity for the problem it found - hypothesis, success and guardrail metrics, audience, runtime and creates it in draft state only for human review. It never activates anything. [TRYABLE] Role: You are my digital experience strategist, with read access to Adobe Analytics and read/write access to Adobe Target. Every claim traces to a metric you actually pulled. Distinguish what the data shows from what you infer. Input: Page: [URL] · Window: last [30] days vs. preceding period · Goal: [conversion /