All Skills

Generates a compact HTML KPI digest from Adobe Analytics, showing how key metrics changed over a period and what drove the movement.

Generates a polished, leadership-ready performance briefing with KPI tiles, executive narrative bullets, and a driver analysis — all as a print-ready HTML document.

Creates complete AEM components with dialog, HTL template, Sling Model, unit tests, and clientlibs. Supports extending Core Components and project components, and can translate Figma designs into pixel-perfect HTL, CSS, and JS.

Convert static HTML pages to Edge Delivery Services while preserving original design and enabling content authoring in Document Authoring.

Generate comprehensive admin documentation for AEM Edge Delivery Services project handover, covering Config Service setup, permissions, Admin API operations, and troubleshooting.

Import a single webpage from any URL into canonical EDS block format — structured HTML that authors edit in DA. Scrapes the page, analyzes structure, maps to existing blocks, and generates HTML for immediate local preview.

Hand off tasks to the SLICC browser agent or install a new skill into SLICC from a GitHub repository.

Preview and verify imported content in a local AEM Edge Delivery Services development server, validating rendering and comparing with the original page.

Surveys available blocks from a local AEM Edge Delivery Services project and Block Collection to understand the block palette available for authoring, returning a block inventory with purposes to inform content modeling decisions.

Guides migration of AEM Cloud Service schedulers from legacy patterns to OSGi-compliant Sling Scheduler or Sling Jobs, with classification, review checklists, and troubleshooting.

Orchestrates end-to-end lifecycle workflows for the Adobe Dispatcher Apache HTTP Server module in AEM 6.5 LTS, from design and implementation through validation, release readiness, and incident troubleshooting.

Migrate AEM replication code from legacy CQ Replicator and Sling Replication Agent APIs to the Sling Distribution API for AEM as a Cloud Service, covering agent selection, async handling, and service-user setup.

Migrates AEM Cloud Service code from legacy AssetManager APIs (createAssetForBinary, getAssetForBinary, removeAssetForBinary, client-facing createAsset) to Direct Binary Access or in-JVM resolver patterns, covering IMS authentication and service-user setup.

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Diagnose and fix common AEM 6.5 LTS replication issues including blocked queues, connectivity failures, and content distribution problems.

Prove a feature works by delegating verification to an independent sub-agent that drives the real app, then open a pull request with the proof.

Diagnose the PM-to-Director transition using altitude (scope) and horizon (time) axes, identify transition zones, and apply the Cascading Context Map when organizational direction is vague.

Map every stakeholder before engaging anyone, using an equity-aware process that captures not just obvious sponsors but gatekeepers, impacted communities, and overlooked voices.

Calculate SaaS revenue, retention, and growth metrics to diagnose momentum, churn, expansion, or product-market-fit signals.

Plan tailored engagement for a specific stakeholder before critical conversations, milestones, or relationship repairs.

Build evaluation frameworks for agent systems with deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, and outcome measurement.

Model agent mental states using BDI (Beliefs, Desires, Intentions) ontology patterns, enabling cognitive reasoning, explainability, and semantic interoperability in multi-agent systems.

Diagnose and mitigate context degradation patterns including lost-in-middle failures, context poisoning, distraction, confusion, and clash in AI agent systems.

Design multi-agent systems with context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, and decision frameworks for when multiple agents are justified.

Provides expert guidance for distributed training with DeepSpeed, covering ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, and sparse attention.

Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.

Compress large language models by 40-60% with minimal accuracy loss using one-shot pruning techniques like Wanda and SparseGPT, enabling faster inference and deployment on constrained hardware.

Generates publication-quality figures for ML papers, including architecture diagrams via Gemini and data-driven charts via matplotlib/seaborn.

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality.

Compress large language models using knowledge distillation from teacher to student models, covering temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Records research provenance as a post-task epilogue, scanning conversation history to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with provenance tags.

Generate marketplace product image cards, including main images, secondary product shots, and A+ content modules, via the Higgsfield CLI.

Design n8n AI agents with best practices for node selection, sub-node wiring, tool design, structured output, and memory management.

Makes n8n workflow failures loud, structured, and recoverable by wiring per-node error outputs, retry logic, error-trigger workflows, and proper HTTP response shapes.

Routes n8n workflow-building tasks to the correct skill, with defaults and red-flag guidance for reliable n8n automation.

Enforces an opinionated UI baseline to prevent AI-generated interface slop, covering Tailwind CSS, accessible components, interaction patterns, animation constraints, typography, layout, and performance.