Results for “edge-finding”

Coordinate multi-stage edge research pipelines from candidate detection through strategy design, review, revision, and export.

Analyze and improve the internal link structure of an AEM Edge Delivery Services site by building a link graph from the query index and page content, identifying orphan pages, hub pages, and content silos, and generating specific linking recommendations with suggested anchor text and placement.

Use when designing solutions to identify and test boundary conditions, unusual inputs, and uncommon scenarios that could cause failures. This skill provides a systematic approach to finding edge cases before they become bugs in production.

Walk every branching path and boundary condition in content, report only unhandled edge cases. Orthogonal to adversarial review - method-driven not attitude-driven. Use when you need exhaustive edge-case analysis of code, specs, or diffs.

Walk every branching path and boundary condition in content, report only unhandled edge cases. Orthogonal to adversarial review - method-driven not attitude-driven. Use when you need exhaustive edge-case analysis of code, specs, or diffs.

Walk every branching path and boundary condition in content, report only unhandled edge cases. Orthogonal to adversarial review - method-driven not attitude-driven. Use when you need exhaustive edge-case analysis of code, specs, or diffs.

Measures the trade-off between computation time and energy consumption in mobile edge computing by computing a weighted sum of the two objectives, given system configuration parameters and per-user task characteristics.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Clusters raw detection tickets into reusable edge concepts with thesis, invalidation signals, and strategy playbooks before strategy design.

Use this skill for search indexing, embeddings, RAG chunking, freshness, retrieval evaluation, source citations. Trigger when the task involves ai engineering work related to Search Indexing RAG, implementation, audits, debugging, strategy, or validation.

Migrate Jetpack Compose apps to support edge-to-edge displays, fix UI components obscured by system bars, and handle IME insets.

Search reference implementations and code examples from Adobe's Block Collection and community Block Party for AEM Edge Delivery blocks, plugins, and build tools.

Audit and optimize headings across AEM Edge Delivery Services pages for search intent, hierarchy, and consistency.

Identifies error-prone APIs, dangerous configurations, and footgun designs that enable security mistakes in API designs, configuration schemas, and cryptographic library ergonomics.

Audit images across AEM Edge Delivery Services pages for SEO and performance, checking alt text quality, dimensions, lazy loading, fetch priority, file naming, and decorative image handling, and generate per-page reports with fix instructions.

Rewrites AEM Edge Delivery Services page content for AI search discoverability (Generative Engine Optimization) by analyzing structure, clarity, and factual density.

Find adjacent vendors and subs (not top market primes) who fill a capability gap against a displacement target using USASpending flows and SAM entity signals. Use when user defines a teaming gap and wants vault-ready partner shortlist with citations.

Convert daily market observations and news reactions into structured edge hints, with optional LLM augmentation, outputting a canonical hints.yaml for downstream concept synthesis.

Reviews trading strategy drafts for edge plausibility, overfitting risk, sample size adequacy, and execution realism, outputting PASS/REVISE/REJECT verdicts with confidence scores.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

Frontier research tracking and trend scouting specialist. Use when the user asks to talk to Fade, requests the scout, or needs arxiv monitoring, paradigm shift detection, and emerging method analysis.

Analyzes institutional order flow, dark pool prints, and options flow sweeps to generate trade setups with defined entry, stop-loss, and target levels.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.

Idea Maze

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

Analyzes development cycle and quality metrics to identify recurring issues, then generates additive patches to skill instructions to prevent them.

Identify error-prone APIs and dangerous configurations

Converts abstract edge concepts into concrete strategy draft variants with configurable risk profiles and optional exportable ticket YAMLs for downstream validation.

Manipulate phylogenetic trees, detect evolutionary events, integrate NCBI taxonomy, and create publication-quality visualizations using the ETE toolkit.

Research companies, industries, or competitor sets using web search and seven analytical lenses to produce structured intelligence for downstream product management tasks.

Build web interfaces with genuine design quality, not AI slop. Use for any frontend work - landing pages, web apps, dashboards, admin panels, components, interactive experiences. Activates for both greenfield builds and modifications to existing applications. Detects existing design systems and respects them. Covers composition, typography, color, motion, and copy. Verifies results via screenshots before declaring done.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Layer 0 of the keyword research pipeline. Builds a topic-graph snapshot for the brand's category before any seed work, approximated from Semrush phrase_related + phrase_questions on the category seeds plus the brand's own ranking footprint (domain_organic_unique). Idempotent on brand-config hash; never blocks the pipeline; cheap.