Skill UI

A comprehensive framework for producing high-authority, multi-layered AI industry content. This skill teaches how to structure content across three pillars: real-time quick news reports (Speed), data-driven deep dives (Depth), and annual trend reports/panorama maps (Authority). Ideal for content creators, journalists, and industry analysts needing to establish thought leadership in the rapidly evolving AI sector.

Emulates Liu Cixin's hard science fiction creative thinking, offering frameworks for macro-detail narrative, scientific concept-driven social conflict, technological optimism, and cosmic sociology. Ideal for generating sci-fi story ideas, worldbuilding, and dark forest-style plot conflicts.

A skill that captures Guixingren's AI technology media creation mindset, featuring Silicon Valley first-line perspectives, China-US dual-line narratives, rapid news aggregation structure, and AI community productization operations. Suitable for AI industry news writing, Silicon Valley reporting, China-US AI comparison analysis, developer tool reviews, and AI community operations, including hackathons, podcasts, and offline events.

Framework for AI media writing inspired by Synced. Focuses on paper-first reporting, quantitative data, academic plus industry parallelism, layered analysis, and productized content. Applies to deep tech reviews, model benchmarks, industry trend reports, and editorial commentary.

This skill provides a strategic framework for content creation within the AI Agent vertical. It emphasizes hands-on product testing, translating complex technical concepts for general audiences, community-driven distribution, and practical tutorials. Ideal for tech media creators, AI product reviewers, and content strategists aiming to build authority in the AI Agent space through verified experience and clear communication.

Andru.ia's principal solutions architect skill. Diagnoses workspace state, distinguishes new (Pure Engine) from existing (Evolution) projects, and sets optimal technical roadmap and expert squad for Spanish-language AI projects. Generates tareas.md and plan_implementacion.md artifacts.

This skill acts as a Systems Engineer for the Andru.ia platform. It is designed to create, draft, and deploy new AI skills within a specific repository while adhering to the Diamond Standard. The workflow involves defining technical names, expert roles, and key outputs, then generating necessary documentation like README files and registry snippets. It ensures high-quality, non-generic skills with defined expert roles and proper folder structures.

A comprehensive guide to building production-grade evaluation systems for Large Language Models (LLMs). This skill covers advanced methodologies, including Direct Scoring and Pairwise Comparison, alongside critical techniques for mitigating systematic biases (e.g., Position Bias, Length Bias). Learn how to select appropriate metrics and structure prompts to ensure reliable, consistent, and objective quality assessment across various AI applications.

This skill enables systematic improvement of existing AI agents by analyzing performance metrics, identifying failure modes, and applying prompt engineering techniques. It involves establishing baselines, optimizing prompts through chain-of-thought and few-shot examples, refining role definitions, and validating changes through structured testing and A/B comparisons to ensure reliability and efficiency.

Comprehensive techniques for testing REST, SOAP, and GraphQL APIs during bug bounty hunting and penetration testing. Covers vulnerability discovery, authentication bypass, IDOR exploitation, injection attacks, and API-specific vectors.

This skill covers strategies for compressing conversation history in AI agent sessions to manage context window limits. It focuses on optimizing tokens-per-task rather than just tokens-per-request. Techniques include anchored iterative summarization, opaque compression, and regenerative summaries. It also addresses artifact trail integrity and evaluation methods like probe-based testing to ensure critical information like file paths and decisions are preserved during compression.

Context optimization extends effective capacity of limited context windows via compression, masking, caching, and partitioning. It aims to better utilize available capacity rather than increasing window size. Techniques include summarizing context, masking verbose outputs, reusing KV-cache, and partitioning work across sub-agents to reduce costs and latency in AI agent systems.