Subject
10 entries
Skills
Bookmarks
Autoresearch Skill: autonomous skill optimization via eval loops
A Claude Code skill that autonomously optimizes other skills by running eval loops — binary scoring, targeted mutation, keep/discard decisions. Andrej Karpathy's training loop methodology applied to prompt engineering.
Tessl: package manager for AI agent skills
Tessl is npm for AI agent skills — versioned, evaluated, and discoverable through a public registry. Skills can be submitted via GitHub URL or evaluated locally via CLI before publishing.
Tessl content-strategy skill: community agent skills in practice
A Tessl-registered content-strategy skill by coreyhaines31 with 81% quality rating — an example of community-contributed, evaluated agent skills in the Tessl registry. Shows what agent skill distribution looks like in practice.
AI Design Components: 76 Claude skills across full-stack development
76 production-ready Claude skills across frontend, backend, DevOps, security, cloud, and AI/ML — organized as 19 plugin groups with a Skillchain v3.0 guided workflow system. One of the larger open-source Claude skill collections.
Skyll: runtime skill discovery for AI agents
Skyll is a REST API and MCP server that lets any AI agent discover and learn skills at runtime, without pre-installation. Aggregates SKILL.md files from GitHub and returns structured JSON for context injection.
AgentSkills.io: open format for agent capabilities
AgentSkills.io is a simple open format for giving AI agents new capabilities and expertise — a registry and spec for agent skill files. Makes skills portable and discoverable across agent frameworks.
Build Personal Moats
Erik Torenberg's essay on building personal moats — durable competitive advantages that compound over time by combining rare skills in ways that are hard to replicate. The key: find what's easy for you but hard for others.
Machine Learning Skills for Jobs (2013)
A Quora thread on what skills machine learning jobs required in 2013 — the answer set reflects the early data science job market before the role fragmented into ML engineer, data scientist, and AI researcher specializations. A snapshot of what practitioners thought mattered at the time.
How to Better Compete with Other Data Scientists
AnalyticBridge post on differentiation strategies for data scientists in an increasingly crowded field circa 2013. Covers specialization, communication skills, and building a public track record as ways to stand out beyond pure technical competence.
A Taxonomy of Data Science
Hilary Mason and Chris Wiggins' 2010 taxonomy of data science roles and skills, organized around the OSEMN framework: Obtain, Scrub, Explore, Model, iNterpret. One of the earliest attempts to define what data science actually comprises as a discipline.