Total 50,657 skills, AI & Machine Learning has 8491 skills
Showing 12 of 8491 skills
Search and discover academic scholars using OpenJobs AI. Find researchers by name, affiliation, research areas, citations, h-index, publications, and more with structured filters.
One sentence — what this skill does and when Claude should use it.
Asks for user feedback after each task or cron job completion and runs a recursive learning flow. If output is good, asks what was good until 10 approvals; if needs improvement, asks why/how/what via multiple choice plus optional examples, uses web search and iterative thinking to resolve, and caps iterations by severity (slight 5, medium 10, severe 20). Keeps feedback non-intrusive. Use when completing discrete tasks or cron jobs for the user.
Run explicit Extruct API tasks through the bundled Extruct CLI. Covers Deep Search, semantic search, lookalike search, company and people tables, column operations, enrichment, and contact finding.
One-click model liberation toolkit for removing refusal behaviors from LLMs via surgical abliteration techniques
MacOS voice input tool with local/cloud ASR engines, LLM text optimization, and fully local storage built in Swift
AI-Native Issue-Driven development workflow. From GitHub Issue to merged PR: parse issue, explore codebase, design technical plan, execute with agent team, create PR, and cleanup. Use when a user wants to implement a GitHub Issue end-to-end: `/issue-flow #123` or `/issue-flow` to pick from open issues.
Toolkit for creating and validating skills and subagents. Use when: creating a new skill (fast or full mode), validating an existing skill, deciding Skills vs Subagents, migrating docs to skills, estimating token cost, or running a security scan. Triggers: "create skill", "build skill", "validate skill", "new subagent", "skills vs subagents", "estimate tokens", "security scan".
Agent onboarding automation for AIBTC first-hour setup. Use when a new or existing agent needs a structured bootstrap flow: wallet readiness, AIBTC registration check, heartbeat health checks/check-in, safe skill-pack installs, and a one-command doctor summary with next actions.
Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.
Orchestrate subagent workflows for complex tasks that benefit from decomposition, role-based delegation, and parallel execution. Use when Codex should assemble a temporary team of subagents, choose roles from a reusable role library, create a controlled fallback role when no preset role fits, coordinate read-heavy work in parallel, or handle write-heavy work with ownership boundaries, staged execution, and an integrator-led merge path.
Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.