Total 53,177 skills, AI & Machine Learning has 8896 skills
Showing 12 of 8896 skills
Responsible AI development and ethical considerations. Use when evaluating AI bias, implementing fairness measures, conducting ethical assessments, or ensuring AI systems align with human values.
Enforce disciplined agent development workflows with plan-first development, small-slice execution, specialized self-review roles, quality gates, and project setup. Use when starting a new project, setting up development conventions, wanting structured planning, or needing the agent to follow best practices for code quality, review, and validation.
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
Use when deploying your agent to AWS, or when a deploy has failed. Handles pre-flight validation, CDK/IAM/quota error diagnosis, version management, rollback, and canary deployments. Triggers on: "deploy my agent", "agentcore deploy", "deploy failed", "CDK error", "rollback", "canary deploy", "pin version", "redeploy", "deploy stuck". Not for production hardening — use agents-harden. Not for adding capabilities before deploy — use agents-build or agents-connect. Not for VPC configuration errors — use agents-build.
Reference: review of an inbound vendor agreement against the team playbook in `~/.claude/plugins/config/claude-for-legal/commercial-legal/CLAUDE.md`. Flags deviations, assesses risk, generates specific redline language, and routes to the right approver. Loaded by /commercial-legal:review when a vendor MSA, services agreement, or similar is detected.
Generate per-asset visual specifications and AI generation prompts from GDDs, level docs, or character profiles. Produces structured spec files and updates the master asset manifest. Run after art bible and GDD/level design are approved, before production begins.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
ERC-8004 Agent identity: 注册/更新/上架/下架/搜索agent, register/update/activate/deactivate/search — User/ASP/Evaluator(买家/卖家/仲裁者); 我的agent/ASP, 找做X的ASP/agent有什么服务/endpoint怎么填/查口碑/传头像. + Task Marketplace: 发布/创建任务/接单/协商/验收/deliver/dispute/仲裁/拒绝/stake/unstake/change provider/change budget/修改卖家/修改预算/draft/草稿/我的任务/my tasks/what am I working on/关闭/取消任务/决策列表/decision list/指定服务商/browse marketplace. + task watch: 监听任务进展/历史消息/未读消息/未决策/outstanding decisions. + okx-a2a missing/uninitialized. Match by meaning. MUST ACTIVATE on inbound envelopes: (1) {agentId, message:{source:"system", event, jobId,...}} system event; (2) {msgType:"a2a-agent-chat", jobId, sender:{role},...} agent-to-agent task chat (sender.role = COUNTERPARTY, not you); (3) literal "Read the okx-ai skill" (or legacy "Read the okx-agent-task skill") in the envelope.
ComfyUI custom node fundamentals - V3 node structure, Schema, inputs/outputs, registration. Use when creating new ComfyUI custom nodes, defining node classes, or setting up a custom node project.
dontbesilent Execution Diagnosis. Diagnose the real reason behind your 'know what to do but fail to act' using the Adlerian psychology framework. Triggers: /dbs-unblock, /self-check, 'I know what to do but can't do it', 'why do I always procrastinate' Execution block diagnosis using Adlerian psychology framework. Trigger: /dbs-unblock, "I know what to do but can't do it", "why do I procrastinate"
Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.