Results for “ai-agent”

Guides AI agents on working with a deprecated, read-only repository of Claude Skills, covering skill structure, development workflows, and upload automation.

Sets up a codebase for reliable agent-driven development by making it legible (structured docs, custom lints, code graph), executable (one-command dev stack, cloud sandbox), and verifiable (e2e gate, verify-before-ship loop).

Build production-grade AI agents with LangGraph, covering graph construction, state management, reducers, conditional routing, checkpointers, and human-in-the-loop patterns.

Provides a structured process for creating or updating skills that extend an AI agent's capabilities with specialized knowledge, workflows, and tool integrations.

Prevents AI agents from calling methods, imports, or variables that do not provably exist in the user's installed version, reducing hallucinated API usage.

Use when sharing knowledge between agents, companies, or teams to ensure effective transfer of understanding. This skill provides procedures for structured knowledge sharing that ensures the receiver gains actionable understanding, not just information.

Naive UI Skills Library - A comprehensive skill library for AI agents to understand and utilize Naive UI components. Invoke when user needs to work with Naive UI components, theming, i18n, dark mode, or design specifications.

Forces AI agents to investigate before editing or running destructive commands, improving code quality by requiring concrete facts like importers, data schemas, and user instructions.

Write and debug code inside n8n's Custom Code Tool node, with guidance on input modes, return formats, error handling, and common pitfalls.

Orchestrate autonomous AI development pipelines through your Kanban board, managing multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

Train reinforcement learning agents using PPO, SAC, DQN, TD3, DDPG, and A2C algorithms with a scikit-learn-like API. Supports custom Gymnasium environments, vectorized environments, callbacks, and model persistence.

Guides AI agents through Test-Driven Development with 42 prioritized rules covering the red-green-refactor cycle, test design, isolation, data management, assertions, organization, performance, and strategy.

Guides AI agents to apply advanced mathematical and probabilistic techniques (Bloom filters, HyperLogLog, FFT, etc.) for large-scale data problems where classical algorithms are optimal but math offers better asymptotic bounds.

Install, configure, and extend Pi, a terminal coding harness, with support for custom providers, models, extensions, skills, packages, themes, SDK integration, RPC mode, JSON event streams, and ecosystem packages for subagent delegation, MCP servers, interactive forms, and web access.

Test generation is repetitive but critical. Developers skip tests because writing edge cases is tedious. AI agents can generate tests quickly but often produce brittle, unrealistic tests that provide false confidence. Tests that mock internals, use fake data like "foo@example.com", or test implementation details break on every refactor.

Use to measure which help articles actually resolve contacts versus merely being read, including contact-after-view and assisted resolution. Trigger for "are our help articles working", article view counts misleading, deflection measurement, contact after reading an article, self-service success metrics, KB ROI, or stopping AI agents from optimising for page views.

The living embodiment of WinDAGs V3 — the AI orchestration platform where agents accumulate genuine expertise. Knows the full constitution, convention process, tradition positions, behavioral contracts, and derivative documents. Advises on architecture with ADR provenance, explains decisions with tradition attribution, grows source material as we build. Activate on "windags", "WinDAGs", "constitution", "convention", "why did we decide", "what does WinDAGs say", "how should we build", "architecture decision", "tradition", "behavioral contract". NOT for building specific DAGs (use windags-architect), creating individual skills (use skill-architect), or managing skill libraries (use windags-librarian).