AI & ML

paulieb89 Bundle

UK property analysis: comparable sales, EPC ratings, rental yields, stamp duty, market context, and a BUY/WATCH/PASS verdict for a named address or postcode. Use to analyse a property, value a house, check what a place is worth, compare area prices, assess rental yield, or pull a full investment report. British spelling, UK stamp duty bands, real Land Registry and Rightmove data via the BOUCH property MCP.

paulieb89 Skill

Load a Primavera P6 XER file and get a quick progress summary. Returns activity count, status breakdown, and completion percentage. Use when someone uploads an XER file or says "what's in this schedule?" or "quick look at this XER". Requires the PyP6Xer MCP server to be connected.

akillness Bundle

Autonomously optimize an existing AI skill by running it repeatedly against binary evals, mutating one instruction at a time, and keeping only changes that improve pass rate. Based on Karpathy-style autoresearch, but applied to SKILL.md iteration instead of ML training. Use when optimizing a skill, benchmarking prompt quality, building evals for a skill, or running self-improvement loops on reusable agent instructions. Triggers on: skill-autoresearch, optimize this skill, improve this skill, benchmark this skill, eval my skill, run autoresearch on this skill, self-improve skill.

neronain Skill

Launch and interact with CAI (Cybersecurity AI) — an AI-powered offensive/defensive security framework with 300+ model support. Use for CTF challenges, penetration testing workflows, reconnaissance, and vulnerability research.

neronain Skill

Initialize a new Ruflo project with MCP tools, hooks, and agent configuration

neronain Skill

Discover and recommend ruflo plugins based on your workflow, installed MCP tools, and current task

neronain Skill

Route tasks to optimal agents using learned patterns, model recommendations, and confidence scoring

neronain Skill

Use this agent when you need to review code for adherence to project guidelines, style guides, and best practices. This agent should be used proactively after writing or modifying code, especially before committing changes or creating pull requests. It will check for style violations, potential issues, and ensure code follows the established patterns in CLAUDE.md. Also the agent needs to know which files to focus on for the review. In most cases this will be recently completed work which is unstaged in git (can be retrieved by running git diff). However there can be cases where this is different, make sure to specify this as the agent input when calling the agent. Typical triggers include the user asking for a review of a feature they just implemented, the assistant proactively reviewing its own newly-written code before declaring a task done, and a final pre-PR check before opening a pull request. See "When to invoke" in the agent body for worked scenarios.

neronain Skill

Use this agent when code has been written or modified and needs to be simplified for clarity, consistency, and maintainability while preserving all functionality. This agent should be triggered automatically after completing a coding task or writing a logical chunk of code. It simplifies code by following project best practices while retaining all functionality. The agent focuses only on recently modified code unless instructed otherwise. Examples: <example> Context: The assistant has just implemented a new feature that adds user authentication to an API endpoint. user: "Please add authentication to the /api/users endpoint" assistant: "I've implemented the authentication for the /api/users endpoint. Here's the code:" <function call omitted for brevity> <commentary> Since a logical chunk of code has been written (authentication feature), use the Task tool to launch the code-simplifier agent to improve the code's clarity and maintainability while preserving functionality. </commentary> assistant: "Now let me us

neronain Skill

Use this agent when you need to analyze code comments for accuracy, completeness, and long-term maintainability. This includes (1) after generating large documentation comments or docstrings, (2) before finalizing a pull request that adds or modifies comments, (3) when reviewing existing comments for potential technical debt or comment rot, and (4) when you need to verify that comments accurately reflect the code they describe. See "When to invoke" in the agent body for worked scenarios.

neronain Skill

Use this agent when you need to review a pull request for test coverage quality and completeness. This agent should be invoked after a PR is created or updated to ensure tests adequately cover new functionality and edge cases. Typical triggers include the user asking whether tests on a freshly-created PR are thorough, an updated PR adding new logic that needs coverage analysis, and a final pre-merge double-check before marking a PR ready. See "When to invoke" in the agent body for worked scenarios.

neronain Skill

Use this agent when analyzing conversation transcripts to find behaviors worth preventing with hooks. Typical triggers include the /hookify command being invoked without arguments, or the user explicitly asking to look back at the current conversation and surface mistakes that should be prevented in the future. See "When to invoke" in the agent body for worked scenarios.

neronain Skill

Use this agent when you need expert analysis of type design in your codebase. Specifically use it (1) when introducing a new type to ensure it follows best practices for encapsulation and invariant expression, (2) during pull request creation to review all types being added, and (3) when refactoring existing types to improve their design quality. The agent will provide both qualitative feedback and quantitative ratings on encapsulation, invariant expression, usefulness, and enforcement. See "When to invoke" in the agent body for worked scenarios.

understudy-ai Bundle

Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.

spoon94 Skill

Turn any MCP server, OpenAPI spec, or GraphQL endpoint into a CLI. Use this skill when the user wants to interact with an MCP server, OpenAPI/REST API, or GraphQL API via command line, discover available tools/endpoints, call API operations, or generate a new skill from an API. Triggers include "mcp2cli", "call this MCP server", "use this API", "list tools from", "create a skill for this API", "graphql", or any task involving MCP tool invocation, OpenAPI endpoint calls, or GraphQL queries without writing code.

spoon94 Bundle

OpenCLI 全能助手 - 把任意网站/Electron 应用/外部 CLI 变成 `opencli <site> <command>` 的统一操作面。当 agent 要读写已登录网站、驱动真实浏览器、抓取页面、填表点击、修复失效适配器、给新站点写适配器、维护站点 sitemap、或把搜索/查询路由到最佳数据源时，都用这个 skill。这是合并了 7 个官方 opencli 子技能（usage/browser/adapter-author/sitemap-author/browser-sitemap/autofix/smart-search）的统一入口，按任务类型路由到对应章节或 reference。

spoon94 Bundle

基于本地 engram CLI(SQLite + FTS5)的跨会话持久记忆,工作与生活通用。当用户想 「记住/记忆/记一下/存一下/别忘了/下次记得」某件事——一个决策、一次 bug 修复、一个 偏好、一个计划、一个人、一件事、一个推荐(餐厅/书/工具)——或想「回忆/想不起来/ 我们之前怎么…/上次那个…怎么弄的/我之前记过吗/有没有处理过类似的」时,都要使用本技能。 关键词包括「记忆、记住、记一下、想起来、回忆、笔记、之前、上次」等;即使没明说也常 隐含存或搜记忆。场景不限于写代码,也覆盖日常生活(偏好、家人、计划、想法、推荐)。 由于 engram 全文检索只切英文,本技能在存/搜前把中文译成英文,同时双存中文原文以保真。 无 hook、无 MCP,纯 CLI。

compozy Bundle

Deep review of branch diffs, working trees, or GitHub PRs at any size. Use when the user asks for CodeRabbit-grade review, an incremental re-review after new pushes, publication of findings to a PR, a cross-LLM peer-review verdict round, or conformance review against spec artifacts. Don't use for applying fixes, reviewing specs or PRDs as documents, or quick single-file feedback.

compozy Skill

Enforces fresh verification evidence before any completion, fix, or passing claim, and before commits or PR creation. Use when an agent is about to report success, hand off work, or commit code. Do not use for early planning, brainstorming, or tasks that have not yet reached a concrete verification step.

compozy Bundle

Audits a backend AGH change for downstream impact on web/, packages/site, agent-operable CLI/HTTP/UDS surfaces, extensibility surfaces, and config.toml. Adds explicit impact subitems to backend tasks, even when the conclusion is no impact. Use when drafting tasks or reviewing TechSpec coverage for changes touching contracts, handlers, CLI verbs, config, extensions, hooks, skills, tools, resources, bundles, registries, bridges, MCP, or agent workflows. Do not use for purely internal refactors with no public, agent, config, docs, or extensibility surface.

compozy Bundle

Cross-LLM peer review of a TechSpec via compozy exec — scoped Markdown findings for user-directed incorporation. Use when a TechSpec draft has already been approved and the user wants an external review round, especially for autonomy/network/memory-impacting designs. Do not use for PRDs, automatic approval gates, code review batches, or auto-looped review cycles.

compozy Bundle

Orchestrate Claude and Codex worker TUIs from a controller agent through herdr panes and the herdr socket CLI. Use when delegating bounded tasks to herdr worker panes, running user-activated plan-first delegations (Claude Code plan mode, Codex Plan mode), waiting on native agent status (idle, working, blocked, done), or verifying worker reports. Workers launch as interactive TUIs via herdr agent start — never through headless runners (compozy exec, claude -p, codex exec). Not for cmux workspaces (see cmux-orchestration) and not for end-user herdr control.

tobihagemann Skill

Run a multi-agent review of changed files for scope, reuse, quality, efficiency, clarity, and altitude issues followed by fixes. Use when the user asks to "simplify code", "review changed code", "check for code reuse", "review code quality", "review efficiency", "simplify changes", "clean up code", "refactor changes", or "run simplify".

ww-w-ai Bundle

Phase 1: Data Modeling and Terminology Definition. Define database schema, entity relationships, and project glossary. Next: $phase-2-convention. Triggers: schema, data model, entity, ERD, database design, terminology, 스키마, 데이터 모델, 엔티티, 용어 정의, スキーマ, データモデル, 用語, esquema, modelo de datos, schéma, modèle de données, Datenmodell, Schema Do NOT use for: API design (use $phase-4-api), coding rules (use $phase-2-convention).

ww-w-ai Bundle

bkend.ai BaaS quick start guide. Project setup, MCP connection, and first API call. Get started with bkend.ai backend-as-a-service in minutes. Triggers: bkend setup, bkend start, BaaS setup, backend setup, first project, bkend 시작, bkend 설정, 백엔드 설정, bkend開始, bkendセットアップ, bkend开始, bkend设置, empezar bkend, demarrer bkend, bkend starten, iniziare bkend Do NOT use for: auth details (use $bkend-auth), data modeling (use $bkend-data).

kevin-burns Bundle

Write findings, clipped material and long-form documents into a self-hosted Trilium Notes instance over its native MCP — filed under a project, labelled with a controlled vocabulary, and revised in place rather than duplicated. Use when the user says to store, capture, save or file something in Trilium; when a research finding or an article should outlive the session; or when a document produced with the user (a plan, a draft, a report) should live in their notes instead of on one machine. Do NOT use for short recallable facts an agent needs mid-task — those belong in Ogham shared memory.

kevin-burns Bundle

Transcribe audio locally on macOS, Windows or Linux, and optionally write it up as meeting notes. Use whenever the user has an audio or video file and wants what was said - "transcribe this", "what was said on the call", "turn this recording into notes", "write up this meeting", "subtitle this", "get me an .srt", a voice memo, a dictation, an interview, a lecture, a podcast. Runs on-device by default with Whisper (mlx-whisper on Apple Silicon, faster-whisper everywhere else, Parakeet when asked for); Groq, OpenAI and ElevenLabs Scribe are available but only when named explicitly, and never without first stating what will be sent and what it will cost. Normalises and silence-trims the audio with ffmpeg before decoding, which measurably beats reaching for a bigger model, and filters the fabricated segments Whisper invents over silence. Also produces a factual meeting-notes document and a PDF. Not for writing prose or summarising text you already have.

bobmatnyc Skill

Essential Git patterns for effective version control, eliminating redundant Git guidance per agent.

bobmatnyc Skill

Best practices for documenting APIs and code interfaces, eliminating redundant documentation guidance per agent.

bobmatnyc Skill

Adversarial code review rubric — severity taxonomy, the 80% confidence filter, and the APPROVE/WARN/BLOCK verdict protocol. Loaded by the code-critic agent as its primary review reference.

bobmatnyc Skill

MCP (Model Context Protocol) server build and evaluation guide, including local conventions for tool surfaces, config, and testing

drunkcoding Bundle

Per-agent persistent memory for oh-my-open-code. Stores and retrieves knowledge across sessions using agent_id scoping — each agent (oracle, librarian, explore, sisyphus, deep, etc.) maintains its own memory namespace while allowing cross-agent search. Primary use case: persisting deep research findings, architecture decisions, library patterns, and codebase knowledge. Triggers on: "remember", "store this", "what do we know about", "recall", "search memory", "persist findings", or any need to retain knowledge across sessions.

drunkcoding Bundle

Interactive quiz tutor for a CUDA StudyVault built by `cuda-tutor-setup`. Delivers 4-question rounds with concept-level proficiency tracking (🟥/🟨/🟩/🟦/⬜) across the 6 CUDA learning topics: CUDA kernels (threads/blocks/warps, memory hierarchy, TMA, WGMMA, cp.async), CUTLASS + CuTe (Layout/Stride/Tensor, MMA atoms, GEMM pipelines), cuTile (Python-first tile DSL), open-gpu-kernel-modules (RM, GSP firmware, UVM, kernel-open layout), NCCL (collectives, topology, NVLink-SHARP, transports), and NVSHMEM (PGAS, symmetric heap, IBGDA, on-stream API). Use when the user wants to (1) take a diagnostic CUDA assessment, (2) drill weak GPU concepts, (3) study a specific CUDA topic, (4) review the learning dashboard, or says things like "quiz me on CUDA", "test my CUTLASS knowledge", "drill NCCL", "/cuda-tutor", "퀴즈".

drunkcoding Bundle

Generate a university-style course pack (PDF lecture handouts, programming-assignment writeups, conceptual problem sets, topic cheatsheets, a capstone project, and a one-page syllabus) plus matching graded programming-exercise scaffolds (CUDA / Triton / PyTorch starters, a LeetGPU-style autograder harness, and gated solutions for hardest exercises) from a `StudyVault/` produced by `cuda-tutor-setup` or `triton-tutor-setup`. Visual style modeled on CMU 15-418, Stanford CS149, MIT 6.172, OLCF CUDA Training Series, and AlphaGPU LeetGPU challenges. Emits `.tex` + `Makefile`; compiles via `latexmk + pdflatex` if available, otherwise gracefully falls back to source-only. Use when the user wants to produce printable lecture handouts, programming assignments, problem sets, an autograder, or a complete course pack from an existing StudyVault, or says things like "make handouts for my StudyVault", "generate the course pack", "build programming exercises for CUDA", "/tutor-handouts".

drunkcoding Bundle

Generate an Obsidian-format CUDA StudyVault for learning NVIDIA's GPU programming stack. Three modes: (1) Curriculum mode (default, no source files needed) builds a prereq-ordered vault around the 6-topic learning path: CUDA kernels (threads/blocks/warps, memory hierarchy, TMA, WGMMA, cp.async), CUTLASS + CuTe (Layout/Tensor, MMA atoms, GEMM pipelines), cuTile (Python tile DSL), open-gpu-kernel-modules (RM, GSP firmware, UVM, kernel-open), NCCL (collectives, NVLink-SHARP, transports), and NVSHMEM (PGAS, symmetric heap, IBGDA); (2) Codebase mode generates an onboarding vault from a real CUDA/CUTLASS/NCCL/NVSHMEM/driver source tree; (3) Document mode turns NVIDIA whitepapers, GTC slide decks, and PDFs into study notes. Mode is auto-detected from CWD markers. Use when the user wants to learn CUDA from scratch, onboard onto a GPU codebase, build an exercise list for CUDA topics, or preprocess NVIDIA PDFs/docs into structured study material. Pair with the `cuda-tutor` skill to quiz against the resulting vault.

drunkcoding Skill

Guide for writing SGLang CI/UT tests. Covers CustomTestCase, CI registration, server fixtures, model selection, mock testing, and test placement. Always read test/README.md for the full CI layout, how to run tests, and extra tips. Use when creating new tests, adding CI test cases, writing unit tests, or when the user asks to add tests for SGLang features.