AI & ML

luokai0 Skill

Legaldoc Ai

luokai0 Skill

Bountyhub Agent

luokai0 Skill

Messageguard

luokai0 Skill

Gmail Last5

luokai0 Skill

Token Guard

luokai0 Skill

Crypto Hackathon

luokai0 Skill

Brain Search

luokai0 Skill

Scent Trails

luokai0 Bundle

SpecKit Safe Update

luokai0 Skill

Crewmind Bets

luokai0 Skill

Publish Guard

luokai0 Skill

Super Browser

luokai0 Skill

Sentiment Score

luokai0 Bundle

Use when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the bundled `scripts/render_docx.py` for visual checks.

luokai0 Skill

Agent Team Kit

luokai0 Skill

Sports Betting

luokai0 Skill

Bluesky

luokai0 Skill

Advisory Council

luokai0 Skill

Agent Reputation

luokai0 Skill

Free Stack Builder

luokai0 Skill

Raiffeisen Elba

luokai0 Skill

Listonic

luokai0 Skill

Mupibox Media Db

luokai0 Skill

Sota Tracker Mcp

luokai0 Skill

Consensus Interact

taosdata Bundle

IDMP总控编排技能 - 核心枢纽技能。根据用户需求（无论是无文件输入的场景Demo生成，还是带有文件/明确业务数据的具体接入），实现"一键式"自动化编排。静默调度 idmp-sample-data-generator、MCP 可视化工具链和 idmp-analysis-creator 完成全流程（行业调研 -> 数据生成 -> 告警分析 -> 面板创建）。

taosdata Bundle

IDMP datasource diagnosis workflow. Read the connection, probe connectivity, inspect databases and metadata, compare model mappings, and reread after every probe or write.

iamneilroberts Bundle

Delegate a task or question to the OpenAI Codex CLI running as an isolated subagent, then relay its answer. Use when the user invokes /codex <prompt>, says "ask codex", "have codex look at / do X", or wants a second independent model to investigate a question or make a self-contained edit. Read-only by default; /codex --write lets Codex edit the working tree. Distinct from /codex-review, which is the structured JSON review gate — /codex is a general-purpose Codex delegate.

iamneilroberts Skill

The top-level review verb — decide WHETHER, WHEN, WHICH, and HOW HARD to review a change before reaching for a review tool. Assesses the situation (a plan vs a diff, the risk surface, what changed since the last review, rounds already spent), picks the lightest review shape the risk justifies (in-session self-review → cross-model → multi-model panel), sets an initial round budget it will extend only on convergence, and dispatches. Exists to stop review spirals: reviewing before the plan is formed, reviewing after every trivial edit, or looping a reviewer past the point of convergence. Triggers on `/review`, "should I review this", "what review does this need", "is this worth a codex-review / panel".

iamneilroberts Bundle

Tear down a third-party product/tool/library from a single URL and answer two questions: (1) is the product worth adopting as-is — installed into your Claude Code workflow (a CLI, MCP server, skill, or dotfile) or otherwise kept around — and (2) are there features or code worth lifting into your current repo. Most evaluations are hunting for one of those two wins. It resolves the URL to its source (repo, README, launch post — with Reddit and paywalled-fetch fallbacks), characterizes the product from its docs, clones the code locally when it's open source, then fans out read-only subagents — each handed a subset of the codebase plus your repo's context — to find liftable features with file:line evidence and portability notes. The calling session merges the findings into a verdict (adopt / lift / partial / skip) with per-finding analysis and writes a report to docs/evaluations/. Use for "is this repo worth anything to me?", "should I start using this", competitive teardowns, "should we adopt X", "evaluate this

iamneilroberts Bundle

Use when a session is about to bulk-read 3+ files or any single file over ~400 lines, generate predictable boilerplate (tests, fixtures, config scaffolds, doc templates), or compress a long session transcript — and a cheap OpenAI-compatible worker model (Kimi, DeepSeek, OpenRouter, local Ollama) is available via the llm-ask/llm-write/llm-extract CLIs.

iamneilroberts Bundle

Run multiple independent code reviewers (Codex + Gemini + a fresh Claude) on the current diff or a plan, merge their structured verdicts, and apply a consensus-gated challenge round — returning a pass/fail exit code. Use for "panel review this", "get a multi-model review before I ship", "second-opinion review this diff", or /review-panel. Complements single-reviewer /codex-review.

iamneilroberts Bundle

Use when planning how to spend scarce or expiring access to a premium frontier-tier model (usage-capped, deprecation cliff, "how should I spend my remaining premium-model budget", 2x burn rate, model leaving the plan) — before starting any premium-model session or writing a spend plan.

iamneilroberts Skill

Use when asked about Claude Code workflow, prompt caching cost, session economics, CLAUDE.md hygiene, context-window management, plan mode, permission modes, hooks, skills, subagents, checkpointing/rewind, parallel sessions, worktrees, headless/CI usage, the Agent SDK, slash commands, MCP configuration, or "what's the right way to use Claude Code for X". Also use proactively BEFORE suggesting /model, /compact, fast-mode toggle, or editing CLAUDE.md mid-session, since those bust the system cache.

magnus919 Bundle

PhD-level expertise in data science, statistics, and machine learning. Use when the task requires rigorous statistical analysis, experimental design, causal inference, advanced modeling, research methodology, or data science project leadership. Load when the user asks about statistical methods, experimental design, model selection, A/B testing, hypothesis testing, power analysis, regression, causality, Bayesian analysis, or research methodology.

magnus919 Bundle

Progressive disclosure for what AI agents produce. Structure research outputs across three layers of increasing depth — Summary (key findings), Analysis Collection (per-dimension files), and Detailed Dossiers (source excerpts, raw data, transcripts) — so downstream agents and humans consume only as deeply as they need. Load this skill when organizing research outputs, building multi-agent research pipelines, or designing agent collaboration protocols.