OthmanAdi

Reusable skill for coding agents (Claude, GPT, etc.) to build, optimize, debug, and productionize AI agents and coding agents in Rust. Embeds Rig/ADK patterns, Tokio async architecture, tracing observability, performance techniques, erro...

Reusable skill for coding agents to build RedGifs integrations, API clients, scrapers, bulk downloaders, and media tools. Incorporates the unofficial Python wrapper, auth flow, endpoints, rate-limit handling, and cross-language patterns ...

Find niches, topics, audiences, products, channels, and content angles via deep web search. Use whenever Ahmad says "find me a niche", "hunt niches in X", "what's an underserved audience for Y", "find similar sites/videos to <example>", "research the X market", "what are people building in Y space", "/niche-hunter X", or any phrasing that asks for niche / market / topic / audience / product / channel discovery across the open web. Trusts domain + keyword search; chains web_search → web_fetch / web_extract / web_crawl + reddit_search + search_youtube + github_repo_intel. NEVER uses awesome-list shortcuts. Output shape is FLEXIBLE — list, table, report, suggestions, or normal prose — picked from how Ahmad phrased the ask.

Remove AI-generated writing patterns from Egyptian Arabic (عامية مصرية / Masri) text. Use when editing or reviewing Egyptian dialect text to make it sound authentically human-written.

Remove AI-generated writing patterns from Modern Standard Arabic (MSA/الفصحى) text. Use when editing or reviewing Arabic formal text to make it sound naturally human-written.

Remove AI-generated writing patterns from Levantine Arabic (الشامي — Syrian, Lebanese, Palestinian) text. Use when editing or reviewing Levantine dialect text to make it sound authentically human-written.

Remove AI-generated writing patterns from Modern Hebrew (עברית מודרנית) text. Use when editing or reviewing Hebrew text to make it sound naturally human-written.

Scrub English drafts for AI tells before publishing. Use for any English LinkedIn post or newsletter draft right after composition and BEFORE export. Removes em-dash-as-pause, weak hedges, rule-of-three patterns, AI vocabulary, negative parallelisms, and inflated symbolism. Triggers on "polish", "humanize", "make this sound human", "/polisher", or after any English composer output. Leaves Arabic and Hebrew drafts untouched — humanizer-* skills handle those.

Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session recovery after /clear.

Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session recovery after /clear.

Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session recovery after /clear.

Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session recovery after /clear.

Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Hermes adaptation with minimal notes.

Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session recovery after /clear.

Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session recovery after /clear.

نظام تخطيط الملفات بنمط Manus لتنظيم وتتبع تقدم المهام المعقدة. ينشئ ملفات task_plan.md و findings.md و progress.md. يُستخدم عند طلب التخطيط أو تحليل المهام أو تنظيم المشاريع أو تتبع التقدم أو الخطط متعددة الخطوات. يدعم الاستعادة التلقائية للجلسة بعد /clear. كلمات التشغيل: تخطيط المهام، إدارة المشاريع، خطة العمل، تحليل المهام، تنظيم المشروع، تتبع التقدم، خطة متعددة الخطوات، ساعدني في التخطيط، تحليل المشروع

OpenUI generative UI with Anthropic Claude SDK backend. Stream conversion to OpenAI NDJSON format.

OpenUI generative UI with C# ASP.NET Core Minimal API backend. Direct OpenAI API SSE streaming via HttpClient on .NET 10.

OpenUI generative UI with Elixir Phoenix backend. Chunked SSE streaming via Plug.Conn and Req.

OpenUI generative UI with Go (net/http) backend. Direct OpenAI API streaming via HTTP.

OpenUI generative UI with a Java Spring Boot (WebFlux) backend. Streams the OpenAI API directly via WebClient as SSE.

OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.

OpenUI generative UI with OpenAI SDK backend. Streaming chat completions with gpt-5.5 (or any current OpenAI-compatible model).

OpenUI generative UI with a PHP (Laravel 13.x) backend. Forwards OpenAI's SSE stream verbatim via response()->stream().

Trigger the self-improvement loop after completing a task. Analyzes friction, extracts learnings, and updates AGENTS.md with new rules and preferences. Use after any task that involved struggle, correction, or discovery of a new pattern. Triggers on keywords like learn, self-improve, update rules, what did I learn, retrospective, post-mortem.

Decompose features from an existing codebase into detailed, agent-ready specifications for replatforming or migration. Use when analyzing legacy code, extracting feature behavior, mapping old architecture to new, or preparing migration specs. Triggers on keywords like decompose, analyze features, extract behavior, migration spec, map codebase.

Implement features in a new architecture using specifications from apex-decompose. Use when replatforming, migrating codebases, rebuilding features in a new framework, or translating specs into working code. Triggers on keywords like replatform, migrate, rebuild, implement spec, new architecture.

Execute well-defined tasks autonomously with zero human intervention. Fire-and-forget mode for straightforward work like adding tests, fixing lint errors, writing docs, or implementing simple features. Use when the task is clear, low-risk, and has objective pass/fail criteria. Triggers on keywords like fire and forget, autonomous task, simple fix, add tests, fix lint.

Execute complex tasks with periodic human checkpoints. Background work with steering — the agent works autonomously but pauses at defined decision points for human input. Use for medium-complexity tasks like feature implementation, refactoring, or multi-file changes. Triggers on keywords like guided task, checkpoint, background with review, implement feature, refactor module.

Real-time pair programming mode for complex architectural work. The agent and human collaborate interactively on design decisions, complex debugging, or exploratory work. Use for high-stakes tasks, new architecture design, critical bug investigation, or when every step needs discussion. Triggers on keywords like pair, collaborate, design together, debug with me, architect, explore.

Continuously bridges DESIGN.md into the active planning phase. When the user is working on a UI phase, reads the project's DESIGN.md, extracts the relevant token slice, and appends it to findings.md under "## Design Context" so the agent sees design constraints in its attention window. Never edits DESIGN.md, never edits task_plan.md. Idles when DESIGN.md is absent.

Harvests a DESIGN.md from an existing codebase, critiques it against the actual code, and applies safe quick-win fixes. Three modes harvest / critique / fix. Shells out to `npx @google/design.md lint` for structural validation; never reimplements Google's linter. The on-ramp that makes DESIGN.md adoptable for every existing repo, not just greenfield projects.

Three-tier memory layer for the orchestrator. Routes scratch (file-tier), WHERE (QMD), and WHY (MemPalace) through the existing code-memory-router slash command. Never edits planning files directly. Every WHY-tier promotion goes through the HITL gate.

Makes sessions durable across `/clear`, machine reboots, and IDE switches. SessionStart catchup pulls scratch + last MemPalace recall + planning files into context. Stop promotion writes a handoff note and runs memory-keeper. Manual `/mddesign:handoff` re-runs either at any time.

Turns a plan phase into a PhaseSpec v1 and dispatches it to a specialist subagent via the Agent tool. Subagent reads files itself, returns a structured result, the result is logged into progress.md. Markdown is the shared state; no in-memory shared store across agents.

AutoResearchClaw 23 阶段自主研究管线的中文技能。自动设置、交互式配置、执行监控和故障诊断。当用户提到 ResearchClaw、自主研究、论文生成、研究管线时使用。

Automate setup, configuration, execution, monitoring, and troubleshooting of AutoResearchClaw — the 23-stage autonomous research pipeline that generates conference-grade papers. Use when the user mentions ResearchClaw, wants to write a research paper autonomously, needs to set up or debug the pipeline, or says research paper, autonomous research, or paper generation.

AutoResearchClaw 23 阶段自主研究管线的中文技能。自动设置、交互式配置、执行监控和故障诊断。当用户提到 ResearchClaw、自主研究、论文生成、研究管线时使用。

Manus-style context engineering for Agent Teams. Coordinate multiple Claude Code instances with shared planning files. Use when complex tasks need parallel work (code review, debugging, feature development). Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1.

Deep-dive into any codebase and produce structured knowledge artifacts that turn a coding agent into a codebase specialist. This skill should be used when the user asks to "study this repo", "understand this codebase", "document this project", "onboard me onto this code", "create codebase knowledge", "map this architecture", or when asked to produce knowledge artifacts for any agent working on an unfamiliar repository.

A live visual Kanban board for long-running agentic tasks. Nobody wants to read a markdown plan or stare at raw HTML while an agent grinds for an hour, so Plandeck turns the plan into a board that organizes itself. Use it when a task is multi-step, long-running, or needs visual planning. Break the work into cards, declare which card depends on which, and Plandeck computes the critical path, auto-promotes cards to Ready as their dependencies clear, rolls up story-point estimates into an honest percent complete, and names the one next action. Plans stay plain files on disk, so they survive /clear and context resets. After a reset, run plandeck next to recover the single next move. Renders a live board in the browser over SSE. Triggers on plan a project, break down a task, kanban board, task board, visual plan, long-running agent, agentic task, track dependencies, critical path, story-point estimates, what is next, plandeck.

Connect and operate the Loophole Bridge (Ableton Live MCP server). Use when the user wants to check Live/bridge prerequisites, wire an MCP client to Loophole, or run a Live editing recipe (humanize, build arrangement, batch rename, chord from prompt). Triggers: "is my Ableton bridge working", "set up loophole", "connect Claude to Ableton", "/doctor", "/setup".

HTML-first persistent planning skill. Generates a single self-contained plan.html with interactive phases, drag-and-drop tickets, sliders, mockups, and embedded JSON state. Survives /clear via session catchup, tamper-protected by SHA-256, mirrors across 17 IDEs, ships 10 templates across Thariq's 9 categories, exports back to Markdown on demand. Use when asked to "plan it", "make me an html plan", "show me the plan", "render the plan", or when starting any multi-step task that needs a navigable artifact instead of a markdown wall.

Routes memory and retrieval tasks between MemPalace and QMD. Use this skill whenever the user asks WHERE something is in the codebase (route to QMD), WHY something was built a certain way or what was decided (route to MemPalace), or needs BOTH code and historical context (combined). Trigger on phrases like: 'where is X', 'find X in the code', 'search the codebase for X', 'what did we decide about X', 'why does X work this way', 'show me X and why we built it', 'look up X', 'what's in the handoffs about X', 'pull the relevant files for X'. Use proactively whenever a question could benefit from codebase search OR project memory — don't wait for the user to explicitly ask for a search.