AI & ML

robertoatila Skill

Multi-agent conversational orchestration framework for autonomous collaborative LLM task execution.

robertoatila Skill

Newest, latest and most advanced model that is able to control.

robertoatila Skill

Creates adversarial red-teaming plugins, prompt injection tests, and safety graders.

robertoatila Skill

Maintains persistent task_plan.md, findings.md, and progress.md files across agent sessions.

robertoatila Skill

Fine-tunes and serves OpenPI physical intelligence models for robot policy inference.

robertoatila Skill

Reference-free preference optimization for efficient LLM alignment without a reference model.

robertoatila Skill

Nuclear-grade 16-agent pre-publish release gate. Runs /get-unpublished-changes to detect all changes since last npm release, spawns up to 10 ultrabrain agents for deep per-change analysis, invokes /review-work (5 agents) for holistic review, and 1 oracle for overall release synthesis. Runs ONLY when the user explicitly asks for a pre-publish review — a plain publish/release request MUST NOT trigger this; /publish ships directly. Triggers: 'pre-publish review', 'review before publish', 'release review', 'pre-release review', 'ready to publish?', 'can I publish?', 'pre-publish', 'safe to publish', 'publishing review', 'pre-publish check'.

robertoatila Skill

Multi-model AI streaming and fallback router across 200+ LLMs via OpenRouter.

robertoatila Skill

Runs promptfoo evaluation suites, model-graded rubrics, and regression test matrices.

robertoatila Skill

Trains and serves Mixture of Experts (MoE) models with DeepSpeed and HuggingFace.

robertoatila Skill

Activation-aware weight quantization for 4-bit LLM compression with low latency.

robertoatila Skill

Store a dag definition once and re-run it in one or two lines, instead of pasting the full definition JSON into every eval cell. MUST USE whenever the user wants to save a DAG for reuse, run a previously saved/named DAG, schedule the same graph repeatedly (nightly/weekly audits, recurring multi-agent pipelines), or asks where to put a dag definition file. Triggers: dag library, save this dag, reuse a dag, run the saved dag, stored dag definition, recurring dag, nightly dag, dag 정의 저장, 저장된 dag 실행, dag 반복 실행, DAG 만들어두고 여러 번.

robertoatila Skill

Escalation skill for blocked or hard-to-reach web access — load it when a normal browse/fetch is blocked (WAF, 403, Cloudflare, JS-only render, login-gated, or a platform a generic fetcher cannot read). Tiered router: TIER 1 insane-search (headless extraction + WAF bypass via curl_cffi TLS impersonation, yt-dlp, Jina Reader, public APIs, Playwright real-Chrome fallback); TIER 1.5 agent-reach (platform-native readers for Chinese and social platforms: Xiaohongshu, Douyin, Weibo, Bilibili, V2EX, WeChat, plus Twitter/Reddit/LinkedIn/GitHub); TIER 2 Chrome stealth (CloakBrowser stealth Chromium + agent-browser CDP for clicks, forms, screenshots, video, cookie login). Triggers: blocked site, bypass bot detection, cloudflare/WAF bypass, scrape, stealth browser, import cookies, fill form, screenshot, play youtube, xiaohongshu, douyin, weibo, bilibili, v2ex, wechat article, podcast transcript. NOT for simple searches (use web-search) or plain fetches (use webfetch).

robertoatila Skill

Binding ultrawork mode directive for omo-senpi. When a prompt contains ultrawork or ulw, the omo input hook injects the full directive as a hidden custom message (customType omo-ultrawork:directive, display false) ahead of the user's text, which is left untouched; a prompt queued while the agent is streaming instead carries the directive appended inside that same message. The directive is present in the conversation context; on the idle path it is not shown in the visible prompt, while a queued prompt carries the directive visibly (exactly as before this change). When the directive is already present in the conversation, do not read this file again - this file is that same directive. Read this file only when ultrawork mode is requested and the directive is not already present in the conversation.

robertoatila Skill

Run a dependency graph of child agents in one call with the native workflow tool. Use when the user asks for mass-ulw, a DAG of tasks, fan-out/fan-in work, or multi-agent execution where some tasks must wait on others.

promptpartner Skill

Check whether this project's Agentsmith harness is installed correctly and healthy — fires on "is my harness set up right?", "harness doctor", "check my harness". Part of the Agentsmith harness; checks each selected agent's managed rules, settings, skills, hooks, verification, and leanness with a one-line fix for each finding.

robertoatila Skill

Merges multiple fine-tuned LLMs using mergekit without retraining or compute overhead.

robertoatila Skill

High-throughput LLM serving with PagedAttention, vLLM continuous batching, and quantization.

robertoatila Skill

Prunes neural network weights for sparsity and inference acceleration using Wanda/SparseGPT.

robertoatila Skill

Extends transformer context windows using RoPE, YaRN, ALiBi, and position interpolation.

legalquants Bundle

Use when the user has typed a short or vague prompt and the system is configured to expand prompts before submission, or when the user explicitly invokes "Enhance Prompt" or asks the system to "improve this prompt before sending." Rewrites the user's input into a structured legal prompt with role, jurisdiction, task, constraints, and output format made explicit, and returns the expansion alongside a brief reasoning section so the user can review, edit, or skip before the expanded prompt is submitted to the model.

clay-run Skill

Clay workflows — simplify a workflow via the CLI (`clay workflows` commands): merge redundant nodes, cut unnecessary complexity, and replace LLM nodes with deterministic alternatives where possible.

robertoatila Skill

High-throughput LLM serving with RadixAttention prefix caching and structured decoding.

robertoatila Skill

Fast, memory-efficient LLM fine-tuning using Unsloth LoRA/QLoRA optimized kernels.

robertoatila Skill

Tracks ML experiments, versioning, artifacts, and production deployments with MLflow.

robertoatila Skill

Extracts structured data from LLMs with Pydantic type validation and retries.

clay-run Skill

Clay workflows — reduce a workflow's credit and LLM cost via the CLI (`clay workflows` commands). Identifies expensive patterns and suggests cheaper alternatives.

promptpartner Skill

Orient a non-coder to this project's Agentsmith harness — fires on "what is this harness?", "what are my rules?", "which profile am I on?", "what do I type next?". Part of the Agentsmith harness; reads the canonical instructions and selected agent's safety configuration, then reports the profile, rules, safety mode, and next step in plain language.

promptpartner Bundle

Write or review anything an agent reads — a core rule, a profile gate, a SKILL.md description, an instruction-file line, a subagent prompt, a handoff note, a verify-phase label. Part of the Agentsmith harness; supplies the levers that decide whether a line changes behaviour or only costs tokens — the two loads, context pointers, the ladder, completion criteria, leading words, the no-op test. Its default move is deletion, so it earns most on a draft that already exists.

robertoatila Skill

Quantizes and serves models in GGUF format for efficient CPU/GPU inference.

robertoatila Skill

Evaluates LLM safety guardrails, jailbreak resistance, and toxicity using NVIDIA NeMo.

robertoatila Skill

Infers gene regulatory networks from bulk and single-cell transcriptomics data.

robertoatila Skill

Accelerates LLM inference throughput using speculative decoding and Medusa multi-head draft prediction.

shenseanchen Skill

Connect Waku Memory, hosted cross-agent memory in Claude Code, Codex, Grok Bot, Hermes. Export Waku skills.

shenseanchen Skill

Add a new tool to Waku the right way — schema, safe execution, deterministic eval, honest output. Use when adding or modifying agent tools or when the user asks for a new capability.

shenseanchen Skill

Walk Sean through an incoming waku-agent PR or issue and present it his way — four fixed sections: what this is, why it matters, how HE can test it with you as copilot, and are we ready to merge / reply / close and why. Use whenever Sean asks to look at, test, triage, or decide on a pull request or an issue, and whenever a queue of them is being drained one at a time.