Results for “retellai”

Reverse shell generation and listener management. Generates platform-specific reverse shell payloads (bash, python, php, powershell, java, ruby, perl, netcat, socat, msfvenom) and sets up listeners in the Kali container. Supports one-liner generation, encoded payloads for WAF/filter bypass, listener setup with session capture, and shell stabilization. Chains from /pentester, /metasploit, or /post-exploit when command execution is confirmed.

Fine-tune and align language models using reinforcement learning with TRL, including SFT, DPO, PPO, GRPO, and reward model training.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Re-renders a subject or scene from multiple dramatic camera angles while maintaining consistent identity and detail.

Pauses mid-conversation to reassess direction, assumptions, and bias, then recommends whether to continue, pivot, or pause.

Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.

Template do pack (ai-ml/03-rag-pipeline.md). Orienta o agente em integracao de IA/ML, LLM e pipelines de dados alinhado a esse contexto.

Use when building, fixing, polishing, or comparing any terminal UI — ratatui/crossterm REPLs, agent CLI chrome — or when a TUI shows a frozen spinner, dead keys, duplicated responses, stale cells, flicker, resume failures on dangling tool_use, or when you need side-by-side screenshots against reference CLIs.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Train, evaluate, distill, quantize, export, and run inference for RT-DETR object detection models using NVIDIA TAO.

Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning) with support for SFT, DPO, GRPO, KTO, RLOO, and reward model training via CLI commands.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.

セッション再開時に最新5件のhandoffを読み込み文脈を復元するスキル。「おはよう」「こんにちは」「こんばんは」「再開」「restart」または /resume-session を呼んだ時にトリガーする。new-session の対（読込側）。

Render videos from React/Remotion component code via inference.sh CLI. Pass TSX code, get MP4 output with configurable resolution, FPS, duration, and codec.

Use when the team needs a strategy for validation errors, API failures, job retries, user-facing error UX, observability, or recovery behavior. Trigger on requests to design error handling across frontend, backend, and integrations.

`task-agent`/`review-agent`: use when broker delivery, ordering, acknowledgement, DLQ, backpressure, or replay changes; skip synchronous retry without message semantics.

`analysis-agent`/`task-agent`/`review-agent`: use for recurrence guards on known defects, incidents, or escaped failures; skip speculative risk without a prior failure mechanism.

Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. Reads the 'why' from real user operation logs. Works with Field/Echo for persona validation.

Design reading plans with retention strategies. TRIGGERS - Use when user needs help with reading-plan related tasks.

Trigger: circuit breaker, exponential backoff, jitter delay, connection timeout, API retry. Scope: Client request resiliency, API retries, fault isolation. Boundary: Excludes visual state loading skeletons.

Optimizes React and Next.js performance with 69 prioritized rules covering waterfalls, bundle size, server-side rendering, and re-rendering patterns.

Post-train LLMs with reinforcement learning using the slime framework, which integrates Megatron-LM for training and SGLang for rollout generation.

Watches conversations continuously and compresses them into prioritized notes, consolidating and recovering missed sessions with multiple redundancy layers.

Use quando o usuário pede para executar Codex CLI (codex exec, codex resume) ou referencia OpenAI Codex para análise de código, refatoração ou edição automatizada. Usa GPT-5.2 por padrão para engenharia de software de ponta.

Quick reference for Redis best practices across common backend use cases, including caching strategies, rate limiting, distributed locks, pub/sub, streams, and connection management.

Queries the Metabolomics Workbench REST API to retrieve metabolite, study, and RefMet data in JSON or text formats.

Polls a local relay for animation-refinement jobs from the Timeline Inspector, posts suggestions or applies changes to transition code.

Generates specific, implementable fixes for each vulnerability finding, producing code patches, configuration changes, dependency updates, and IaC fixes with before/after code and verification steps.

SENTINEL v2.0 — Quality Trader Convergence Scanner. Inverted pipeline: find ELITE/RELIABLE traders, see where they converge. When 5+ quality traders hold the same asset in the same direction, enter.

Called by /plot to upgrade regression and summary tables to top-journal standards.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Text-to-speech and speech-to-text using fal.ai audio models

Interact with Atlassian Jira from the terminal: search issues, view details, create issues, add comments, list projects, and transition status. Use when the user mentions Jira, a ticket key (e.g. PROJ-123), or asks about issues, bugs, tasks, projects, or sprint work.