39 boosters for "transform" — open source, verified from GitHub, ready to install
Proposal Strategist transforms RFPs into persuasive win narratives through strategic positioning, compelling themes, and executive summary craft. Sales teams, proposal managers, and business development professionals use it to move beyond compliance-driven responses to buyer-centric storytelling.
Feedback Synthesizer is an agent that collects and analyzes user feedback from multiple channels to extract actionable product insights and prioritization recommendations. Product managers, designers, and engineering teams benefit from converting qualitative feedback into data-driven strategic decisions.
Analytics Reporter is an AI agent that transforms raw data into actionable business insights through statistical analysis, dashboard creation, and KPI tracking. Data teams and business analysts use it to generate strategic reports and data-driven recommendations.
A specialized presales advisor for Chinese government digital transformation projects, helping technical teams navigate procurement policies, design solutions, and win bids through expertise in compliance, policy interpretation, and stakeholder management.
A specialized agent that transforms complex technical concepts into clear, developer-friendly documentation for APIs, READMEs, and tutorials. Ideal for engineering teams, open-source maintainers, and anyone who needs to document code quickly and effectively.
Transforms complex business information into polished executive summaries using proven consulting frameworks (SCQA, Pyramid Principle) tailored for C-suite decision-makers. Ideal for consultants, strategists, and business leaders who need to distill lengthy analyses into actionable insights quickly.
This skill enables users to generate and edit images directly within Claude Code using the OpenAI Image API, supporting use cases from product mockups to concept art. Developers and designers benefit by automating image creation workflows without leaving their coding environment.
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no server required. Use this skill when you need to: The pipeline API is the easiest way to use models. It groups together preprocessing, model inference,
This skill is for running evaluations against models on the Hugging Face Hub on local hardware. It does not cover: If the user wants to run the same eval remotely on Hugging Face Jobs, hand off to the skill and pass it one of the local scripts in this skill.
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
This skill enables AI assistants to create, configure, and manage datasets on Hugging Face Hub with SQL-based querying and transformation capabilities. It's valuable for developers building data workflows and ML projects that require programmatic dataset management.
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
Automates GitHub pull request analysis by gathering diffs, comments, related issues, and local code context to provide comprehensive reviews. Developers and code reviewers benefit from faster, more thorough PR evaluations.
MIRIX is a Multi-Agent Personal Assistant with an Advanced Memory System. It features a six-agent memory architecture (Core, Episodic, Semantic, Procedural, Resource, Knowledge Vault) with screen activity tracking and privacy-first design. 1. Follow PEP 8 strictly 3. Documentation
Real-time monocular depth estimation using Depth Anything v2. Transforms camera feeds with colorized depth maps — near objects appear warm, far objects appear cool. When used for privacy mode, the blend mode fully anonymizes the scene while preserving spatial layout and activity, enabling security
Multi-pattern search/replace tool for bulk refactoring with simultaneous replacements, file/directory renaming, and case-preserving transformations. Then execute if output looks correct:
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
JN is a command-line ETL tool for data transformation using NDJSON as a universal format, enabling developers to filter, convert, and stream data across CSV/JSON/Excel/YAML formats with Unix pipes.
Transform a Vibes app into a multi-tenant SaaS with subdomain-based tenancy. Adds Clerk authentication, subscription gating, and generates a unified app with landing page, tenant routing, and admin dashboard.
You are an expert in prompt engineering and systematic application of prompting frameworks. Help users transform vague or incomplete prompts into well-structured, effective prompts through analysis, dialogue, and framework application. When a user provides a prompt to improve, analyze across dimensi
<!-- Canonical source: AGENTS.md — keep this file in sync --> stream-chain creates a chain of streams out of regular functions, asynchronous functions, generators, and existing streams, while properly handling backpressure. The result is a Duplex stream. It is a lightweight, zero-dependency micro-pa
"name": "orchestrator", "description": "Autonomous Development Orchestrator - Transform ideas into production-ready applications through multi-agent pipeline. Spec → Plan → Tasks → 100% Working App.", "repository": "https://github.com/devsforge/orchestrator",
"name": "ralph-dev", "version": "0.4.10", "description": "Autonomous end-to-end development system - from requirement to production-ready code with zero manual intervention",
