54 boosters for "nsf" — open source, verified from GitHub, ready to install
doc-coauthoring guides users through a structured three-stage workflow (context gathering, refinement, and reader testing) for collaboratively creating technical documentation, proposals, and specs. It benefits technical writers, engineers, and product managers who need to efficiently co-author structured content.
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.
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.
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.
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.
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.
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.
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.
Fast, calibration-free weight quantization supporting 8/4/3/2/1-bit precision with multiple optimized backends. HQQ uses to define quantization parameters: The core quantized layer that replaces :
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 :
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,
Tiebreakers when the request is ambiguous: "embedding model" / "vector search" / "similarity" → [SentenceTransformer]. "rerank" / "ranker" / "two-stage" → [CrossEncoder]. "SPLADE" / "sparse" / "inverted index" → [SparseEncoder]. If still unclear, ask. Override only if the user specifies otherwise: T
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
You are an expert at using the TRL (Transformers Reinforcement Learning) library to train and fine-tune large language models. TRL provides CLI commands for post-training foundation models using state-of-the-art techniques: TRL is built on top of Hugging Face Transformers and Accelerate, providing s
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.
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.
Enables developers to interact with Hugging Face Hub directly from Claude Code using the `hf` CLI—downloading models/datasets, uploading files, creating repositories, and managing cache without leaving the coding environment.
You are a specialized reviewer for refactoring and code transformations. Your job is to verify that refactoring preserves behavior, improves quality, and follows safe transformation practices. You render verdicts on refactoring quality. Before reviewing, frame the question space E(X,Q): Your task pr
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
Improve the clarity and voice of AI-assisted academic writing while keeping the precise, evidence-bound voice that scholarship requires and matching the author's own style. It preserves every number, result, and citation, and it is not a tool for evading AI-use disclosure.
Procedural memory for AI coding agents. Transforms scattered sessions into persistent, cross-agent memory. Uses a three-layer cognitive architecture that mirrors human expertise development. AI coding agents accumulate valuable knowledge but it's: You've solved auth bugs three times this month acros
Multi-pattern search/replace tool for bulk refactoring with simultaneous replacements, file/directory renaming, and case-preserving transformations. Then execute if output looks correct:
