13,737 mods in this category, of every kind an
agent can take. Each one carries what it costs per session, what the
scan found, and whether it is the original.
Turn document corpora (books, merged TXT, PDF/EPUB, OCR text) into structured agent knowledge: normalize → deterministic source segmentation → chapter maps with provenance → book skills → evaluation gates. Use when the user wants to convert books/materials into AI skills, build a knowledge base from a corpus, evaluate…
Spectral vector search using graph Laplacian eigenstructure. Build signal graphs, run λτ-indexed queries, and analyse spectral properties of vector datasets.
★not rated 2
changed 3d agoA34 tokens
originalApache-2.0
BridgeNode — x402 pay-per-request AI inference. OpenAI-compatible API + MCP server, Solana USDC, gas-free micropayments. No API keys. Free models included. Live prices: bridgenode.cc/v1/models. Use when an agent lacks a provider API key or wants privacy-preserving per-request AI inference pricing.
Write photorealistic live-action cinematic Seedance 2.0 prompts for Higgsfield, built on five grounding pillars that stop AI drift and floaty motion. Use for "cinematic film prompt", "shot like a movie", realistic body movement, grounded motion, restrained emotional close-ups, driving scenes, fight choreography…
A Chinese-language skill that turns a one-sentence video idea into a complete storyboard prompt after asking for missing details. It organises the prompt into basic settings, mood and image quality, and visual content for video models such as Seedance 2.0.
Design and ship a companion JSON-LD knowledge graph (graph.jsonld) next to llms.txt for projects with stable concept-level structure. Encodes domain entities and relationships as schema.org triples for LLM citation. Use when project has matrix / hierarchy / phase-binding structure that prose alone leaves implicit, AND…
Design production-grade agentic AI systems from a natural-language use case. Runs a clarification loop, walks grounded decision trees (workflow vs agent, RAG vs fine-tuning, single vs multi-agent, autonomy tiers), and emits a detailed enterprise system design document with embedded interactive architecture, sequence…
Fine-tune LLMs using Red Hat training-hub library with SFT, LoRA, and OSFT algorithms. Use when preparing JSONL datasets, running training jobs, configuring hardware, scaling to clusters, evaluating models, or deploying with vLLM.
Statistically test whether a prompt or SKILL.md change is actually better than the old version. Use when the user asks to compare two prompts, A/B test a prompt change, check if a recent edit really improved things, find rule conflicts in a long prompt, or identify which sections of a prompt are pulling weight.…
Generate synthetic and simulated datasets for evaluation and fine-tuning using Azure AI Foundry simulators. Create non-adversarial task data, adversarial safety data, and conversation datasets without manual data collection.
A bridge to Google's Gemini service that lets a coding agent analyze images, videos, and audio when its main model cannot handle those formats directly.
Production readiness review for applications incorporating AI / ML — covering classical ML models, generative models, LLM-powered applications, RAG systems, and agentic workflows. Checks AI system inventory, EU AI Act classification, training data governance, model supply chain, evaluation harness, prompt injection…
Comprehensive prompt engineering framework for designing, optimizing, and iterating LLM prompts. This skill should be used when users request prompt creation, optimization, or improvement for any LLM task, or when users need help translating vague requirements into effective prompts through collaborative dialogue and…
Create and optimize reusable prompt templates for AI assistants and autonomous agents. Use when user says "make a prompt for this task", "improve this prompt", "write a prompt template", "create a prompt for [task]", "optimize this prompt", or requests help designing instructions for LLMs or agents. Also trigger when…
Use when a raw prompt needs rewriting or sharpening, when the user says "rewrite this prompt", "make this prompt better", or asks for a prompt to hand to another agent. Also use before launching any autonomous, long-running, parallel or expensive run (/loop, subagent fan-out, worktrees, overnight work), and whenever a…
A Chinese-language skill for creating prompts, including analysing requirements, asking follow-up questions, applying prompt structures, and producing multiple versions.
Turn a user's idea, scene description, or mood into ONE production-grade cinematic still-image prompt in English for text-to-image models (Seedream, Midjourney, Flux, DALL-E, or any image generator). Use this skill whenever the user asks for an image prompt, a cinematic frame, a "movie still" look, a photorealistic…
Skill for working with ESM2 protein language models from Meta FAIR. Use this skill whenever the user wants to generate protein embeddings or representations, score variant effects or predict mutation fitness, run contact prediction, or use ESMFold for structure prediction. Also trigger when the user mentions ESM2…
Writes, rewrites, reviews, and audits prompts and system prompts using Anthropic's current official guidance, and selects the right model and effort level for the task before drafting. Use when the user asks to write a prompt, improve or fix a prompt, design a system prompt, or check a prompt before running a task …
Use when the user wants to use an LLM coding agent such as Codex or Claude Code to run a reproducible machine learning workflow on tabular CSV data, including data profiling, leakage checks, binary classification baselines, evaluation metrics, threshold reports, model cards, and final reports.
Executes an automated data retrieval pipeline connecting to Kaggle, OpenML, SEC EDGAR, and FRED to extract raw datasets for machine learning and data engineering workloads. Use when a user needs to fetch, download, or search for raw datasets, market data, or macro-economic statistics.
Pick the right LLM or media model for a task, backed by live benchmark data from artificialanalysis.ai. Use when the user asks "which model should I use for X", "what's the best/fastest/cheapest model", "compare model A vs B", "model leaderboard", or anything about model intelligence / speed / price / context /…
★not rated 2 4mo agoA154 tokens
originalMIT
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