fastcode-search
Native Node.js semantic search for Agent. No external dependencies.
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Native Node.js semantic search for Agent. No external dependencies.
Automated news aggregation and reporting agent.
ARA World Model — read-only reasoning engine over ONE Agent-Native Research Artifact (ARA), run LOCALLY with the coding agent itself as the LLM (no SDK, no API key). Given an ARA directory and a free-text query, it answers any question about the ARA — a forward "what if I change X", but equally why-did-this-work, what-should-I-try, is-this-sound, how-do-these-compare, or anything else — by retrieving precedent from the ARA's native files (references/RETRIEVE.md) and answering as the Predictor (references/PREDICT.md): a bold, grounded, falsifiable Answer shaped to what the question actually calls for. TRIGGERS: ask the world model, wm predict, predict with the world model, what if I change X, forecast the loss curve, will this help, why did this work, what should I try next, is this claim sound, compare these, retrieve precedent, what precedent surfaces.
Research Visualizer. Renders an existing Agent-Native Research Artifact (ARA) into ONE self-contained, interactive HTML file showing the AI scientist's step-by-step research process: a clickable process map of the exploration tree (branches and dead ends included) on the left, and a per-step drill-down on the right — what the step did (its narrative written in plain language a person can follow), why (the linked claim), the real result (verbatim grounded numbers + inline figures + tables), and the code/artifact pointer. Read-only consumer of the artifact — it never changes how research is done. When the ARA carries them, it also surfaces (each optional, only when present) the related-work dependency graph, the problem framing, a concepts glossary with in-text term popovers, and the solution recipes — reached from header disclosures without leaving the process map. Accepts either an existing ARA or raw research input (a paper, repo, run logs, or notes); when the input is not yet an ARA it is compiled into one first, then visualized. TRIGGERS: visualize, visualizer, trajectory view, render the ARA, see the steps, step-by-step view, process map, replay the trajectory, watch the agent work, drill into steps, visualize a paper, visualize a repo, visualize a run
Expert guidance across solar physics, planetary science, stellar evolution, cosmology, and observational techniques with 2025 mission data
Research UI component patterns across 60 component types and 95 production design systems (2,676+ examples from component.gallery). Compare implementations, find alternative component names, ground frontend decisions in real-world precedent. Pairs with minoan-frontend-design for research-then-build workflows. Triggers on component patterns, how do others implement X, design system research, component comparison.
Product research for dropshipping businesses. Identify profitable products with reliable suppliers, healthy margins, and manageable competition. Evaluates shipping times, return risk, and marketing viability.
Competitor research — pricing comparison, bestseller analysis, differentiation strategy
A package manager for AI knowledge. Meta-skill for on-demand skill discovery and installation — search curated local repositories or the global skills.sh registry. Self-teaching capability that finds, evaluates, and installs relevant skills when the agent lacks expertise. Use when encountering unfamiliar frameworks, languages, tools, or domains. Use when you need to "learn a skill", "find skills for X", "search for skills", or when you lack expertise in a specific technology.