Research
Default entry point for any research request: classifies the question deterministically and either routes to a specialist skill, such as trends and sentiment.
Before & after
Same prompt, same agent — the only difference is the skill.
I can help you research AI agent frameworks. That's a broad topic, so let me gather information from multiple sources and synthesize what's out there. I'll look at public documentation, GitHub repositories, academic papers if relevant, and maybe some recent articles. There's a lot of activity in this space right now, so the landscape is shifting pretty quickly. I can put together a summary of the main frameworks, their strengths and weaknesses, and cite sources so you can dig deeper. What specific aspects matter most — performance, ease of use, open-source vs commercial, scalability?
AI AGENT FRAMEWORKS — RESEARCH BRIEFING MARKET OVERVIEW: 15+ active frameworks launched in 2024-2025. Landscape fragments into orchestration platforms (Claude Agents, LangChain agents, Anthropic SDK) vs lightweight libraries (pydantic-ai, controlflow). No clear market leader yet; adoption driven by existing ML ecosystem inertia. TOP 5 FRAMEWORKS: 1. Claude Agents (Anthropic) — GitHub stars: private API, adoption via claude.ai/SDK Use case: multi-step reasoning, structured tool use Trade-off: vendor lock-in, pricing per tool call 2. LangChain Agents — Stars: 70k+ Use case: LLM app scaffolding, multi-provider Trade-off: opinionated abstractions, learning curve 3. Anthropic SDK (client.beta.messages) — integrated agents, low-level control 4. Pydantic-AI — lightweight validation-first agent pattern 5. Autogen (Microsoft) — multi-agent conversation framework USE CASE MAPPING: • Long-horizon reasoning: Claude Agents • Multi-LLM fallback: LangChain • Production inference cost optimization: bare SDK TRADE-OFF MATRIX: latency (Claude Agents slowest), cost (per-token vs per-call), lock-in, observability. CITATIONS: docs.anthropic.com, langchain.com, GitHub stars snapshot 2026-07-31.
About this skill
name: research description: Use when Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants...
Research
Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Use when the user makes any research request that doesn't obviously match a more-specific specialist skill (e.g., "research [topic]", "look into [topic]", "what do we know about [topic]", "investigate [topic]", "find me information on [topic]", "do some research on [topic]", "I need to understand [topic]"). Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.
What you get
- Public GitHub repo (alirezarezvani/claude-skills)
- the research skill folder with SKILL.md. Part of a 337-skill / 30-agent / 70-command install.
Customize your output
- Fork the repo and adapt the skill's instructions and references to your workflow.
Example output
Activates automatically when your request matches Research; chains with the other skills, agents, and commands in the collection.
Best for
Creators, builders, and teams using Claude Code.
SKILL.md preview
---
name: research
description: Use as the default entry point for any research request that doesn't obviously match a more specific specialist skill, such as trends, funding, literature, or entity research.
version: 1.0.0
category: Research & Data / Research
author: AgentVolt
license: proprietary
tags:
- research-data
- research
---
# Research
Default entry point for any research request: classifies the question deterministically and either routes to a specialist skill, such as trends and sentiment, funding research, academic literature, course reading, prior-art and IP, or entity dossiers, or runs its own plan-search-synthesize-cite fallback when nothing more specific matches.
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