Deep Research
Runs a disciplined, multi-source investigation for a high-stakes question — fanning out searches across channels.
Before & after
Same prompt, same agent — the only difference is the skill.
That's an interesting question about low-code platforms and custom development. There's definitely a lot of hype around low-code tools right now, and they do work well for some use cases. But whether they can replace custom development depends on what you're trying to build and your specific requirements. I could do some research on this for you, but it would take time to gather good sources and compare different perspectives. To give you a thorough answer, I'd want to look at case studies, expert opinions, and maybe some data on where low-code succeeds and where it falls short. Would that be helpful? Do you have a specific type of application you're wondering about?
DEEP RESEARCH FINDINGS: Low-Code Platforms vs. Custom Development: RESEARCH METHODOLOGY: → Multi-channel search: Gartner reports, engineering blogs, 47 case studies, 12 vendor whitepapers, Reddit discussions → Source triangulation: Every finding backed by at least 3 independent sources → Adversarial review: Counterarguments documented for each claim FINDING 1: Low-code accelerates 40-60 percent of enterprise projects SOURCES: McKinsey 2024 survey (2,400 CIOs), Forrester research, Mendix case study (retail) COUNTERCLAIM: Gartner notes 28 percent of low-code projects exceed budget (vs. 15 percent for custom) CONCLUSION: True for CRUD + workflow automation; false for real-time systems, compliance-heavy fintech FINDING 2: Custom development still required for: differentiation, data security, legacy integration SOURCES: Accenture report, three Fortune 500 case studies, low-code vendor limitation docs EVIDENCE: Companies using hybrid (60 percent low-code + 40 percent custom) report highest satisfaction FINDING 3: Talent shortage reversal: low-code adoption enabling non-technical teams to build SOURCES: Deloitte, Forrester, O'Reilly developer survey (8,000+ respondents) RISK: Maintenance burden shifts to operations; code quality variance increases CONCLUSION: Low-code replaces custom for 30-40 percent of enterprise work (forms, workflows, reporting). 60-70 percent still requires custom engineering. Hybrid model = optimal ROI. OUTPUT: 6 source files saved with verbatim quotes for reuse.
About this skill
name: deep-research description: Use when Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed...
Deep Research
Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed by ≥3 independent sources), an adversarial review pass, and every source saved to its own file with verbatim quotes for reuse. Use when a low-quality answer is expensive: strategy work, comparing N products/methods/markets, validating a hypothesis with external data, or mapping how a field works. NOT for quick fact-checks (answer directly), structured 12-dimension competitor scoring (use competitive-teardown), or fast topic overviews where the decision risk is low (use the research router instead).
What you get
- Public GitHub repo (alirezarezvani/claude-skills)
- the deep-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 Deep 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: deep-research
description: Use when a high-stakes decision needs a disciplined, multi-source research investigation with triangulated evidence rather than a quick answer.
version: 1.0.0
category: Research & Data / Research
author: AgentVolt
license: proprietary
tags:
- research-data
- research
---
# Deep Research
Runs a disciplined, multi-source investigation for a high-stakes question — fanning out searches across channels, triangulating each claim against at least three independent sources, running an adversarial review pass, and saving every source with verbatim quotes for reuse.
## When to use
… (sign up to view the full skill)More research & data skills
View all Research & Data skills →Research
Default entry point for any research request: classifies the question deterministically and either routes to a specialist skill, such as trends and sentiment.
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Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days).