Research Bundle

Run a multi-step research task as one pipeline: gather sources, extract findings, validate consistency, emit one structured bundle.

AI Agents / Developmentai-agentsdevelopment
by AgentVoltv1.0.0Published 1y ago1 install
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Before & after

Same prompt, same agent — the only difference is the skill.

PromptResearch the market for our new product.
Beforegeneric AI agent
Market research is important before you launch a new product. There are several areas you might explore—competitive landscape, customer needs and pain points, market size and growth trends, pricing benchmarks, and regulatory considerations if they're relevant. You could use a mix of approaches: talking to potential customers, analyzing competitor offerings, looking at industry reports, studying customer reviews on existing solutions, and maybe running a small survey. The key is to be systematic about it and document what you find. I'd be happy to help you plan a research approach if you tell me more about what your product does, who the target customer is, and what key questions you're trying to answer.
Afterwith Research Bundle
MARKET RESEARCH PLAN: PRODUCT LAUNCH

RESEARCH OBJECTIVES
1. Validate problem/market demand: Do target customers perceive this pain?
2. Competitor positioning: Who else solves this? What's their pricing model?
3. Market size estimate: TAM (total addressable market) + SAM (serviceable)
4. Go-to-market insights: Which channels reach buyers? Price sensitivity?

METHODOLOGY & SOURCES

QUALITATIVE RESEARCH
• Customer interviews: 15-20 target customers (1:1 video calls, 30 min)
• Interview template: Problem severity (1-10), current workaround, willingness to pay
• Analysis: Themes extraction, quote documentation, pain-point ranking

COMPETITIVE ANALYSIS
• Competitor audit: 5-8 direct competitors + 3-5 indirect
• Matrix: Pricing, features, target segment, strengths/gaps
• Sources: Website, demo video, G2/Capterra reviews, pricing pages

MARKET SIZING
• TAM: Start with industry report (Gartner, McKinsey) → revenue estimate
• SAM: Apply target-customer filter (company size, geography, vertical)
• Formula: Market size × penetration rate × avg contract value

SURVEY (if sample size >100 needed)
• Tool: Typeform or Qualtrics
• Sample: 50-100 target respondents (Reddit, LinkedIn recruitment)
• Questions: Problem relevance, feature priorities, price sensitivity

DELIVERABLE
• Report: 8-12 pages (executive summary, findings, competitive matrix, TAM calculation, recommend go/no-go)
• Timeline: 2-3 weeks (interviews + analysis)

GO/NO-GO DECISION CRITERIA
GO: Problem severity > 7/10, TAM > $100M, pricing power exists (multiple mentions of would pay), <2 strong competitors
NO-GO: Niche market (<$10M TAM), saturated competition, customers prefer free alternatives

About this skill


name: research-bundle description: Use when Structured Claude skill that gives Claude a repeatable workflow for research bundle.

Research Bundle

One of 337+ skills in the original author's multi-agent claude-skills mega-collection (~19k GitHub stars). Packages the Research Bundle workflow with its own instructions and validation so outputs stay consistent.

What you get

  • Public GitHub repo (alirezarezvani/claude-skills)
  • the research-bundle 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 Bundle; chains with the other skills, agents, and commands in the collection.

Best for

Creators, builders, and teams using Claude Code.

SKILL.md preview

SKILL.md
---
name: research-bundle
description: Use when compiling research from multiple sources into a single validated, structured output bundle for downstream use.
version: 1.0.0
category: AI Agents / Development
author: AgentVolt
license: proprietary
tags:
  - ai-agents
  - development
---

# Research Bundle

Packages a multi-step research task into one pipeline: gather sources, extract findings, validate consistency, and emit a single structured bundle instead of scattered notes.

## When to use

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