hdb-product-researcher

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Use when given a product idea or existing products to research — analyzes functionality, market positioning, competitive landscape, feature desirability, and community sentiment from Reddit

hughdbrown By hughdbrown schedule Updated 3/27/2026

name: hdb:product-researcher description: Use when given a product idea or existing products to research — analyzes functionality, market positioning, competitive landscape, feature desirability, and community sentiment from Reddit

hdb-product-researcher

Research products and their market to produce a competitive analysis grounded in functionality, feature desirability, and real user sentiment.

Usage

/hdb:product-researcher <product idea or product names/URLs>

Description

Performs deep product research given either a product idea (to find existing players) or pointers to specific products (to analyze them directly). Produces a structured research document covering what each product does, what the category has in common, which features users love most, how difficult it would be to build a competitor, and what Reddit users actually say about these products. The output is a reference document for product strategy decisions.

Instructions

When the user invokes /hdb-product-researcher <input>:

Phase 1: Scope the research

  1. Determine the input type:

    • Product idea — The user has a concept and wants to know who else is doing it. Proceed to step 2.
    • Specific products — The user names products or provides URLs. Skip to step 3.
    • Both — The user has specific products AND wants to see who else is in the space. Do both steps 2 and 3.
  2. Discover the competitive landscape. Search the web to identify:

    • The product category name (what users call this space)
    • The top 5-10 products in this category (both commercial and open source)
    • Any notable newcomers or recently shut-down products
    • Adjacent categories that partially overlap
    • If the product is software, search GitHub for top open-source alternatives (see Phase 2a)

    Present the discovered products to the user and ask: "Are these the right products to analyze? Should I add or remove any?"

  3. Confirm the product list. State the products that will be researched and the category definition. If the user provided products, ask if they want the broader landscape included or just the named products.

Phase 2: Research each product

  1. For each product, gather:

    Core functionality:

    • What does it do? One-paragraph summary of the product's purpose
    • What is the primary use case? Who is the target user?
    • What is the pricing model? (Free, freemium, subscription, one-time, usage-based)
    • What platforms does it run on? (Web, desktop, mobile, CLI, API)

    Feature inventory:

    • List all major features visible from the product's marketing site, documentation, and changelog
    • Categorize features as: core (essential to the product category), differentiating (unique to this product), or table-stakes (expected but not differentiating)
    • Note any features that are prominently marketed (what the company thinks matters most)

    Technical characteristics:

    • Open source or proprietary?
    • Self-hostable or cloud-only?
    • API available? What kind? (REST, GraphQL, SDK)
    • Integrations with other tools
    • Data portability — can users export their data easily?

    Market signals:

    • Approximate user base or traction indicators (funding raised, employees, customer logos, app store ratings)
    • How long has the product existed?
    • Recent trajectory — growing, stable, declining, pivoting?
  2. Use web search and scraping to gather this information from:

    • The product's own website and documentation
    • Review sites (G2, Capterra, Product Hunt, AlternativeTo)
    • Tech press coverage
    • App store listings if applicable

Phase 2a: Open-source landscape (software products only)

  1. Search GitHub for open-source alternatives in the product category:

    • Search GitHub by topic, description, and category keywords
    • Sort by stars to find the most popular projects
    • For each significant open-source project (1000+ stars, or fewer if the category is niche), gather:

    Repository health:

    • Stars, forks, and open issues count
    • Last commit date and commit frequency (active / maintained / abandoned?)
    • Number of contributors
    • Release cadence — how often are new versions published?

    Project maturity:

    • Is it production-ready or experimental?
    • Does it have documentation, tests, and CI?
    • What license? (MIT, GPL, AGPL, etc. — this affects commercial viability)
    • Is there a company or foundation behind it, or is it community-driven?

    Feature comparison to commercial products:

    • Which commercial features does the open-source project replicate?
    • What is missing compared to the paid alternatives?
    • Are there features unique to the open-source version? (Self-hosting, extensibility, plugin systems)

    Community signals:

    • What do GitHub issues and discussions reveal about pain points?
    • Are there forks that address specific gaps? (Indicates unmet needs)
    • How responsive are maintainers to issues and PRs?
  2. Assess the open-source threat/opportunity:

    • Could an open-source project be the foundation for a competitive product?
    • Which open-source projects could a new entrant build on top of instead of starting from scratch?
    • Are commercial products at risk of being displaced by open-source alternatives?

Phase 3: Reddit sentiment analysis

  1. Search Reddit for discussions about each product and the product category. Use targeted searches:

    • site:reddit.com "<product name>" review
    • site:reddit.com "<product name>" vs
    • site:reddit.com "<product name>" alternative
    • site:reddit.com "looking for" OR "recommend" <category keywords>
    • site:reddit.com "<product name>" switched from OR switched to
    • site:reddit.com "<product name>" open source OR self-hosted (for software products)
  2. For each product, extract from Reddit discussions:

    Loved features — Which specific features do commenters praise? Quote representative comments. Rank by frequency of mention.

    Pain points — What do users complain about? Common frustrations, missing features, broken workflows. Quote representative comments.

    Deal-breakers — Which products have users explicitly turned away from, and why? Capture the specific reason: "I tried X but left because it didn't have Y."

    Unmet needs — What do users say they wish existed? Capture requests like "I just need something that does X without all the bloat of Y."

    Switching patterns — Who switches from what to what, and why? Capture migration paths: "I moved from X to Y because..."

  3. Assess overall Reddit sentiment volume:

    • How many distinct Reddit threads discuss this product category?
    • How many users express a need for a product like this?
    • Which subreddits have the most discussion? (This reveals the user community)
    • Is discussion volume growing, stable, or declining over time?

Phase 4: Competitive analysis

  1. Identify category commonalities:
  • What features does every product in the category share? (These are table-stakes)
  • What is the standard pricing model?
  • What is the typical platform coverage?
  • What technical approach do most products take?
  1. Identify differentiators:

    • What makes each product unique?
    • Which differentiators actually matter to users (based on Reddit sentiment)?
    • Which differentiators are marketing-speak that users don't mention?
  2. Map feature desirability by combining the feature inventory with Reddit sentiment:

    • Must-have features — Users leave products that lack these
    • Delight features — Users praise products that have these, but don't leave over their absence
    • Indifferent features — Present in products but rarely mentioned by users
    • Anti-features — Features users actively complain about (bloat, complexity, privacy concerns)

Phase 5: Build-difficulty assessment

  1. For each product, estimate the difficulty of building a competitive alternative:

    Technical complexity:

    • What are the hardest technical problems to solve? (e.g., real-time sync, ML models, data pipelines)
    • Are there open-source libraries or frameworks that solve the hard parts?
    • What infrastructure is required? (Simple web server? GPU clusters? Edge network?)

    Data and network effects:

    • Does the product benefit from network effects? (More users = more value?)
    • Does it require a large dataset to function? (Training data, content library, marketplace inventory?)
    • How hard is the cold-start problem?

    Moat assessment:

    • What makes this product hard to copy? (Brand, data, integrations, patents, community?)
    • What is easily copyable? (UI patterns, feature set, pricing model?)
    • Are there regulatory or compliance barriers?

    Estimated effort: Classify as:

    • Weekend project — Core functionality achievable by one developer in days
    • Side project — Core functionality achievable by one developer in 1-3 months
    • Startup — Requires a small team and months of focused work
    • Major undertaking — Requires significant investment, specialized expertise, or data acquisition
    • Extremely difficult — Strong moats (network effects, data, regulation) make competition impractical without substantial resources

Phase 6: Synthesize

  1. Produce the research document with these sections:

    Executive Summary — 3-5 sentences: what is this product category, how many players exist, what do users care about most, and how hard is it to compete.

    Product Evaluations — For each product:

    • One-paragraph summary
    • Strengths (with Reddit evidence)
    • Weaknesses (with Reddit evidence)
    • Who is this product best for?
    • Overall rating: strong / adequate / weak / declining

    Comparison Matrix — A table comparing all products across key dimensions:

    • Core features (present/absent/partial)
    • Pricing
    • Platforms
    • Open source / self-hostable
    • API availability
    • GitHub stars (for open-source projects)
    • Reddit sentiment (positive/mixed/negative)

    Feature Desirability Map — The must-have / delight / indifferent / anti-feature breakdown with evidence.

    Reddit Sentiment Summary:

    • Total discussion volume and trend
    • Top 5 most-loved features across the category (with quotes)
    • Top 5 most-requested missing features (with quotes)
    • Top 5 reasons users leave products (with quotes)
    • Subreddits where this category is discussed

    Market Assessment:

    • Is this market growing, stable, or shrinking?
    • Is there an underserved segment?
    • What would a new entrant need to offer to win users?
    • What is the minimum viable feature set based on user sentiment?

    Open-Source Landscape (software products only):

    • Top open-source alternatives with GitHub stats (stars, contributors, last activity)
    • Feature gap analysis vs. commercial products
    • Viability as a foundation for a new product
    • License implications for commercial use

    Build-Difficulty Summary — For each product, the effort estimate and key moats. Note which open-source projects reduce build difficulty. Overall assessment: is there room for a new competitor?

    Opportunities — Based on all research, where are the gaps? What could a new product do that nobody is doing well? Can any existing open-source project be extended to fill the gap?

    Sources — URLs for all product pages, Reddit threads, review sites, and articles consulted.

  2. Write the research document to a file. Suggest: research/<category-slug>-product-research.md

Guidelines

  • Let users speak for themselves. Quote Reddit comments directly rather than paraphrasing. Attribute to subreddit and approximate date when possible.
  • Distinguish marketing from reality. What a product's website says and what users experience are often different. Note discrepancies.
  • Be opinionated where evidence supports it. "Product X dominates this category because..." is more useful than "there are several products." Ground opinions in data.
  • Flag uncertainty. If information cannot be verified (user counts, revenue, private companies), mark it as [ESTIMATED] or [UNVERIFIED].
  • Prioritize recency. A Reddit thread from 6 months ago is more relevant than a blog post from 3 years ago. Note dates on all evidence.
  • Don't over-research niche products. If a product has minimal Reddit discussion and limited market presence, a brief evaluation is sufficient. Spend depth on the products users actually discuss.
  • Think like a founder. The person reading this research is deciding whether to build something. Every section should help them make that decision.
Install via CLI
npx skills add https://github.com/hughdbrown/claude-skills --skill hdb-product-researcher
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