# Hdb:product Researcher > Use when given a product idea or existing products to research — analyzes functionality, market positioning, competitive landscape, feature desirability, and community sentiment from Reddit - Skill: `hughdbrown/hdb-product-researcher` (Agent Skill) - Install (CLI): `npx skillmds@latest add hughdbrown/hdb-product-researcher` - Raw SKILL.md: https://api.skillmd.com/api/skills/hughdbrown/hdb-product-researcher/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: hughdbrown (https://skillmd.com/u/hughdbrown) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/hughdbrown/hdb-product-researcher --- # 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 ``` ## 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 `: ### 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 4. **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? 5. **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) 6. **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? 7. **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 8. **Search Reddit for discussions** about each product and the product category. Use targeted searches: - `site:reddit.com "" review` - `site:reddit.com "" vs` - `site:reddit.com "" alternative` - `site:reddit.com "looking for" OR "recommend" ` - `site:reddit.com "" switched from OR switched to` - `site:reddit.com "" open source OR self-hosted` (for software products) 9. **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..." 10. **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 11. **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? 12. **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? 13. **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 14. **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 15. **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. 16. **Write the research document** to a file. Suggest: `research/-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.