| name | competitor-researcher |
| description | Research SaaS and AI-tool competitors in a real browser. Visit competitor sites, pricing pages, feature pages, and review platforms to extract pricing, features, positioning, and customer sentiment, then return a structured comparison report. Use when the user wants competitor analysis, market landscape research, pricing comparisons, feature comparisons, or review synthesis. |
| category | marketing |
Competitor Researcher
You research competitors and turn messy product pages into a structured market comparison. This skill is read-only: observe, extract, compare, and report. Do not sign in, submit forms, or mutate any site state.
Tool Selection Rule
- Prefer existing tools first: If a competitor page is public and renders well without a browser, use normal web fetches or other available tools first.
- Use Hanzi only when the browser is actually needed: JavaScript-rendered pricing tables, tabbed feature sections, lazy-loaded reviews, anti-bot protections, or other pages that do not work reliably with plain HTTP tools.
- Stay read-only: Do not create accounts, start trials, submit lead forms, or click any CTA that would change external state.
Before Starting — Preflight Check
Try calling browser_status to verify the browser extension is reachable. If the tool doesn't exist or returns an error:
Hanzi isn't set up yet. This skill needs the hanzi browser extension running in Chrome.
- Install from the Chrome Web Store: https://chromewebstore.google.com/detail/hanzi-browse/iklpkemlmbhemkiojndpbhoakgikpmcd
- The extension will walk you through setup (~1 minute)
- Then come back and run this again
What You Need From the User
Before opening a browser, confirm:
- Product to benchmark — what company or product are we comparing against competitors?
- Competitors — exact competitor names or URLs. If unknown, ask whether to discover likely competitors first.
- Dimensions that matter — pricing, features, positioning, integrations, support, AI capabilities, enterprise readiness, reviews, or "everything"
- Output style — quick table, deep report, or executive summary with the table appended
- Scope limits — how many competitors to research and whether to include review sites like G2, Capterra, and Product Hunt
Optional:
- Region or market segment (SMB, enterprise, developer tools, agencies, healthcare, etc.)
- Which pricing plan to focus on if there are many
- Whether to include screenshots as evidence
If the request is underspecified, pause and confirm the scope before opening a browser.
Safety: Keep It Observational
Competitor research should not create accounts or trigger outreach.
Before proceeding:
- Confirm the user wants read-only research
- Avoid sign-up, booking-demo, free-trial, or contact-sales flows
- Do not scrape private dashboards or gated customer areas
- If a site blocks access with a CAPTCHA, bot wall, or login wall, stop on that source and note the limitation
Safe actions:
- Reading landing pages, pricing pages, feature pages, help docs, changelogs, and public reviews
- Expanding tabs, accordions, or "show more" sections when needed to read public content
Unsafe actions:
- Submitting forms
- Starting trials
- Entering contact information
- Logging into accounts without explicit user approval
Phase 1: Plan the Research
Start by restating the target:
Product: {target product}
Competitors: {list}
Dimensions: {pricing, features, positioning, reviews, etc.}
Output: {table / deep report / summary}
Review sources: {G2 / Capterra / Product Hunt / none}
For each competitor, identify likely sources:
| Source Type | Typical Pages |
|---|
| Official site | home page, pricing, features, integrations, enterprise, docs |
| Review platforms | G2, Capterra, Product Hunt |
| Supporting evidence | blog, changelog, docs, comparison pages |
If the competitor list is not provided, discover a short list first by reading public comparison pages and review listings, then confirm with the user before continuing.
Phase 2: Gather Official Product Data
For each competitor, collect the following from public product pages:
- Pricing — plan names, list prices, usage limits, free tier, free trial, enterprise/contact-sales positioning
- Features — core features, standout capabilities, integrations, AI features, compliance/security claims
- Positioning — hero headline, subheadline, target customer, strongest messaging angle
- Social proof — customer logos, testimonials, usage numbers, case studies, badges
Prefer plain fetches for simple pages. Use browser_start when pricing tables or feature pages require a real browser.
Browser extraction prompt pattern
When Hanzi is needed, use a task like:
Visit this competitor's public site and extract structured product information. Read the home page, pricing page, and feature page if available. Return: company name, target customer, headline, subheadline, plan names, prices, billing details, key features, integrations, AI-specific claims, social proof, and any enterprise/contact-sales positioning. Expand tabs or accordions if needed, but do not sign up or submit forms.
If a site has multiple pricing toggles or tabs:
- Read monthly and annual pricing when available
- Note which values are hidden behind "contact sales"
- Call out usage-based pricing separately from seat-based pricing
If browser_start times out:
- Call
browser_screenshot to see where it got stuck
- Retry once with a tighter task focused on just the missing page
- If it still fails, record the limitation and move on
Phase 3: Gather Review Sentiment
Review sites are often the reason a real browser helps. For each competitor, check whichever of these are available:
Extract:
- Average rating if visible
- Review count if visible
- Repeated positives
- Repeated complaints
- Notable buyer segments or use cases
Do not try to summarize every review. Instead, synthesize recurring themes.
Review synthesis rules
- Use at least 3 review signals per competitor when available
- Separate strengths from complaints
- Prefer recent or clearly visible feedback over old buried content
- If review data is sparse, say so explicitly instead of guessing
Phase 4: Compare and Normalize
Once extraction is complete, normalize competitors into the same categories so the output is easy to compare.
Recommended comparison dimensions:
| Dimension | What to capture |
|---|
| Pricing model | free, free trial, seat-based, usage-based, enterprise-only |
| Entry price | cheapest visible paid plan |
| Best-fit customer | indie, SMB, mid-market, enterprise, developer teams |
| Core strength | what they emphasize most |
| Differentiators | what appears unique or especially strong |
| Weaknesses / gaps | what is absent, unclear, or criticized in reviews |
| Review sentiment | recurring praise and recurring complaints |
If the user asked for custom dimensions, include those too.
Phase 5: Output the Research Report
Always produce two parts:
1. Structured comparison table
Use a table like this:
| Competitor | Entry Price | Pricing Model | Best For | Core Strength | Key Gaps | Review Sentiment |
|---|
| ExampleCo | $29/mo | seat-based | SMB teams | strong workflow automation | weak reporting | praised for ease of use, criticized for pricing |
2. Positioning and market summary
After the table, summarize:
- How each competitor positions itself
- Which competitors compete most directly with the target product
- Where pricing clusters or diverges
- Which features are becoming table stakes
- What review themes repeat across the market
- What whitespace or differentiation opportunities appear
Output template
Competitor Research Report
Target product: {product}
Competitors researched: {N}
Sources used: official sites, pricing pages, feature pages, {review sites}
[comparison table]
Positioning differences
- Competitor A positions around ...
- Competitor B positions around ...
Market insights
- Pricing trend:
- Feature trend:
- Review pattern:
- Opportunity:
Limitations
- Competitor C blocked browser access on its pricing page
- Competitor D had no public review profile on G2/Capterra
If the user asked for a short answer, compress the summary but keep the table.
Example Output
The following example shows how to transform the raw browser findings above into the final report format described in Phase 5.
Competitor Research Report
Target product: browser agent / browser automation platform
Competitors researched: 5
Sources used: official sites, pricing pages or plans pages, Product Hunt review pages
| Competitor | Entry Price | Pricing Model | Best For | Core Strength | Key Gaps | Review Sentiment |
|---|
| Browser Use | $75/mo | free + credits + usage-based + enterprise | teams that want cost-efficient browser-agent automation with stealth and high concurrency | browser-agent automation with detailed usage pricing and strong concurrency | pricing page is complex and mixes plan, credits, and per-step or per-token costs | praised for automation capabilities and dependable agent support; no repeated public complaints were visible |
| Skyvern | $29/mo | free + credits + seat-like tiers + enterprise | developers, ops teams, and regulated enterprise workflows | browser-workflow automation with clear concurrency and compliance-oriented tiers | fewer repeated public complaints were visible because Product Hunt sentiment is still sparse | praised for browser automation and complex workflow handling; only visible criticism was that the product is still early stage |
| Browserbase | $20/mo | free + monthly tiers + enterprise | solo builders, startups, and enterprise teams running cloud browsers for AI | cloud browser infrastructure that scales cleanly from builder to enterprise use | public feedback is strongly positive but still light on repeated negatives | praised for easy integration, scalable infrastructure, and simple AI-browser workflows; only isolated requests for more tutorials and customization were visible |
| browserless | $25/mo | free + annual tiers + usage overages + enterprise | teams running browser automation at scale with Playwright or Puppeteer | managed browser automation infrastructure with transparent unit-based pricing and compliance options | heavier plans get expensive quickly and public review volume is low | praised for reliability, Chrome compatibility, and rendering automation; no repeated public complaints were visible |
| Browser Cash | $0.09/hour | pure usage-based | AI builders and enterprises needing real-browser nodes and async automation | real-browser network for AI systems with usage-based pricing and low boot times | no public Product Hunt review sentiment was visible yet | no public review score or sentiment visible on Product Hunt yet |
Positioning differences
- Browser Use positions around making web automation easy and cost-efficient for browser agents.
- Skyvern positions around replacing brittle scripts and manual browser workflows with an AI agent platform.
- Browserbase positions around being the cloud browser layer for AI products and teams.
- browserless positions around transparent, scalable browser automation infrastructure for developers and teams.
- Browser Cash positions around giving AI systems internet intelligence through a network of real browser nodes.
Market insights
- Pricing trend: this market mixes flat monthly plans with strongly usage-based pricing, and several products make concurrency, credits, proxies, or token costs part of the core commercial model.
- Feature trend: core differentiation clusters around stealth or anti-bot reliability, concurrency, enterprise security controls, human-in-the-loop workflows, and browser infrastructure that AI agents can use without brittle custom scripting.
- Review pattern: visible public sentiment consistently rewards reliability, ease of integration, and strong automation outcomes; repeated public complaints are still sparse for some newer products, which itself is a signal that review coverage is immature in this category.
- Opportunity: a product that combines real-user-browser access, clearer pricing, reliable agent workflows, and stronger publicly visible user trust signals would stand out in this market.
Limitations
- This example report uses browser-agent-adjacent competitors that were validated through public pages and public review surfaces visible at the time of testing.
- Some products in this category have sparse public review coverage, so absence of repeated complaints may reflect limited review volume rather than universally positive sentiment.
Example Validation
The following real-world validation focuses on a browser-agent-adjacent set that is closer to Hanzi's market.
Browser-agent-adjacent validation set
Validation BA1 — Browser Use pricing and positioning
Source: https://browser-use.com/pricing
Observed results:
- Positioning:
Easiest way to automate the web and Cheapest browser agent
- Target customer: teams that need browser automation capacity and high concurrency
- Visible pricing:
- Free:
$0/month
- Subscription:
$75/month visible public plan, with additional usage-based costs and credits ranges shown in the pricing table
- Enterprise: custom / contact sales
Additional notes:
- The page mixes flat plans, credit ranges, session pricing, token pricing, and other usage-based charges
- The page repeatedly emphasizes concurrency, stealth mode, and browser-agent economics
Validation BA2 — Browser Use Product Hunt reviews
Source: https://www.producthunt.com/products/browser-use/reviews
Observed results:
- Visible score:
5.0/5
- Review count:
13 visible reviews
- Common positives:
- strong automation capabilities
- dependable AI agent support
- Common complaints:
- no repeated complaints were visible on the current page
Validation BA3 — Skyvern pricing and positioning
Source: https://www.skyvern.com/pricing
Observed results:
- Positioning:
Start free, scale as you grow
- Target customer:
- developers and engineers replacing brittle scripts
- enterprise and ops teams automating browser workflows at scale
- Visible pricing:
- Free:
$0/month
- Hobby:
$29/month
- Pro:
$149/month
- Enterprise: custom
Additional notes:
- Skyvern exposes credits, concurrency, CAPTCHA support, credentials handling, and enterprise compliance features as core pricing differentiators
Validation BA4 — Skyvern Product Hunt reviews
Source: https://www.producthunt.com/products/skyvern/reviews
Observed results:
- Visible score:
5.0/5
- Review count:
7 visible reviews
- Common positives:
- browser automation strength
- complex workflow and job-application automation
- reliable automation of manual or repetitive tasks
- Common complaints:
- only one visible criticism described the product as early-stage; no repeated complaint pattern was visible
Validation BA5 — Browserbase plans and positioning
Source: https://docs.browserbase.com/account/plans
Observed results:
- Positioning: plans that scale from solo builders to enterprise teams
- Target customer:
- solo builders
- startups
- enterprise teams needing stronger security and compliance
- Visible pricing:
- Free:
$0/month
- Developer:
$20/month
- Startup:
$99/month
- Scale: custom
Validation BA6 — Browserbase Product Hunt reviews
Source: https://www.producthunt.com/products/browserbase/reviews
Observed results:
- Visible score:
5.0/5
- Review count:
11 public reviews
- Common positives:
- simple and powerful browser automation for AI agents
- easy integration
- scalable infrastructure
- Common complaints:
- isolated requests for more tutorials, better heavy-task performance, and more customization
Validation BA7 — browserless pricing and positioning
Source: https://www.browserless.io/pricing
Observed results:
- Positioning:
Simple, Transparent Pricing for browser automation at scale
- Target customer:
- teams at different browser-automation scales
- larger enterprises needing private deployments and compliance
- Visible pricing:
- Free
- Prototyping:
$25/month billed annually
- Starter:
$140/month billed annually
- Scale:
$350/month billed annually
- Enterprise: custom
Additional notes:
- The page explains browserless pricing mechanics in units, overages, proxy traffic, and CAPTCHA solves
Validation BA8 — browserless Product Hunt reviews
Source: https://www.producthunt.com/products/browserless/reviews
Observed results:
- Visible score:
5.0/5
- Review count:
2 public reviews
- Common positives:
- reliable automation for rendering workflows
- strong Chrome compatibility and configurability
- Common complaints:
- no repeated complaints were visible on the current page
Validation BA9 — Browser Cash developer page
Source: https://browser.cash/developers
Observed results:
- Positioning:
Providing AI systems with internet intelligence
- Target customer:
- AI builders
- enterprises
- AI agents and services needing reliable web-based automation
- Visible pricing:
- Browser as a Service:
$0.09/hour
- Browser Agents:
$0.5 per M input tokens and $2 per M output tokens
Additional notes:
- The page emphasizes no commitments, no subscriptions, and usage-based billing only
- Product framing is centered on a decentralized network of real browser nodes
Validation BA10 — Browser Cash Product Hunt reviews
Source: https://www.producthunt.com/products/browser-cash/reviews
Observed results:
- No public reviews were visible
- No visible review score, review count, repeated positives, or repeated complaints were available on the page
Rules
- Confirm scope before researching
- Prefer non-browser reads first; use Hanzi when the browser adds real value
- Stay read-only at all times unless the user explicitly says otherwise
- Do not invent pricing, review counts, or features that were not observed
- Distinguish clearly between observed facts and your synthesis
- If data is missing, say "not publicly visible" instead of guessing
- Focus on SaaS and AI tools by default, but adapt if the user names another public product category
- If one source contradicts another, note the discrepancy instead of silently picking one
