swarmia.com
success
Claude Code
intent

What does swarmia.com do and who is it for? Explain it back to me.

18steps
87.4sduration
$1.1048cost
277,587tokens
22 steps4 reasoning steps7 searches
home
docs
docs
/product
/features
docs
search
/product/engineering-m…
/product/data-platform
search
docs
search
/alternative/jellyfish
search
/git-analytics
search
search
search
55%
on-site discovery
82%
reliability
9%
link following
path origin
  • previous resource9%
  • web search45%
  • prior knowledge45%
insight

The agent assembled a comprehensive overview of Swarmia by combining fetched product pages with web search results, achieving the core discovery-evaluation task. The site published clear positioning and product descriptions across multiple pages, but pricing details and competitive differentiation required heavy reliance on web search and prior knowledge to fill gaps that the main website did not make easily accessible in machine-readable form.

  • ›Steps [1], [2], [3], [9], [10] provided the foundational product narrative: engineering intelligence platform with metrics, AI adoption tracking, developer surveys, and software capitalization. These were content-bearing fetches that articulated the core value proposition and use cases.
  • ›Step [13] (pricing page) was cited but the agent noted the response was 'truncated in my attempts to fetch it' — the pricing page exists but was not fully accessible in the fetch, forcing reliance on web search [11] and prior knowledge for specific pricing figures ($23-45/month per developer).
  • ›Steps [16] and [17] (competitive comparison pages and git-analytics positioning) were discoverable via direct URL patterns and provided some differentiation narrative ('metrics into behavior change', GitHub-centric), but the agent had to supplement these with external comparison articles from steps [12] and [15] to build a robust competitor list.
  • ›Integration support (GitHub, Slack, Jira, etc.) and notably the Bitbucket/GitLab limitation were not clearly surfaced on the site itself — the agent found these via web search and help documentation.
  • ›The site made product pages easy to guess (/product, /pricing, /alternative/[competitor]) but did not publish a comprehensive integration list or detailed pricing breakdown in a single, machine-readable format.
  • ›Heavy reliance on web search (45%) and prior knowledge (45%) alongside direct site fetches (9% from previous artifacts) reflects that the site exposed positioning and messaging well but fragmented detailed operational information across multiple pages and required external sources to synthesize a complete picture.

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