Signal Scout
Evidence-Backed Go-to-Market Research Agent · AI Agents
A Claude Code / OpenCode skill that turns a startup URL into an evidence-backed shortlist of first customers, market segments, and companies to pitch, using public signals only.
View source on GitHubHow it was built
Problem
LLM-generated market research reads well but is routinely wrong: a model will confidently name a "prospect" or cite a "signal" that isn't actually there when you go look. For go-to-market research specifically, a fabricated lead costs real outreach time before anyone notices.
Decisions
- Built verify_sources.py as a hard gate: it re-fetches every cited source and confirms the claimed evidence is actually on the page before a claim is allowed into the report. Nothing ships unchecked.
- Extracted the report rendering into a shared, deterministic component reused across the whole skill family, so formatting bugs and prompt drift get fixed once instead of per-skill.
- Shipped as a standalone Claude Code / OpenCode plugin rather than a hosted service, so it runs against a user's own Claude Code session with no separate backend to operate.
Outcome
The verification step became the template for catching AI fabrication elsewhere: its containment-checking approach was later generalized into its own standalone tool, verify-before-ship.
Key features
- Turns a startup URL into a ranked shortlist of prospects, segments, and companies
- Every claim is backed by a public, checkable source, so nothing in the report is fabricated
- Shared, deterministic report renderer reused across the whole skill family
- Ships as a standalone Claude Code / OpenCode plugin
Impact
Cuts first-customer research from days of manual digging to one evidence-backed report.
Tech stack
Python, Claude Code Skills, OpenCode