README-to-brief
Inspect the exact bounded project context before any hosted model work begins.
Project name generator · README-ready
Give NamingSignal the problem, mechanism, audience, and future scope—or start with the README you already wrote. It extracts an editable brief before generating, so the names reflect the project rather than a generic keyword cloud.
Step 1 · Your project
One sentence is enough. Add the audience, problem, or differentiator—or paste a full README.
The visible text or one fetched public page is capped and screened first. A user-selected public page may be sent to the configured model provider to infer an evidence-grounded brief; pasted and uploaded text stays on the deterministic compiler at this step. No repository clone, source tree, `.env`, credentials, dependencies, or private-network URL. When you sign in, your full sprint is saved privately to your account. NamingSignal separately records the names shown, bounded brief labels, domain evidence, shortlist/reject/winner actions, and registrar-link clicks to improve future results; raw pasted or uploaded source text is not copied into that internal learning ledger. Exact domain checks may be reused until their displayed evidence becomes stale.
Inspect the exact bounded project context before any hosted model work begins.
Check GitHub, npm, and PyPI exact-name evidence alongside selected domain endings.
Export the brief, candidates, evidence, shortlist, and remaining manual actions for the team.
Practical answers
Availability is time-sensitive evidence. A final choice still needs current registrar verification, spoken testing, cultural review where relevant, and appropriate legal clearance.
Yes. The public REST contract, local CLI, and stdio MCP server expose the same bounded workflow for Codex, Claude, and other capable agents.
No. The web flow accepts bounded pasted or uploaded context and safe public imports. It excludes detected secrets and does not clone a private repository.
Yes. The workflow preserves constraints and rejection reasons, so a team can rerun the sprint when the audience, scope, or public-launch requirements change.