pmf-signal-reader

Reading PMF signals involves evaluating retention, word-of-mouth, engagement depth, and revenue simultaneously. This skill spawns four specialized agents to analyze founder context and cycle history, producing a composite signal strength rating and a watchlist of leading indicators.

The core indicators of product-market fit are retention curves, word-of-mouth growth, engagement depth, and revenue traction. Evaluating these four dimensions in parallel prevents over-reliance on surface-level vanity metrics and reveals true user value.

You should run a product-market fit assessment during building cycles that indicate initial traction, or when you are uncertain whether current growth signals represent real traction. It provides an honest read by aggregating retention, engagement, and revenue data.

To generate a PMF assessment report, the system reads founder-context.md and cycle history through four specialized agents. It outputs a pmf-assessment.md file containing a composite signal rating and a watchlist of leading indicators to monitor for the next cycle.

Yes, the PMF signal evaluation works without external dependencies. It independently analyzes founder context and cycle history using four internal agents to produce a composite signal rating, requiring no external components or integrations.