SignalQLQuery language for structured evidence

Why Implement SignalQL? ​

SignalQL gives your product a standard query layer for activity, state, relationships, and time. Instead of building a one-off query format for every feature, you expose one predictable language that AI tools and applications can reuse.

Use it for raw analytics: event counts, behavior trends, funnels, retention checks, and user activity questions.

Use it for task and work management: stale work, dependency chains, ownership gaps, handoff delays, and work items likely to slip.

Use it anywhere systems produce signals: ops events, security activity, support threads, repository activity, or social/activity streams.

The benefit is consistency: one retrieval layer, many domains, no hardcoded product assumptions in the language core.

From Question to Answer ​

SignalQL gives product, data, and engineering teams a shared way to ask evidence questions across activity streams, entities, and relationships. Retrieval stays in a readable, reviewable query instead of scattered SQL and one-off dashboard logic.

The result is faster iteration with clearer review: AI tools can draft SignalQL, execution stays deterministic, and interpretation is intentionally separated into AI or application layers above the language.

Choose your path ​

Build with SignalQL ​

• Run a first query with the CLI path, or run the local playground from a repo checkout.
• Connect AI tools through Cursor, Claude, ChatGPT, or MCP.
• Implement against the v0.3 spec, language scope, and AST schema.

Evaluate Adoption ​

• Why SignalQL explains the analytics and AI workflow gap.
• Adoption brief outlines a low-risk pilot, success criteria, and rollback.
• Spec versions keeps language evolution transparent, including historical standards.