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# KnitKnot Changelog: June 2026

June was about trustworthy numbers: one metrics spine behind every score on every surface, and run-over-run deltas you can actually act on.

Permalink: https://knitknot.ai/changelog/2026-06/

  • - Every number traces to its evidence: All aggregate metrics now read from one canonical metrics layer. Derived facts and verdicts are written at scoring time, every headline number on a report drills down to the exact AI responses behind it, and the drill-down list is the same row set as the headline. One win/loss/tie definition everywhere: win rate is wins over decided matchups, with ties counted in the denominator.
  • - Run Measurement tab: The run header shows your current snapshot; the new Measurement tab shows what changed since the last run. Mention deltas, entity shifts, and citation impact with flip attribution, so when a score moves you can see which sources and competitors moved it.
  • - MCP server: your AI presence, queryable from inside the assistants: KnitKnot's MCP server is live at mcp.knitknot.ai. Customer-facing tools provide workspace-scoped access to competitive position, score trends, competitors, issues, playbooks, source intelligence, demand topics, prompts, coverage, and workspace context, with supported playbook status updates. Connect through OAuth or use an API key for a programmatic client.
  • - Playbooks, now report-grade and on-demand: We rebuilt playbook detail around a tighter reader journey, richer evidence sections, and severity-ranked misrepresentation proof, with recency-aware reprioritization keeping the freshest losses and wins near the top. You can now generate or rebuild a playbook directly from the Playbook page, even when auto-generation does not fire on its own.
  • - A broader prompt library from cleaner research: Topic generation now runs top-down from cleaned research, with wider keyword harvest around ICP anchors and per-product topic sets. Each subject gets a flatter, broader prompt library with a tighter competitor-to-landscape mix, and rebuilds stay idempotent, so coverage gets broader without getting noisy or wiping the library.

Raw mirror of this content: /llms.txt. Site-wide summary: /llms.txt · full content: /llms-full.txt

← All updates

June 2026

June was about trustworthy numbers: one metrics spine behind every score on every surface, and run-over-run deltas you can actually act on.

Every number traces to its evidence

All aggregate metrics now read from one canonical metrics layer. Derived facts and verdicts are written at scoring time, every headline number on a report drills down to the exact AI responses behind it, and the drill-down list is the same row set as the headline. One win/loss/tie definition everywhere: win rate is wins over decided matchups, with ties counted in the denominator.

Run Measurement tab

The run header shows your current snapshot; the new Measurement tab shows what changed since the last run. Mention deltas, entity shifts, and citation impact with flip attribution, so when a score moves you can see which sources and competitors moved it.

MCP server: your AI presence, queryable from inside the assistants

KnitKnot's MCP server is live at mcp.knitknot.ai. Customer-facing tools provide workspace-scoped access to competitive position, score trends, competitors, issues, playbooks, source intelligence, demand topics, prompts, coverage, and workspace context, with supported playbook status updates. Connect through OAuth or use an API key for a programmatic client.

Playbooks, now report-grade and on-demand

We rebuilt playbook detail around a tighter reader journey, richer evidence sections, and severity-ranked misrepresentation proof, with recency-aware reprioritization keeping the freshest losses and wins near the top. You can now generate or rebuild a playbook directly from the Playbook page, even when auto-generation does not fire on its own.

A broader prompt library from cleaner research

Topic generation now runs top-down from cleaned research, with wider keyword harvest around ICP anchors and per-product topic sets. Each subject gets a flatter, broader prompt library with a tighter competitor-to-landscape mix, and rebuilds stay idempotent, so coverage gets broader without getting noisy or wiping the library.