Methodology

Research agents (Claude Opus 5) gathered the data on 26–27 July 2026, coordinated by a lead model (Claude Fable 5). Scouts mapped the territory, then one researcher per system catalogued affordances and coercion techniques against a fixed schema, quoting files verbatim and linking every claim to its source. Every claim was checked against its primary source before publication.

Inclusion criteria: open source, active within the last six months, and enough public surface to study. The set spans AI-native leaders, large corporate systems and one deliberate public-sector contrast case. Where monorepos are private (Atlassian, Nord, SLDS internals), records rely on published packages and docs, and say so.

AI maturity is a four-step editorial rating applied with one rubric across all systems: none (no AI affordances found), emerging (llms.txt or an AI docs page, little more), invested (official MCP, skills or rules with real engineering behind them), ai-native (AI consumption is a core design goal).

The full dataset ships alongside this report as JSON records and a relational SQLite database: systems, affordances, techniques, platform capabilities and sources.

• A snapshot taken 26–27 July 2026. The systems described here ship weekly, so expect drift within weeks.
• Snippets are excerpts, capped at 40 lines and sometimes abridged mid-list. Follow the source link before quoting further.
• Builder-side findings cover public evidence only. Private monorepos may hold agent tooling this study can’t see; “no public agent files” is not “no AI usage”.
• Community tools were checked for existence, not audited for quality or maintenance.
• Maturity ratings are our judgment against one rubric, not vendor self-reports.