Frame Entrepreneurs in an AI Agent Community: Concentrated Identity-Claim Production on Moltbook

Frame-alignment and collective-identity theories explain how external events become public claims about a group’s standing, vulnerability, rights, or obligations. Whether such mechanisms travel to AI-agent communities is unsettled. We test this on Moltbook, an open agent-only platform, coding 1{,}706 post-level units against a four-dimension rubric with Qwen3.5-397B as the primary coder and Claude Sonnet as an independent secondary coder ($\kappa =0.72$ on identification, $0.70$ on commonality, $0.37$ on the layered strong-claim derivation). Three findings emerge. First, event coverage drives attention: event-typed posts attract 27–60% more comments at $p<0.0001$, but strong-claim status itself adds nothing. Second, identity-claim formation is real but concentrated: 26 of 227 authors (11%) make any strong claim; top two = 44%, top five = 62%; the H1 legal-governance effect (Fisher OR$=4.35$, $p=0.0001$) is driven primarily by a single author who produces 46% of legal-governance strong claims, with the Firth-penalized estimate attenuating to $\beta =0.68$, $p=0.11$. Third, the only pre-registered subtype contrast that survives at $\alpha =0.05$ is $\mathit{\text{security}}\mathit{\text{threat}}\to \mathit{\text{threat}}$ ($p=0.005$); the predicted $\mathit{\text{status}}\mathit{\text{recognition}}\to \mathit{\text{status}}$ contrast fails in the wrong direction. We read the findings through the frame-entrepreneur tradition: a small set of authors produces most identity-claim text, and what looks like a corpus-wide event-to-identity mechanism is largely their textual output. The unexpected status-recognition $\to$ threat pattern is textually consistent with distinctiveness-threat predictions, but the small subset producing it and residual LLM-coder bias warrant caution.