Udit Akhouri releases ADHD ("a skill for agents"), an open source project (MIT, v0.1.4, ~1,000 stars) that tackles the premature convergence of autoregressive reasoning: an LLM anchors on its first idea, and tree-based methods don't really escape it — "Tree-of-Thought widens the search but traverses a single shared context, so the anchoring persists across branches." The project's stance: this is an architecture problem, not a prompting problem.
The mechanics consist of two watertight phases. Diverge: N parallel, isolated agent calls — no shared context — each receive the problem through one of 15 cognitive frames deliberately distorted (with selection logic and custom frames), under a system prompt that forbids evaluating. Focus: a separate critic, with an opposite system prompt, scores the ideas (originality, viability, fit), clusters them by underlying angle, flags the traps with their reasons, and deepens the best survivors. The generator/critic separation is "mechanical": distinct LLM calls, not roles simulated within a single context — the same intuition as the separate-context adversarial review of the Bun project ([[sumner-bun-rewrite-rust-claude-2026-07-08]]).
The signature demo compares, on "a CLI that calls an LLM and sometimes freezes for 90s," the baseline (4 textbook patterns: progressive timeouts, exponential backoff, hedged requests, streaming — "the answer a senior gives in 30 seconds") against ADHD: 30+ ideas across 6 clusters, 20 named traps, and a non-obvious pick — the "rage-quit" button that animates with the wait and instantly reroutes the request to a faster, cheaper model, because "the slow model might just be the wrong model for this prompt." On 6 open problems, the author's evaluation (LLM judge) gives breadth 9.00 vs 4.83, novelty 7.83 vs 2.67, trap detection 9.50 vs 1.83, actionability 9.50 vs 6.50 — self-reported figures, to be read as claims.
Distribution runs through the skills ecosystem (same channel as [[skill-pocock-grill-with-docs-2026-06]]): npx skills add UditAkhourii/adhd auto-detects ~50 agents (Claude Code, Cursor, Codex, Cline, Gemini CLI, Windsurf…), invocation via /adhd or auto-triggering on ideation intents, a CLI and an npm library, all built on the Claude and Codex Agent SDKs. Traction is tangible: a feature on The New Stack, a preprint, adoption by repowire (PR #313 merged — the frames become "peers" of the mesh-orchestrator), mstack (think plugin), zk-flow-oss, and an independent research review (testdouble/han) whose findings live on in public issues. Takeaway: useful divergence isn't prompted, it's architected — through context isolation and the mechanical generator/critic opposition.