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The highest-leverage way to use the library: call run() at decision points inside a larger planning, coding, or review agent, and feed the deepened sketches back into the agent’s context.

Good moments to call it

  • An agent stuck after N attempts on a bug — widen the hypothesis space with new hypothesis classes
  • A planning agent at a branch point with high uncertainty
  • A code-review agent asked “what could go wrong here”
  • A refactor agent picking which abstraction to introduce
  • A test-generation agent generating adversarial inputs (the inversion frame is built for this)

Programmatic skill injection (Agent SDK)

You can also inject the skill itself into a Claude Agent SDK session, letting the agent run the loop with its own Task tool instead of via the library:

Which shape to pick

Library run() call

Minimal token substrate — each branch carries only the problem + frame prompt. Structured RunResult back. Best for batch and embedding in pipelines.

Skill inside a session

The agent orchestrates the frames itself with fresh-context Task calls. Zero extra dependencies, but every branch re-loads the session’s base context — see the cost model.

Real-world integrations

Projects have wired ADHD into their own loops in several shapes — mesh-orchestrator peers (repowire), a marketplace think plugin (mstack), an anchoring-bias pre-pass before review (zk-flow-oss), and a codebase-onboarding explanation engine (wtfismyrepo). See Adopters for the full list.