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ADHD is small on purpose — the value lives in the frames and the loop discipline, not the LOC. Most contributions are one of:
  1. A new frame (highest leverage, smallest change)
  2. An improvement to the diverge/score/cluster/deepen loop
  3. A new eval problem that exposes where ADHD wins or loses
  4. Docs / examples / launch material

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Dev setup

Iterating without rebuilding:

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Authoring a new frame

The cheapest, highest-leverage contribution — every new frame widens what the package can surface for the next user. A good frame pushes the generator into a corner it would not naturally drift toward; bad frames are paraphrases of an existing one. See Frames → Authoring your own for the shape, the quality bar, and how to test it in isolation.

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Adding an eval problem

Eval problems live in bench/problems.json. See Evals → Methodology for what makes a good problem and how to run just yours. Costs ~10 LLM calls per problem.

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Loop changes (engine.ts)

The loop is small on purpose. Before changing it, read the source spec — most “improvements” violate the load-bearing invariants:
  • Branches must not see each other during divergence. This is the whole point.
  • Generator and critic must use separate LLM calls with opposite system prompts. Don’t merge them for efficiency.
  • Score before deepen. Deepening unscored ideas is just expensive generation.
  • Cluster by underlying angle, not surface keywords. If the cluster pass starts outputting “caching ideas” instead of “remove-the-server plays”, the prompt has drifted.
If your change weakens any of these, the bar is higher. Good loop changes: recursive deepen (multi-level ToT), pluggable scorers, streaming output during divergence, cross-model support.

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Style

  • TypeScript strict mode. No any unless commented why.
  • Comments are for why, not what. Code shows what.
  • New deps need justification. Current deps: @anthropic-ai/claude-agent-sdk, p-limit, zod — that’s already three more than the loop strictly needs.
  • No emojis in source (the renderer is allowed terminal symbols).

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PRs

  • One concern per PR.
  • New frame? Include a 2-line “what this frame catches that others miss” in the PR body.
  • New eval? Include the run output.
  • Loop change? Include before/after on at least one eval problem.
By contributing, you agree your contribution is licensed under MIT.