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Gemini Executive Synthesis

ADHD skill for coding agents: implementing `frame-selection learning across runs` via a 'dreaming' feedback loop.

Technical Positioning
Enhancing `ADHD`'s adaptive intelligence and efficiency by dynamically optimizing `frame selection` based on historical performance.
SaaS Insight & Market Implications
Implementing `frame-selection learning` via a 'dreaming' feedback loop addresses a critical efficiency and intelligence gap in `ADHD`'s current static frame selection. Dynamically biasing frame choices based on historical performance for specific problem types will significantly enhance `ADHD`'s effectiveness and reduce computational waste. This architectural evolution, moving from 'prospective divergence' to 'retrospective consolidation,' positions `ADHD` as a more adaptive and intelligent `LLM` agent. It directly improves output quality and resource utilization, offering a compelling competitive advantage in the agent skill market.
Proprietary Technical Taxonomy
frame-selection learning dreaming feedback loop static + randomized frame selection frame-fitness prior problem-type tag embedding of the problem statement historical fitness prospective divergence

Raw Developer Origin & Technical Request

Source Icon GitHub Issue May 27, 2026
Repo: UditAkhourii/adhd
Frame-selection learning across runs (the "dreaming" feedback loop)

Right now frame selection is static + randomized: pick N frames per run, bias toward `code`/`design` tags when codeMode is on, reserve one slot for `wild`. Over many runs, certain frames consistently surface the non-obvious-but-viable pick for certain problem types (e.g. the regulator frame on streaming/audit problems).

**Closing the loop:**
1. After each run, log which frame produced the eventual top-scored idea (and which frame produced the trap-flagged ideas).
2. Build a frame-fitness prior keyed by problem-type tag (or by embedding of the problem statement).
3. Bias future frame selection toward frames with higher historical fitness on similar problems. Keep a small floor of exploration so new frames still get tested.

This is on the roadmap as "memory across runs — learn which frames win for which problem shapes." Escalating to a dedicated issue.

**Architectural framing from u/Plastic-Business-472:** ADHD = prospective divergence (fan out before answering). Dreaming = retrospective consolidation (what worked, compress and carry forward). The dreaming pass would feed back into which frames to spawn next time. Loop: ADHD → work → Dream → ADHD → work → Dream.

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*Raised by u/Plastic-Business-472 in the r/ClaudeCode thread.*

Developer Debate & Comments

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Adjacent Repository Pain Points

Other highly discussed features and pain points extracted from UditAkhourii/adhd.

Extracted Positioning
ADHD skill for coding agents: restructuring `SKILL.md` documentation for clarity and efficiency.
Optimizing `LLM` agent context loading and improving documentation clarity for developers.
Extracted Positioning
Hyperfocus / flow-state companion skill as part of a 'brain-model series' for `LLM` agents.
Expanding the `ADHD` product line with complementary cognitive emulation skills, addressing the full spectrum of `LLM` reasoning needs.
Extracted Positioning
ADHD skill for coding agents: demonstrating its value proposition through a `side-by-side example` in the `README`.
Making `ADHD`'s abstract benefits concrete and immediately understandable to new users, accelerating comprehension and adoption.
Extracted Positioning
ADHD skill for coding agents: clarifying its methodological distinction from simple 'think about alternatives' prompting.
Defending `ADHD`'s core architectural innovation of `parallel divergence` against oversimplification and demonstrating its superior efficacy.
Extracted Positioning
ADHD skill for coding agents: conducting `head-to-head evaluations` against competing `LLM` reasoning methods.
Establishing `ADHD`'s superior performance and unique value proposition through direct, quantitative comparison against state-of-the-art alternatives.

Frequently Asked Questions

Market intelligence mapped to ADHD skill for coding agents: implementing `frame-selection learning across runs` via a 'dreaming' feedback loop..

What problem does ADHD skill for coding agents: implementing `frame-selection learning across runs` via a 'dreaming' feedback loop. solve?
Based on our AI analysis of the original developer request, its primary technical positioning is: Enhancing `ADHD`'s adaptive intelligence and efficiency by dynamically optimizing `frame selection` based on historical performance.
Which technical concepts are associated with ADHD skill for coding agents: implementing `frame-selection learning across runs` via a 'dreaming' feedback loop.?
Our proprietary extraction maps ADHD skill for coding agents: implementing `frame-selection learning across runs` via a 'dreaming' feedback loop. to adjacent architectural concepts including frame-selection learning, dreaming feedback loop, static + randomized frame selection, frame-fitness prior.

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Cross-Market Term Frequency

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