bookhand

Automates browser and Electron app interactions for user-flow validation.

Benchmark validation procedure for one assigned benchmark-related target. For optimization EXP-* targets, independently classify candidate outcome. For engineering VAL-* or legacy engineering targets, prove or disprove the required benchmark/performance assertion.

Use when planning or replanning engineering missions that create, change, port, migrate, integrate, or preserve durable codebase behavior across UI, API, CLI, background jobs, data/migrations, libraries, or operator workflows. Defines investigation, scope inventory, coherent VAL-* contracts, evidence floors, multi-target task topology, engineering validation, root-cause patching, and durable guidance. For pure metric search, use optimization-mission-playbook instead.

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

Domain playbook for optimization missions — any task whose goal is to move a metric: performance, latency, throughput, memory, cost, score, quality, compression, ranking, solver, model/eval, and similar metric-improvement work. Defines how to think about and run an optimization mission: establishing the ground truth, trusting the measurement, profiling to the dominant cost, estimating the ceiling, generating and pruning disposable hypotheses, exploring cheaply before verifying expensively, guarding against metric gaming and overfitting, reasoning about correctness and trade-offs, and stopping on evidence.

Adversarial scrutiny procedure for engineering validation assignments. Runs hard-gate commands, reviews the current implementation and evidence integrity against assigned contracts, can use feature-reviewer lanes, and returns per-target verdicts.

Real-surface validation coordinator for engineering validation assignments. Exercises assigned assertions through browser, API, CLI, background, generated-artifact, migration/data, public-library, or parity surfaces and returns per-target verdicts with fresh evidence.