Harden repeated delivery loops with evidence-based harness stripping, immutable runtime-path sprint packets, and broken-window revalidation without automatic revert. Use for Cardputer, browser, CI, runtime, or production work split into repeated sprints.
Review whether a recurring or semi-autonomous agent loop is ready to run. Use before scheduling Codex, Claude Code, Grok, Opencode, CI, issue triage, PR babysitting, dependency sweeping, changelog drafting, or other repeated agent workflows.
Design and evolve agent skills into cost-bounded loops, workflows, scripts, and memory updates. Use when creating or auditing reusable agent methods, converting prompts or skills into workflows, designing open or closed agent loops, defining supervisor/evaluator contracts, or deciding whether repeated agent work…
Build or refine small production-ready Lottie animations from SVGs, logos, UI states, loaders, and branded motion assets with a local preview harness, explicit inputs, and output verification. Use when the user asks for Lottie, JSON animation, SVG reveal animation, logo animation, or lightweight motion assets for…
Build the smallest correct code diff without overengineering, broad refactors, unnecessary dependencies, or speculative abstractions. Use when the user wants a minimal patch, small reviewable diff, stdlib-first implementation, or shortest-path fix that still preserves security, accessibility, trust-boundary…
Review frontend motion and animation code before shipping, with strict checks for purpose, frequency, easing, duration, transform origin, interruptibility, GPU-safe properties, reduced-motion support, and hover behavior. Use when a diff touches transitions, keyframes, Framer Motion, Lottie, drawers, popovers, toasts…
Review code or diffs specifically for needless complexity, replaceable dependencies, dead flexibility, and wrappers over stdlib or native platform behavior. Use when the user asks what can be deleted, simplified, inlined, replaced with stdlib/platform features, or whether a change is over-engineered.
Design or maintain a local personal AI workspace with a root router, isolated domain folders, user-triggered memory, decision logs, and sparse routing corrections. Use when a user wants one agent workspace spanning multiple projects or life/work domains without mixing context or bloating every session.
Execute large tasks through explicit phases with acceptance criteria, required commands, and state updates. Use when work is too large for one implementation pass but too important to leave as a vague checklist. Keep phases falsifiable, end-to-end, and small enough that each one can be verified honestly before moving…
Choose and run a compact pre-implementation, post-implementation, or ship-readiness gate for agent work. Use when a task needs ordered review lenses, proof gates, or release confidence without adopting a heavy multi-agent pipeline runtime.
Break a PRD, plan, or spec into independently executable vertical slices. Use when a product artifact needs to become agent-ready or human-ready implementation tickets, especially for client, product, and multi-track project workflows. Prefer thin end-to-end slices, explicit blockers, and issue-tracker-neutral output.
Audit a private skill, prompt pack, or assistant workflow before publishing it. Use when extracting public reusable method from client, product, team, or repository-specific agent instructions. Mark private vs mixed vs reusable content, rewrite the mixed layer, and gate release on decontextualization.
Instructions for markoblogo/AGENTS.md_generator, covering agents.md, repo context (read this first), guardrails (how to not break agents.md generator), workflow (how we ship changes in agents.md generator) and verification (don't trust yourself, verify).