anionzo

Use when the user asks to create a persistent repo-local Codex agent, scaffold `.codex/agents/*.toml`, define a reusable specialist role, or match an agent to existing local skills and MCP servers.

Use when designing or implementing a premium animated landing page with AI-generated visuals or looping video, especially when the workflow involves Higgfield-style image and video generation, Google AI Studio-style code prompting, React or Tailwind landing-page implementation, dark-mode video blending, scroll-tied playback, aspect-ratio-matched media blocks, or motion-first section design.

Use when the user asks to fine-tune on books, create an SFT dataset from books, train a style-transfer or author-voice model, extract ePub text, segment long-form book content, or prepare literary data for LoRA or small-model training.

Bootstrap a new AI-agent session by reading the repository operating docs and rebuilding project understanding from source. Use when Codex needs to start a new conversation, get up to speed on an unfamiliar repo, read AGENTS.md and README.md completely before acting, investigate the codebase to understand the project's purpose and architecture, or turn a rough onboarding prompt into an execution-ready repo-orientation prompt.

Use when reviewing completes or work is intentionally abandoned. Extract durable patterns, decisions, and failures into history/learnings, then promote only critical reusable lessons to critical-patterns.md.

Use when Codex should turn a concept, topic, tutorial, lesson, algorithm, system, workflow, or abstract idea into a playable browser game that teaches through interaction. Use for planning or implementing educational web games with the Game Studio plugin, including concept-to-mechanic translation, gameplay loop design, learning progression, and browser-game QA.

Use when blocked workers, test failures, build errors, runtime crashes, or integration issues need systematic root-cause debugging. Reads history/learnings/critical-patterns.md to avoid re-solving known issues. Writes debug notes that compounding can later capture.

Use when you need a manual dream-style consolidation pass over Codex artifacts and existing Khuym learnings, including bootstrap-first scans, recurring-window updates, ambiguity resolution for merge/create new/skip, and approval-gated critical-pattern proposals.

Use when designing or implementing a premium animated landing page with AI-generated visuals or looping video, especially when the workflow involves Higgfield-style image and video generation, Google AI Studio-style code prompting, React or Tailwind landing-page implementation, dark-mode video blending, scroll-tied playback, aspect-ratio-matched media blocks, or motion-first section design.

Use when the user asks to fine-tune on books, create an SFT dataset from books, train a style-transfer or author-voice model, extract ePub text, segment long-form book content, or prepare literary data for LoRA or small-model training.

Research the codebase, turn locked decisions into a plain-English phase plan, prepare the current phase for execution, and validate it through a mandatory gate before any code is written. Covers discovery, synthesis, phase planning, contract/story/bead creation, and the full validation gate (structural verification across 8 dimensions, spike execution for HIGH-risk items, bead graph polishing with bv analytics, and explicit user approval). Use after anionzo:exploring completes. Reads CONTEXT.md, retrieves institutional learnings, runs discovery and synthesis, writes discovery.md, approach.md, phase-plan.md, and then writes current-phase contract/story artifacts plus beads for that phase only. No execution begins until the validation gate passes and the user explicitly approves.

Post-execution quality verification skill for the anionzo ecosystem. Invoke after the final phase swarm completes. Runs 5 parallel specialist review agents, 3-level artifact verification, human UAT, and finishing (PR, cleanup, epic close). Review issues become beads instead of per-finding markdown files; P1 still blocks merge while P2/P3 become non-blocking follow-up beads. Absorbs finishing responsibilities and hands off to compounding.

Orchestrates parallel worker agents for phase execution and defines the worker implementation loop. Use after the anionzo:validating skill approves the current phase for execution. Initializes the overseer/orchestrator context, spawns bounded worker subagents, monitors Agent Mail for completions/blockers/file conflicts, coordinates rescues and course corrections, and hands off either to planning for the next phase or to reviewing after the final phase. The orchestrator TENDS — it never implements beads directly. Workers self-route from the live bead graph, implement beads atomically, and report back through Agent Mail.

Bootstrap meta-skill for the anionzo agentic development ecosystem. Load first on any anionzo project. Lists all 9+2 skills with routing logic, session scout/bootstrap, small-change vs standard-feature vs high-risk mode selection, go mode (full-auto pipeline with 4 human gates), priority rules, and state resume. Invoke when starting a new session, choosing which skill to use, running the full pipeline end-to-end, or resuming after a handoff.

Use when starting a new session, when the task shape is unclear, when a request mixes multiple intents, or when you need to route to the right skill and working mode. This is the router — load it first, then hand off to the correct skill.

Use when creating a new anionzo skill, editing an existing anionzo skill, or verifying a skill works under pressure before deploying. Use when you need an agent skill to be bulletproof against rationalization. Do NOT use for project-specific AGENTS.md conventions or one-off solutions.

Review code for correctness, security, performance, maintainability, and test quality. Use when the user asks for a review, audit, or change assessment.

Debug failures systematically by reproducing the issue, isolating the cause, applying a fix, and verifying the outcome. Use when the user reports a bug, crash, flaky behavior, or failing test.

Refactor code safely using small steps, tests, and proven patterns. Use when the user asks to clean up code, reduce duplication, or improve structure without changing behavior.