maos

Front-desk concierge for the Open Design platform (`nexu-io/open-design`) — onboard · teach the platform (od CLI, 152 design-systems, MCP) · guide an intent to the right integration tier + exact `od` invocation · audit whether Open Design is installed/configured. Thin wrapper over the `opendesign-concierge` skill. Routes to the od CLI / design-system runbooks — reimplements nothing, never re-clones Open Design.

Close the session out cleanly + hand it off (exit-hygiene sweep, debrief, ai-agnostic continuation seed, optional auto-spawn of the next session)

Orient + heal + isolate the git workspace before work (branch detect, safe heal from origin, lazy worktree)

Drive the current session to QUIESCENCE — no open ticket/gap/fix/failure/PR, every PR green + answered, agentic convergence — by composing the native /goal condition-loop with a pluggable inner driver (default auto-pilot). Override-friendly.

Reverse-engineer source code into an OpenSpec SPEC model (as-built behavioral contract) — code as read-only oracle, distill→recast→faithfulness-check→validate→score-card. Thin wrapper over the `reveng` skill. src→spec is the priority pair; other source→target pairs are roadmap.

Alexandr Wang thinking archetype — Scale AI founder. Use when you need data quality thinking, AI infrastructure strategy, data labeling pipeline design, or enterprise AI deployment reasoning.

Dario & Daniela Amodei thinking archetype — Anthropic founders. Use when you need safety-first AI reasoning, responsible scaling, alignment-aware design, or balancing capability with safety in AI systems.

Andrew Bosworth thinking archetype — Meta CTO, Reality Labs. Use when you need hardware-software integration thinking, AR/VR platform strategy, developer platform design, or balancing ambitious R&D with shipping products.

Bill Gates thinking archetype — Microsoft founder, philanthropist. Use when you need systematic long-term analysis, pragmatic problem decomposition, technology-for-impact thinking, or evaluating business viability.

Demis Hassabis thinking archetype — DeepMind founder. Use when you need first-principles reasoning about intelligence, scientific rigor in AI systems, game-theoretic thinking, or bridging neuroscience with engineering.

Analyze tasks and recommend optimal sub-agent(s) for execution

Use when about to spawn a subagent/skill/task (Task tool, Agent tool, /command). Defines 6 decision criteria (decomposable/specialist-exists/audit-capacity/score≥MEDIUM/not-HUMAN-DOMAIN/time-budget), 10 mandatory briefing components (context/scope/motivation/purpose/objective/DoR/DoD/deliverables/feedback-loops/constraints), accountability preservation (parent NEVER delegates accountability — only execution; "delegating does not waive the responsibility received"), recursion ≤2, parallel ≤3. Harmonizes the agentic-inheritance principle (tree-returns-to-root · subordinate-is-parent's-full-responsibility · audit-output · zero-drift). Cross-vendor AAIF.

Generic, vendor-neutral session-observability engine (ASH — Agentic Session Harness). Per-session journals capturing goal · tasks · decisions[] · sources · transcript_hash, plus a decision-audit (why the agent decided X, spec_alignment drift). Ships CLIs (agentic-walkthrough timeline · agentic-decisions audit report · agentic-decide capture · agentic-reindex backfill) + SessionStart/Stop hooks. Use when you need an auditable record of what an agent did and WHY across sessions. Promoted from a host product 2026-06-02 (Layer-1 community engine).

Use when you need to evaluate, test, score, benchmark, or QA an agentic-tool (a skill/SKILL.md, agent, subagent, slash-command, prompt, or MCP-tool) — e.g. "is this skill any good?", "does this skill actually trigger?", "test this agent", "did my edit regress the skill?", "compare these two skill versions", "score this command". Produces a behavioral eval report; does NOT author or modify the tool.

Use when you want to turn a raw intent/instruction into a REUSABLE agentic-tool — e.g. "turn this into a skill", "forge an agentic-tool for X", "make this a recurring command/agent", "convert these instructions into a tool", "criar um agentic-tool para …", "research then build the best tool for …". Researches pre-existing internal + external solutions FIRST (DRY), decides the OPTIMAL artifact TYPE among {prompt · skill · command · agent/subagent · mcp · plugin · marketplace · rule/hook}, names it (delegating to `anima` when present, else 5-axis inline fallback), makes it AI-agnostic + multi-agentic, then forges + saves it (operator-confirmed). The genesis stage of the agentic-tool lifecycle (forge → evaluate → train → operate → deprecate). Hands off to agentic-tool-evaluator + -trainer. Cross-vendor AAIF (Claude / Cursor / Codex / Copilot / Gemini / Aider).

10 hooks across 5 events