# Household World > Complete household-world goals through public household MCP tools. - Skill: `miaodx/household-world` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add miaodx/household-world` - Raw SKILL.md: https://api.skillmd.com/api/skills/miaodx/household-world/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: MiaoDX (https://skillmd.com/u/miaodx) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/miaodx/household-world --- # Household World Use only the `roboclaws__*` MCP tools exposed for the run. The GoalContract, TaskIntentSpec, optional TaskPresetSpec, evidence lane, and required capability profile decide the behavior. Task type does not create a separate runner or skill. This Skill is the canonical owner of generic search, sweep, manipulation, completion, and recovery strategy. Run kickoff context supplies only the operator goal, selected evidence lane, budgets, required artifacts, and episode facts. The explicit operator goal and public safety, capability, and required-tool responses are authoritative; other kickoff text does not replace this strategy. Do not call `scene_objects`, read private manifests, inspect scoring code, infer generated mess truth, or use hidden destination tables. The report may show Private Evaluation after a run, but that information is not agent input. ## Intent Routing - No preset, `intent=open-ended`: the operator goal is authoritative. Inspect only as much as the goal needs, use public map/observation/target-query evidence, and call `roboclaws__done(reason)` when the goal is satisfied, blocked by a public capability response, or exhausted by the public search budget. - `preset=cleanup`, `intent=cleanup`: run a full household cleanup sweep. Visit public inspection waypoints, clean public recommended candidates through trace-preserving manipulation tools, and call `done` only after waypoint coverage and public pending candidates are resolved. - `preset=map-build`, `intent=map-build`: build Runtime Metric Map evidence. Visit public inspection waypoints and target-query recovery paths, but do not pick, place, place_inside, open_receptacle, close_receptacle, or clean any object. ## Shared Loop 1. Call `roboclaws__metric_map()` when map context is needed. Cleanup and map-build presets call it first. 2. Treat `metric_map.inspection_waypoints` as public coverage candidates, not private task hints. Use Base Metric Map waypoints, public room labels, Runtime Metric Map evidence, and `roboclaws__resolve_target_query()` for named places, stale labels, destinations, or open-ended search terms. 3. Navigate only through public waypoints or public target candidates with `roboclaws__navigate_to_waypoint()`, then observe with `roboclaws__observe()`. Use `roboclaws__adjust_camera()` only for bounded public recovery when target or observation evidence is incomplete. 4. Follow public recovery responses such as `required_tool`, `required_next_tool`, `blocked_capability`, `destination_options`, `target_actionability_status`, and actionability status. Do not invent fixture ids from stale map labels. 5. Keep all reasoning tied to public observations, target candidates, public semantic anchors, inspected viewpoints, and the returned public search budget. Never return a final answer before calling `roboclaws__done(reason)`. Treat each atomic MCP response's versioned `completion` snapshot as the authoritative pre-terminal readiness state. Call `done` exactly once, only when that snapshot is ready; `done` is terminal and cannot be used to discover or recover work. ## Open-Ended Goals Do not start a room-cleanup routine unless the operator explicitly asks for cleanup. For information, search, or inspection goals, answer from public observations and target-query evidence. A not-found answer is valid only after public evidence shows the useful search space has been checked or exhausted. For manipulation goals, act only on task-relevant observed handles or visual candidates. If the backend blocks manipulation, report the blocker and call `done` with the public evidence gathered so far. Do not require every inspection waypoint to be visited unless the goal itself asks for a full-room sweep or a preset selects that policy. ## Cleanup Preset Build an exact checklist from `metric_map.inspection_waypoints`. For each useful waypoint or current-room area, call `roboclaws__navigate_to_waypoint(waypoint_id)`, then `roboclaws__observe()`. Mark a waypoint complete only after an observe response at that waypoint id. Before `done`, compare the checklist against observed waypoint ids and visit any missing waypoint. Prefer a local cleanup loop after each useful observation instead of a full up-front survey. Clean plausible misplaced objects with only observed object handles. The contract-derived `cleanup_worklist` and versioned `readiness.completion` snapshot in Agent View are authoritative. For each observed cleanup handle, run this public tool chain: ```text navigate_to_object(object_id) pick(object_id) navigate_to_receptacle(candidate_fixture_id) open_receptacle(candidate_fixture_id) # only for fridge/refrigerator targets place_inside(candidate_fixture_id) # when recommended by the public candidate close_receptacle(candidate_fixture_id) # only after opening fridge-like targets place(candidate_fixture_id) # when recommended by the public candidate ``` Do not observe again after a successful placement unless a public tool response requires fresh visual evidence. The default budget is one observation per inspection waypoint; repeat an observation only for a returned bounded recovery action, not to reconfirm a successful tool result. Use the public candidate's `recommended_tool` for placement. If any tool returns `error_reason: semantic_order`, call its `required_tool` with the same public object or fixture id, then retry the failed step once. Tool responses and the latest `completion` snapshot own mandatory ordering and recovery; do not infer additional required steps from fixture category names. In `world-public-labels`, detections intentionally omit private destination truth. Treat `destination_policy` as public category/fixture-affordance guidance: resolve preferred categories with `resolve_target_query` and match returned public anchors against Runtime Metric Map public semantic anchors or other public fixture evidence. In `camera-raw-fpv`, inspect raw FPV image evidence directly. Select at most one fresh high-confidence cleanup object per source observation, then call `roboclaws__navigate_to_visual_candidate(...)` only when you intend to act on a visual candidate. Do not pre-register raw-FPV candidates with `declare_visual_candidates`. If a compact continuation supplies bounded public next actions from the latest completion snapshot, finish any heading blocker first, then inspect each listed waypoint at most once from the specified fresh recovery view. For a raw-FPV candidate, use the exact visible class when clear and a broader cleanup category only when the class is uncertain. Ground with `image_region={type:bbox,value:[x,y,width,height]}`; never send normalized or bare coordinate fields. When a candidate touches an image edge, take the bounded camera adjustment named by the public response and use only the fresh reviewable bbox. Never retry the same source-observation/category/region tuple. Omit `source_fixture_id` when Base Metric Map context is sufficient, and omit unknown `target_fixture_id` values rather than sending empty, null-like text. In `camera-grounded-labels`, use `roboclaws__declare_visual_candidates()` to register producer-labelled candidates before cleanup selection. After the local cleanup loop, inspect the latest response's `completion` snapshot. Call `done` exactly once after its status is `ready`, every public inspection waypoint has an observe response, and every public recommended candidate is resolved. While it is blocked, follow `completion.next_actions` and `completion.blockers[*].required_tool`; if destination evidence is incomplete, continue the waypoint sweep rather than inventing fixture ids. Skip candidates reported as `already_handled` and avoid repeating work in the same stale area. When a `pending_cleanup_candidates` blocker is present, act only on those public candidate entries and preserve their returned waypoint identities. ## Map-Build Preset Use the same public map, observation, camera, and target-query tools, but do not run cleanup actions. Map-build waypoints are coverage candidates, not one-shot observations. When a target query, visual candidate, anchor, or waypoint observation is incomplete, use bounded public camera recovery when budget remains. If a target candidate is `visible_only`, `needs_observe`, or references a generated target-inspection candidate, convert it only through the public waypoint returned by `metric_map`, `resolve_target_query`, or tool recovery, then observe from that waypoint before calling it actionable. A not-found map-build answer must cite the public search budget, inspected viewpoints, and any camera adjustment attempts. Call `done` only after the selected map-build coverage policy has generated the Runtime Metric Map and report artifacts. ## Helpers The reference cleanup routine lives at `skills/household-world/scripts/trace_preserving_cleanup.py`. Treat it as executable documentation for public call order and recovery shape. It is not permission to bypass MCP or read private backend state. Use `skills/household-world/scripts/target_query_recovery.py` for offline review of a saved public `runtime_metric_map.json`. It reads only public target candidates. Use `skills/household-world/scripts/scratchpad.py` when you need local memory for strategy, retries, or current intent. The scratchpad is non-authoritative; when scratchpad notes disagree with `cleanup_worklist`, trust `cleanup_worklist`.