scene-gaussian-map-alignment

scene-gaussian-map-alignment is a skill for Claude Code, Codex from MiaoDX/roboclaws. It costs 86 tokens per session (1,448 once invoked), scanned A, original, MIT.

A workflow for aligning 3D scene assets, robot maps, and semantic locations into a digital-twin evidence report.

In plain words
What is it for?
Connecting Gaussian or splat scenes, 3D models, occupancy maps, navigation anchors, and operator reports while recording evidence and assumptions.
Why use it?
It separates blocked, candidate, verified, and runtime-proven alignments so uncertain coordinate matches are not presented as fact.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is ./scripts/dev/run_pytest_standalone.sh \.

Good fit Connecting Gaussian or splat scenes, 3D models, occupancy maps, navigation anchors, and operator reports while recording evidence and assumptions.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/MiaoDX/roboclaws
agentmods
npx agentmods add skills/miaodx/roboclaws/scene-gaussian-map-alignment

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for scene-gaussian-map-alignment

README.md
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Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,448 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00086 $0.01448
Opus 5 $0.00043 $0.00724
Sonnet 5 $0.00017 $0.00290
Haiku 4.5 $0.00009 $0.00145

Measured 9d ago against content hash 7e1431dfaf2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

scene-gaussian-map-alignment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/summarize_alignment_evidence.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/scene-gaussian-map-alignment/SKILL.md · 151 lines

How it starts

The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scene Gaussian Map Alignment

Use this when a bundle combines a 3D Gaussian/splat/PLY/USD/OBJ scene, a robot map such as Nav2 YAML plus occupancy PGM, semantic anchors from navigation_memory.json, or an Isaac/operator-console report that must label evidence honestly.

Boundary

Keep stable, repeatable work in scripts. The skill owns the judgment that changes from scene to scene:

  • Stable scripts parse headers, bounds, map metadata, semantic-memory JSON, transforms, smoke artifacts, images, and HTML reports.
  • The skill decides the evidence tier, asks for missing assets, chooses which anchors are credible, names blockers, and prevents overclaiming.
  • Do not hide scene-specific assumptions in a script default. If an assumption will vary with the next Gaussian scene, write it in the skill/report as an explicit decision.

Evidence Tiers

Use these labels consistently:

  • blocked: geometry, map files, semantic anchors, or coordinate evidence are missing.
  • candidate: bbox fit, scale/translate, manual placement, or another heuristic alignment exists.
  • verified: named physical/semantic anchors match across map and scene with residuals recorded.
  • runtime_proven: Isaac or robot-view smoke renders/navigates through candidate waypoints and writes view evidence.
  • planner_backed: a real planner/Nav2-equivalent path proof exists.

Never skip tiers in wording. A runtime smoke can prove that rendered robot views exist at candidate poses; it does not by itself prove Nav2 planner parity.

Workflow

  1. Inventory every asset before aligning: Gaussian/splat/PLY files and whether they are rendered or only inspected, USD/OBJ/mesh world bounds, Nav2 YAML, occupancy grid, semantic memory, map-bundle context, anchor ids, and any segmentation/object manifest/correspondence/calibration evidence.

  2. Run the deterministic tools that apply to the available assets:

    python -m roboclaws.household.agibot_map_bundle \
      --source-map-dir <map-root> \
      --context-json <context-json> \
      --output-dir assets/maps/<bundle-name>
    
    .venv-isaaclab/bin/python -m roboclaws.backends.isaaclab.b1_readiness \
      --b1-root <scene-root> \
      --map12-root <map-root> \
      --output output/<run>/readiness.json
    
    .venv-isaaclab/bin/python -m roboclaws.backends.isaaclab.b1_navigation_smoke \
      --b1-root <scene-root> \
      --map12-root <map-root> \
      --output-dir output/<run> \
      --accept-nvidia-eula
    
    .venv-isaaclab/bin/python -m roboclaws.backends.isaaclab.b1_readiness \
      --b1-root <scene-root> \
      --map12-root <map-root> \
      --navigation-artifact output/<run>/navigation_smoke.json \
      --require-navigation-success \
      --output output/<run>/readiness_with_navigation.json
    
    python -m roboclaws.backends.isaaclab.b1_navigation_report \
      --run-dir output/<run>
    

Read the full file on GitHub · 151 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 151 lines · 86 tokens per session scan A 7e1431dfaf2b

Subscribe to this mod's changes

scene-gaussian-map-alignment is a skill published in the GitHub repository MiaoDX/roboclaws (6 stars, last pushed 6d ago), licensed MIT. It adds 86 tokens to every session and 1,448 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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