{"slug":"jaccen-3dgs-mcp-renderer","name":"3dgs-mcp-renderer","description":"MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering.","long_description":"---\nname: 3dgs-mcp-renderer\ndescription: \"MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering.\"\nlicense: Apache-2.0\nmetadata:\n  version: \"1.1.0\"\n  author: jaccen\n  tags: [\"mcp\", \"3dgs\", \"gaussian-splatting\", \"rendering\", \"three.js\", \"webgpu\", \"voice\", \"agent\", \"interactive\", \"spec-first\", \"sculpting\", \"code-first\", \"slat\", \"latent-editing\"]\n  disable-model-invocation: true\n  user-invocable: true\n---\n\n# 3DGS MCP Renderer — Agent-3DGS Interaction via MCP Protocol\n\nPrototype specification for integrating MCP (Model Context Protocol) with 3DGS rendering pipelines, enabling AI Agents to directly manipulate Three.js/3DGS rendering parameters and achieve voice-driven 3D scene reconstruction.\n\n## Architecture\n\n```\n┌─────────────┐     ┌─────────────┐     ┌──────────────────┐     ┌──────────────────┐\n│ Voice/Text  │────▶│   Agent     │────▶│  MCP Server      │────▶│  3DGS Renderer   │\n│ (Whisper/   │     │ (Claude/    │     │  (Node.js/       │     │  (Three.js/      │\n│  Prompt)    │     │  TeleClaw)  │     │   Python)        │     │   WebGPU/HiGS/   │\n│             │◀────│             │◀────│                  │◀────│   DDF-GS)        │\n└─────────────┘     └─────────────┘     └──────────────────┘     └──────────────────┘\n                        │                      │                       │\n                        │  Tool calls          │  WebSocket/HTTP       │  WebGL/WebGPU/\n                        │  (MCP protocol)       │  transport            │  HiGS/DDF-GS\n```\n\n## Spec-First Sculpting Pipeline (v0.9.0)\n\n> **Design inspiration**: img2threejs (GitHub: img2threejs/img2threejs) — open-source AI Skill that converts a single image into an interactive Three.js 3D model via a stage-gated sculpting pipeline. We borrow two core principles: (1) **spec-first** — define quality criteria and component hierarchy before any rendering; (2) **stage-gated sculpting** — progressive refinement with acceptance checks at each stage.\n\n### Why Spec-First for MCP Rendering?\n\nThe original MCP pipeline was **reactive**: user issues a voice command → agent maps to a tool → render → verify. This works for single-step edits but fails for complex scene construction because:\n\n- No upfront quality criteria → agent cannot self-assess before rendering\n- No stage gates → errors compound across steps (bad camera → bad selection → bad edit)\n- No component hierarchy → edits are flat, no part-level control\n\n**The fix**: Introduce a `define_scene_spec` tool that runs *before* any sculpting/editing tools. This produces a machine-readable Object Spec that subsequent tools reference as acceptance criteria.\n\n### The 6-Stage Sculpting Pipeline\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│                    SPEC-FIRST SCULPTING                      │\n│                                                             │\n│  ┌──────────────┐                                           │\n│  │ define_scene │  ← Object Spec: component hierarchy,      │\n│  │ _spec        │    material system, quality criteria      │\n│  └──────┬───────┘                                           │\n│         │                                                   │\n│         ▼                                                   │\n│  Stage 1: blockout    → Bounding boxes, camera framing     │\n│         │  gate: bbox coverage ≥ spec.target_coverage?      │\n│         ▼                                                   │\n│  Stage 2: structural  → Part decomposition, hierarchy      │\n│         │  gate: part count & nesting matches spec?         │\n│         ▼                                                   │\n│  Stage 3: form        → Gaussian density/scale/rotation     │\n│         │  gate: PSNR estimate ≥ spec.min_psnr?             │\n│         ▼                                                   │\n│  Stage 4: material    → PBR/SH assignment per part         │\n│         │  gate: material count per part matches spec?      │\n│         ▼                                                   │\n│  Stage 5: surface     → Normal consistency, thin structures │\n│         │  gate: normal consistency score ≥ spec.threshold? │\n│         ▼                                                   │\n│  Stage 6: lighting    → Environment, shadows, AO           │\n│            gate: render quality score ≥ spec.target_score?  │\n└─────────────────────────────────────────────────────────────┘\n```\n\nEach stage is an MCP tool call. The agent renders a frame after each stage, evaluates against the gate, and either advances or retries. This mirrors img2threejs's `blockout → structural → form → material → surface → lighting` flow.\n\n### Gate Evaluation Protocol\n\nFor each stage gate, the agent follows this protocol:\n\n```\n1. Execute stage tool (e.g., sculpt_form with parameters)\n2. Call render_frame() to get current visual state\n3. Call query_scene(query_type=\"stats\") to get quantitative metrics\n4. Compare metrics against spec gate criteria\n5. If pass → advance to next stage\n6. If fail → adjust parameters and retry (max 3 attempts)\n7. If 3 failures → report to user with diagnostic info\n```\n\n### Voice-Driven Sculpting Example\n\n> **Loaded on demand** — See [mcp-tools-spec.md](references/mcp-tools-spec.md) for the full voice-driven sculpting example (desk scene with 8-step agent pipeline).\n\n## Code-First Rendering Philosophy (v0.9.0)\n\n> **Design inspiration**: img2threejs outputs pure Three.js code (not GLB/OBJ/PLY), making every model fully editable, version-controllable, and lightweight. We adopt this philosophy for 3DGS scene export.\n\n### Traditional 3DGS Export vs Code-First Export\n\n| Aspect | Traditional (.ply/.splat) | Code-First (.js + .splat) |\n|--------|--------------------------|--------------------------|\n| Editability | Binary blob, hard to edit | Source code, any field adjustable |\n| Version control | Binary diff, no merge | Text diff, git-friendly |\n| File size | Full Gaussian set (MB-GB) | Code skeleton (KB) + compressed splat data |\n| Scene composition | Single flat Gaussian cloud | Hierarchical code with part-level control |\n| Interaction logic | Must be added externally | Embedded in code |\n| 3DGS data | All in one file | Separate .splat file loaded by code |\n| Procedural elements | Not supported | Parametric geometry in code (e.g., desk surface = PlaneGeometry) |\n\n### Hybrid: Procedural Code + 3DGS Splatting\n\nThe key insight: **not everything needs to be Gaussians**. For a desk scene:\n- Desk surface → procedural `BoxGeometry` in code (simple, editable, lightweight)\n- Monitor screen texture → procedural `MeshStandardMaterial` (or 3DGS if view-dependent)\n- Complex organic objects → 3DGS splatting data (where procedural code can't compete)\n\n> **Loaded on demand** — See [code-first-examples.md](references/code-first-examples.md) for hybrid export code examples.\n\n### When to Use Code-First vs Pure Splat\n\n| Scene Element | Recommendation | Why |\n|--------------|---------------|-----|\n| Flat surfaces (walls, floors, desks) | Procedural code | Simple, editable, tiny file size |\n| Parametric objects (cabinets, shelves) | Procedural code | Adjust dimensions in code |\n| Organic objects (plants, food, fabric) | 3DGS splat | Can't match quality procedurally |\n| View-dependent surfaces (screens, mirrors) | 3DGS splat | SH coefficients capture view dependence |\n| Articulated parts (joints, hinges) | Procedural code | Joint parameters are explicit in code |\n| Mixed scenes (most real cases) | Hybrid code + splat | Best of both worlds |\n\n### SLAT Connection\n\nThe code-first approach connects to SLAT (see `../../references/slat-unified-representation.md`): the structured latent's voxel grid naturally maps to a procedural geometry skeleton, while the per-voxel features decode to 3DGS splatting for complex regions. **SLAT encode → hierarchical decode: simple voxels → procedural code, complex voxels → 3DGS splats.**\n\n## SLAT Latent Editing (v1.0.0)\n\n> **Theoretical basis**: SLAT (Structured Latent Aggregation Transform) — see `../../references/slat-unified-representation.md`. A scene is encoded into a compact structured latent (a voxel grid over the scene, each voxel aggregating local Gaussian features), edited in latent space, then re-decoded back to a Gaussian set. This lets the agent manipulate entire semantic regions with a single operation, independent of per-Gaussian IDs.\n\n### Encoding: Scene → Structured Latent\n\n`encode_scene_slatent` voxelizes the active scene into a regular grid (`voxel_size`, default 1.0), assigning each Gaussian to a voxel by position. Each voxel stores an aggregated feature vector (mean position, mean scale, mean color, mean opacity, size, plus optional weighted semantic/part labels). The result is a `slat_id` referencing an in-memory snapshot with an `encode_loss` (reconstruction RMSE), letting the agent judge fidelity before editing.\n\n### Editing in Latent Space\n\n`edit_scene_latent` applies a `LatentEditOp` to voxels matched by a `LatentSelector` (by voxel ids, a spatial box, or a part name — substring, case-insensitive). Seven operations are supported:\n\n| Op | Fields | Effect |\n|----|--------|--------|\n| `translate` | `delta: Vec3` | Move matched voxels (and their Gaussians) by a vector |\n| `scale` | `factor: number`, `origin: Vec3` | Scale voxel positions relative to an origin |\n| `rotate` | `angleDeg: number`, `axis: Vec3`, `origin: Vec3` | Rotate voxels around an axis (degrees) |\n| `recolor` | `color: Vec3`, `mix: number` | Blend matched voxels' colors toward a target |\n| `opacity` | `opacity: number`, `mode` | Set or scale opacity (mode `set`/`scale`) |\n| `smooth` | `iterations: number`, `strength: number` | Smooth feature positions/colors by averaging neighbors |\n| `delete` | `target: \"voxel\"` | Remove all Gaussians in matched voxels |\n\n**Schema vs core naming**: the MCP JSON schema uses snake_case (`angle_deg`); the internal `LatentEditOp` uses camelCase (`angleDeg`). Handlers convert at the boundary. Library/test callers use camelCase directly.\n\n**Safety gate**: `edit_scene_latent` computes `affected_gaussians`; if this exceeds 10% of the scene, the edit is rejected unless `confirm=true`. This reuses the project-wide 10% safety rule.\n\n**Apply to scene**: with `apply_to_scene=true` (default) the edit is re-decoded and broadcast to the renderer via `modify_gaussians`; with `false` it only updates the in-memory snapshot, so the agent can preview/cancel before committing.\n\n### Decoding: Latent → Scene\n\nDecoding rebuilds the Gaussian set: matched voxels are re-instantiated from edited features, untouched voxels keep their original Gaussians. `delete` removes the affected Gaussians entirely.\n\n### Voice-Driven SLAT Example\n\n> **Loaded on demand** — See [mcp-tools-spec.md](references/mcp-tools-spec.md) for full SLAT voice examples (\"encode the scene\", \"move the cluster left\", \"scale the group up\", etc.).\n\n## Cross-Scene Latent Transfer & Interpolation (v1.1.0)\n\nv1.1 extends SLAT beyond a single scene. A latent edit computed on one scene (source) can now be **transferred** to another scene (target), or the two scenes can be **interpolated** in latent space. Both operations rely on a spatial correspondence built over the voxel grids.\n\n### Correspondence: Voxel Grid Matching\n\nBoth operations build a voxel grid over the source scene (cell size = `match_radius`) via `buildVoxelGrid`, then for each target voxel find the nearest source voxel within `match_radius` (`nearestVoxel`, 3×3×3 neighborhood search). The resulting pairs carry the relative changes across scenes.\n\n### Transferring a Latent Edit\n\n`transfer_scene_edit` re-applies a `LatentEditOp` from source to target **as a relative change**:\n\n| ","tagline":"MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. 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Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":80,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Stars/forks activity: 149 stars, 10 forks; issue activity unavailable in current metadata","Permission surface: shell or command execution, filesystem or document access"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":68,"label":"Promising"},"supply":{"track":"Coding and developer agents","scenario":"GitHub automation","maintenance":"2d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","High-risk permission hints: Shell or command execution","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"],"agent_contract":{"task_input":"Use 3dgs-mcp-renderer in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 77/100 Strong shortlist","Audit: 80/100 Needs review","Safety: 44/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"jaccen-3dgs-mcp-renderer (3dgs-mcp-renderer)","install_command":"npx skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-mcp-renderer","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"jaccen-3dgs-mcp-renderer","task":"Use 3dgs-mcp-renderer in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/jaccen-3dgs-mcp-renderer","api":"https://www.openagentskill.com/api/agent/skills/jaccen-3dgs-mcp-renderer","audit":"https://www.openagentskill.com/skills/jaccen-3dgs-mcp-renderer/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=jaccen-3dgs-mcp-renderer&task=Use%203dgs-mcp-renderer%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%203dgs-mcp-renderer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%203dgs-mcp-renderer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/jaccen-3dgs-mcp-renderer/install","manifest":"https://www.openagentskill.com/api/registry/manifest/jaccen-3dgs-mcp-renderer"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"jaccen-3dgs-mcp-renderer","name":"3dgs-mcp-renderer","description":"MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering.","category":"automation","url":"https://www.openagentskill.com/skills/jaccen-3dgs-mcp-renderer","repository":"https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer","github_repo":"jaccen/Awesome-Gaussian-Skills"},"suited_tasks":["GitHub automation workflows","Claude Code teams","builders willing to evaluate younger projects","Inspect repository metadata","Compare code changes","Write concise engineering summaries","Inspect source files","Explain architecture"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-mcp-renderer","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add jaccen-3dgs-mcp-renderer"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"3dgs-mcp-renderer\" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"jaccen-3dgs-mcp-renderer\",\"task\":\"Install 3dgs-mcp-renderer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"3dgs-mcp-renderer\" as a Claude Code skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"jaccen-3dgs-mcp-renderer\",\"task\":\"Install 3dgs-mcp-renderer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"3dgs-mcp-renderer\" from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"jaccen-3dgs-mcp-renderer\",\"task\":\"Install 3dgs-mcp-renderer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/jaccen-3dgs-mcp-renderer/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/jaccen-3dgs-mcp-renderer"},"trust":{"score":77,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"149 GitHub stars","repoActivity":"149 stars, 10 forks","lastPushed":"2d since push","license":"Apache-2.0","repository":"https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer","install":"npx skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-mcp-renderer","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["automation","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Stars/forks activity: 149 stars, 10 forks; issue activity unavailable in current metadata","Permission surface: shell or command execution, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":80,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Stars/forks activity: 149 stars, 10 forks; issue activity unavailable in current metadata","Permission surface: shell or command execution, filesystem or document access"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":68,"label":"Promising"},"supply":{"track":"Coding and developer agents","scenario":"GitHub automation","maintenance":"2d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","High-risk permission hints: Shell or command execution","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"],"agent_contract":{"task_input":"Use 3dgs-mcp-renderer in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 77/100 Strong shortlist","Audit: 80/100 Needs review","Safety: 44/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"jaccen-3dgs-mcp-renderer (3dgs-mcp-renderer)","install_command":"npx skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-mcp-renderer","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"jaccen-3dgs-mcp-renderer","task":"Use 3dgs-mcp-renderer in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/jaccen-3dgs-mcp-renderer","api":"https://www.openagentskill.com/api/agent/skills/jaccen-3dgs-mcp-renderer","audit":"https://www.openagentskill.com/skills/jaccen-3dgs-mcp-renderer/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=jaccen-3dgs-mcp-renderer&task=Use%203dgs-mcp-renderer%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%203dgs-mcp-renderer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%203dgs-mcp-renderer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/jaccen-3dgs-mcp-renderer/install","manifest":"https://www.openagentskill.com/api/registry/manifest/jaccen-3dgs-mcp-renderer"}},"supply_profile":{"track":{"slug":"coding","label":"Coding and developer agents","shortLabel":"Coding","description":"Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills."},"scenario":{"label":"GitHub automation","description":"I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.","useCases":[{"slug":"github-automation","title":"GitHub automation"},{"slug":"coding-agents","title":"Coding agents"},{"slug":"rag-knowledge","title":"RAG and knowledge"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-mcp-renderer","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":149,"starsLabel":"149","forks":10,"license":"Apache-2.0","qualityScore":68,"trustScore":77,"auditScore":80},"maintenance":{"status":"fresh","label":"2d since push","daysSincePush":2,"lastPushedAt":"2026-09-04T07:21:49+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access"]},"coverageTags":["Coding","GitHub automation","automation","agent-skill"]},"audit":{"audit_score":80,"risk_level":"needs_review","risk_label":"Needs review","quality_score":68,"trust_score":77,"maintenance_score":100,"security_score":82,"install_score":92,"warnings":["Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","Stars/forks activity: 149 stars, 10 forks; issue activity unavailable in current metadata","Permission surface: shell or command execution, filesystem or document access"]},"quality_signals":{"model":"v2","star_score":15.23,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"github-automation","title":"GitHub automation","url":"https://www.openagentskill.com/use-cases/github-automation"},{"slug":"coding-agents","title":"Coding agents","url":"https://www.openagentskill.com/use-cases/coding-agents"},{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"}],"stacks":[{"slug":"coding-review-agent","title":"Coding review agent","url":"https://www.openagentskill.com/collections/coding-review-agent"},{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"},{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"}],"install":"npx skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-mcp-renderer","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add jaccen-3dgs-mcp-renderer","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"3dgs-mcp-renderer\" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"jaccen-3dgs-mcp-renderer\",\"task\":\"Install 3dgs-mcp-renderer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"3dgs-mcp-renderer\" as a Claude Code skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"jaccen-3dgs-mcp-renderer\",\"task\":\"Install 3dgs-mcp-renderer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"3dgs-mcp-renderer\" from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"jaccen-3dgs-mcp-renderer\",\"task\":\"Install 3dgs-mcp-renderer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer","github_repo":"jaccen/Awesome-Gaussian-Skills","version":"1.0.0","license":"Apache-2.0","urls":{"web":"https://www.openagentskill.com/skills/jaccen-3dgs-mcp-renderer","repository":"https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/3dgs-mcp-renderer","api":"/api/agent/skills/jaccen-3dgs-mcp-renderer","install_api":"/api/skills/jaccen-3dgs-mcp-renderer/install"},"meta":{"created_at":"2026-09-04T08:33:50.544962+00:00","updated_at":"2026-09-04T08:33:50.762915+00:00","agent_friendly":true}}