Creator · MengTo
Last updated · Sep 1, 2026
Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification.
Creator · MengTo
Last updated · Sep 1, 2026
Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification.
Creator · MengTo
Last updated · Sep 1, 2026
Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification.
Creator · MengTo
Last updated · Sep 1, 2026
Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification.
Review then install
Install targets
Codex install prompt
Install the "author-game-levels" agent skill from https://github.com/MengTo/Skills/tree/main/agent-skills/game-development/author-game-levels. 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: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. 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":"mengto-author-game-levels","task":"Install author-game-levels","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.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + Browser agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add MengTo/Skills --skill author-game-levels
Maintenance
fresh
8d since push
Risk
Safe to try
Quality score needs review
GitHub quality
5.7K
84/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
5.7K GitHub stars
Repo activity
5.7K stars, 685 forks
Maintenance
8d since push
License
MIT
Install
npx skills add MengTo/Skills --skill author-game-levels
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add MengTo/Skills --skill author-game-levelsDo not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mengto-author-game-levels/install
Agent should check
Copy prompt
Task: Use author-game-levels in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mengto-author-game-levels/install
Install command: npx skills add MengTo/Skills --skill author-game-levels
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mengto-author-game-levels/install
LLM text format
/api/skills/mengto-author-game-levels/install?format=text
Find alternatives
/api/skills/search?q=author-game-levels&limit=3
Agent prompt
Use author-game-levels for this task. Review https://www.openagentskill.com/api/skills/mengto-author-game-levels/install, then install with: npx skills add MengTo/Skills --skill author-game-levelsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mengto-author-game-levels
LLM text
/api/registry/manifest/mengto-author-game-levels?format=text
Install alias
/api/registry/install/mengto-author-game-levels
Recommend
/api/registry/recommend?task=Use%20author-game-levels%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
Use-case tags
Platforms
Claude Code, Browser agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Browser automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS5.7K GitHub stars
Stars/forks activity
PASS5.7K stars, 685 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
--- name: author-game-levels description: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. ---
# Author Game Levels
Treat architecture as gameplay communication. Every route, arena, gate, prop, and light must help the player read movement, threats, objectives, or state.
## Enforce one gameplay plane
Keep all collision, navigation, encounter routes, objectives, pickups, and player movement on one accessible plane.
- Do not add stairs, ramps, raised platforms, drop-offs, cliffs, bridges, ledges, pits, or vertical traversal. - Do not change gameplay elevation for shortcuts, arenas, hazards, rewards, or visual variety. - If visual height is requested later, keep it non-walkable background dressing. It must not alter navigation, camera occlusion, threat visibility, targetability, or player movement.
## Separate level systems
Maintain explicit, independently testable layers for:
- authored level data and stable zone/anchor IDs; - visual geometry and non-walkable background dressing; - simplified collision geometry; - flat navigation data and movement clearance; - encounter, enemy, gate, reset, pickup, objective, and exit zones.
Share stable IDs and transforms between layers, but never infer collision, navigation, or encounter boundaries from decoration alone.
## Lay out readable play
1. Define the architectural purpose of each space: traversal, orientation, combat, recovery, reward, transition, or objective. 2. Preserve clear movement, camera, and dodge corridors at the intended play distance. 3. Keep threats, pickups, exits, gates, and interaction targets visible before commitment. 4. Telegraph encounters through visible arena shape, approach, state change, and stable zone anchors. 5. Deliberately place arenas, gates, checkpoints, retry spawns, and reset paths. Prevent soft locks, duplicate rewards, hidden re-entry, and enemies pursuing through unrelated zones. 6. Use landmarks, lighting, contrast, and composition to guide without hiding hazards or making the route ambiguous.
## Motivate every local light
Attach every local torch, lantern, brazier, or similar light spatially to a visible emitter. Its position, range, color, intensity, shadowing, and occlusion behavior must match what that emitter appears able to produce.
- Forbid unexplained floating local lights. - Keep a source-to-light inventory with emitter ID, light ID, attachment transform, type, range, color/intensity, occlusion intent, enabled state, and fallback behavior. - Move the light with a moving emitter. Disable or remove its local contribution when the emitter is disabled, hidden, destroyed, or unloaded. - Document ambient or world lighting separately. Use it for deliberate global visibility or mood, never to fake a torch or other local source.
## Validate data and geometry
- Assert that all walkable collision, navigation vertices/links, encounter anchors, gates, objectives, pickups, exits, and reset points remain on the configured gameplay plane within a small tolerance. - Reject walkable slopes, out-of-plane links, vertical shortcuts, elevated spawn points, and level data that implies height-changing traversal. - Check collision/nav agreement, route clearance, zone containment, deterministic gate/reset behavior, stable anchor references, and persistence of level progress. - Validate source-to-light inventory completeness, emitter/light attachment, range/color intent, and moving or disabled emitter state transitions.
## Prove traversal in the browser
Run deterministic route, collision, navigation, encounter, reset, and lighting fixtures, then traverse every critical and optional route in the repository-approved browser.
- On desktop and mobile, verify uninterrupted flat movement, dodge clearance, camera framing, threat/pickup/exit visibility, encounter telegraphs, gates, retry paths, and touch controls. - Exercise moving and disabled emitter cases and confirm no light remains detached or unexplained. - Inspect dense views for occlusion, console health, frame time, draw calls, memory stability, and long-session lighting cost. - Report new failures separately from existing baseline issues.
Source provenance
Decision snapshot
5,689 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for author-game-levels, ready for a manual X post.
author-game-levels: Author or revise readable, flat-world Three.js game levels. Use for movement and camera route... 5.7K stars https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x
Listing + install path for author-game-levels: https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x Install: npx skills add MengTo/Skills --skill author-game-levels
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to MengTo but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels/audit)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)MengTo
@mengto
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.5K StarsReview then install
Install targets
Codex install prompt
Install the "author-game-levels" agent skill from https://github.com/MengTo/Skills/tree/main/agent-skills/game-development/author-game-levels. 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: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. 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":"mengto-author-game-levels","task":"Install author-game-levels","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.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + Browser agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add MengTo/Skills --skill author-game-levels
Maintenance
fresh
8d since push
Risk
Safe to try
Quality score needs review
GitHub quality
5.7K
84/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
5.7K GitHub stars
Repo activity
5.7K stars, 685 forks
Maintenance
8d since push
License
MIT
Install
npx skills add MengTo/Skills --skill author-game-levels
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add MengTo/Skills --skill author-game-levelsDo not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mengto-author-game-levels/install
Agent should check
Copy prompt
Task: Use author-game-levels in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mengto-author-game-levels/install
Install command: npx skills add MengTo/Skills --skill author-game-levels
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mengto-author-game-levels/install
LLM text format
/api/skills/mengto-author-game-levels/install?format=text
Find alternatives
/api/skills/search?q=author-game-levels&limit=3
Agent prompt
Use author-game-levels for this task. Review https://www.openagentskill.com/api/skills/mengto-author-game-levels/install, then install with: npx skills add MengTo/Skills --skill author-game-levelsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mengto-author-game-levels
LLM text
/api/registry/manifest/mengto-author-game-levels?format=text
Install alias
/api/registry/install/mengto-author-game-levels
Recommend
/api/registry/recommend?task=Use%20author-game-levels%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
Use-case tags
Platforms
Claude Code, Browser agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Browser automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS5.7K GitHub stars
Stars/forks activity
PASS5.7K stars, 685 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
--- name: author-game-levels description: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. ---
# Author Game Levels
Treat architecture as gameplay communication. Every route, arena, gate, prop, and light must help the player read movement, threats, objectives, or state.
## Enforce one gameplay plane
Keep all collision, navigation, encounter routes, objectives, pickups, and player movement on one accessible plane.
- Do not add stairs, ramps, raised platforms, drop-offs, cliffs, bridges, ledges, pits, or vertical traversal. - Do not change gameplay elevation for shortcuts, arenas, hazards, rewards, or visual variety. - If visual height is requested later, keep it non-walkable background dressing. It must not alter navigation, camera occlusion, threat visibility, targetability, or player movement.
## Separate level systems
Maintain explicit, independently testable layers for:
- authored level data and stable zone/anchor IDs; - visual geometry and non-walkable background dressing; - simplified collision geometry; - flat navigation data and movement clearance; - encounter, enemy, gate, reset, pickup, objective, and exit zones.
Share stable IDs and transforms between layers, but never infer collision, navigation, or encounter boundaries from decoration alone.
## Lay out readable play
1. Define the architectural purpose of each space: traversal, orientation, combat, recovery, reward, transition, or objective. 2. Preserve clear movement, camera, and dodge corridors at the intended play distance. 3. Keep threats, pickups, exits, gates, and interaction targets visible before commitment. 4. Telegraph encounters through visible arena shape, approach, state change, and stable zone anchors. 5. Deliberately place arenas, gates, checkpoints, retry spawns, and reset paths. Prevent soft locks, duplicate rewards, hidden re-entry, and enemies pursuing through unrelated zones. 6. Use landmarks, lighting, contrast, and composition to guide without hiding hazards or making the route ambiguous.
## Motivate every local light
Attach every local torch, lantern, brazier, or similar light spatially to a visible emitter. Its position, range, color, intensity, shadowing, and occlusion behavior must match what that emitter appears able to produce.
- Forbid unexplained floating local lights. - Keep a source-to-light inventory with emitter ID, light ID, attachment transform, type, range, color/intensity, occlusion intent, enabled state, and fallback behavior. - Move the light with a moving emitter. Disable or remove its local contribution when the emitter is disabled, hidden, destroyed, or unloaded. - Document ambient or world lighting separately. Use it for deliberate global visibility or mood, never to fake a torch or other local source.
## Validate data and geometry
- Assert that all walkable collision, navigation vertices/links, encounter anchors, gates, objectives, pickups, exits, and reset points remain on the configured gameplay plane within a small tolerance. - Reject walkable slopes, out-of-plane links, vertical shortcuts, elevated spawn points, and level data that implies height-changing traversal. - Check collision/nav agreement, route clearance, zone containment, deterministic gate/reset behavior, stable anchor references, and persistence of level progress. - Validate source-to-light inventory completeness, emitter/light attachment, range/color intent, and moving or disabled emitter state transitions.
## Prove traversal in the browser
Run deterministic route, collision, navigation, encounter, reset, and lighting fixtures, then traverse every critical and optional route in the repository-approved browser.
- On desktop and mobile, verify uninterrupted flat movement, dodge clearance, camera framing, threat/pickup/exit visibility, encounter telegraphs, gates, retry paths, and touch controls. - Exercise moving and disabled emitter cases and confirm no light remains detached or unexplained. - Inspect dense views for occlusion, console health, frame time, draw calls, memory stability, and long-session lighting cost. - Report new failures separately from existing baseline issues.
Source provenance
Decision snapshot
5,689 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for author-game-levels, ready for a manual X post.
author-game-levels: Author or revise readable, flat-world Three.js game levels. Use for movement and camera route... 5.7K stars https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x
Listing + install path for author-game-levels: https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x Install: npx skills add MengTo/Skills --skill author-game-levels
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to MengTo but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels/audit)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)MengTo
@mengto
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.5K StarsReview then install
Install targets
Codex install prompt
Install the "author-game-levels" agent skill from https://github.com/MengTo/Skills/tree/main/agent-skills/game-development/author-game-levels. 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: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. 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":"mengto-author-game-levels","task":"Install author-game-levels","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.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + Browser agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add MengTo/Skills --skill author-game-levels
Maintenance
fresh
8d since push
Risk
Safe to try
Quality score needs review
GitHub quality
5.7K
84/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
5.7K GitHub stars
Repo activity
5.7K stars, 685 forks
Maintenance
8d since push
License
MIT
Install
npx skills add MengTo/Skills --skill author-game-levels
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add MengTo/Skills --skill author-game-levelsDo not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mengto-author-game-levels/install
Agent should check
Copy prompt
Task: Use author-game-levels in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mengto-author-game-levels/install
Install command: npx skills add MengTo/Skills --skill author-game-levels
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mengto-author-game-levels/install
LLM text format
/api/skills/mengto-author-game-levels/install?format=text
Find alternatives
/api/skills/search?q=author-game-levels&limit=3
Agent prompt
Use author-game-levels for this task. Review https://www.openagentskill.com/api/skills/mengto-author-game-levels/install, then install with: npx skills add MengTo/Skills --skill author-game-levelsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mengto-author-game-levels
LLM text
/api/registry/manifest/mengto-author-game-levels?format=text
Install alias
/api/registry/install/mengto-author-game-levels
Recommend
/api/registry/recommend?task=Use%20author-game-levels%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
Use-case tags
Platforms
Claude Code, Browser agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Browser automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS5.7K GitHub stars
Stars/forks activity
PASS5.7K stars, 685 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
--- name: author-game-levels description: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. ---
# Author Game Levels
Treat architecture as gameplay communication. Every route, arena, gate, prop, and light must help the player read movement, threats, objectives, or state.
## Enforce one gameplay plane
Keep all collision, navigation, encounter routes, objectives, pickups, and player movement on one accessible plane.
- Do not add stairs, ramps, raised platforms, drop-offs, cliffs, bridges, ledges, pits, or vertical traversal. - Do not change gameplay elevation for shortcuts, arenas, hazards, rewards, or visual variety. - If visual height is requested later, keep it non-walkable background dressing. It must not alter navigation, camera occlusion, threat visibility, targetability, or player movement.
## Separate level systems
Maintain explicit, independently testable layers for:
- authored level data and stable zone/anchor IDs; - visual geometry and non-walkable background dressing; - simplified collision geometry; - flat navigation data and movement clearance; - encounter, enemy, gate, reset, pickup, objective, and exit zones.
Share stable IDs and transforms between layers, but never infer collision, navigation, or encounter boundaries from decoration alone.
## Lay out readable play
1. Define the architectural purpose of each space: traversal, orientation, combat, recovery, reward, transition, or objective. 2. Preserve clear movement, camera, and dodge corridors at the intended play distance. 3. Keep threats, pickups, exits, gates, and interaction targets visible before commitment. 4. Telegraph encounters through visible arena shape, approach, state change, and stable zone anchors. 5. Deliberately place arenas, gates, checkpoints, retry spawns, and reset paths. Prevent soft locks, duplicate rewards, hidden re-entry, and enemies pursuing through unrelated zones. 6. Use landmarks, lighting, contrast, and composition to guide without hiding hazards or making the route ambiguous.
## Motivate every local light
Attach every local torch, lantern, brazier, or similar light spatially to a visible emitter. Its position, range, color, intensity, shadowing, and occlusion behavior must match what that emitter appears able to produce.
- Forbid unexplained floating local lights. - Keep a source-to-light inventory with emitter ID, light ID, attachment transform, type, range, color/intensity, occlusion intent, enabled state, and fallback behavior. - Move the light with a moving emitter. Disable or remove its local contribution when the emitter is disabled, hidden, destroyed, or unloaded. - Document ambient or world lighting separately. Use it for deliberate global visibility or mood, never to fake a torch or other local source.
## Validate data and geometry
- Assert that all walkable collision, navigation vertices/links, encounter anchors, gates, objectives, pickups, exits, and reset points remain on the configured gameplay plane within a small tolerance. - Reject walkable slopes, out-of-plane links, vertical shortcuts, elevated spawn points, and level data that implies height-changing traversal. - Check collision/nav agreement, route clearance, zone containment, deterministic gate/reset behavior, stable anchor references, and persistence of level progress. - Validate source-to-light inventory completeness, emitter/light attachment, range/color intent, and moving or disabled emitter state transitions.
## Prove traversal in the browser
Run deterministic route, collision, navigation, encounter, reset, and lighting fixtures, then traverse every critical and optional route in the repository-approved browser.
- On desktop and mobile, verify uninterrupted flat movement, dodge clearance, camera framing, threat/pickup/exit visibility, encounter telegraphs, gates, retry paths, and touch controls. - Exercise moving and disabled emitter cases and confirm no light remains detached or unexplained. - Inspect dense views for occlusion, console health, frame time, draw calls, memory stability, and long-session lighting cost. - Report new failures separately from existing baseline issues.
Source provenance
Decision snapshot
5,689 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for author-game-levels, ready for a manual X post.
author-game-levels: Author or revise readable, flat-world Three.js game levels. Use for movement and camera route... 5.7K stars https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x
Listing + install path for author-game-levels: https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x Install: npx skills add MengTo/Skills --skill author-game-levels
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to MengTo but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels/audit)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)MengTo
@mengto
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.5K StarsReview then install
Install targets
Codex install prompt
Install the "author-game-levels" agent skill from https://github.com/MengTo/Skills/tree/main/agent-skills/game-development/author-game-levels. 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: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. 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":"mengto-author-game-levels","task":"Install author-game-levels","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.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + Browser agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add MengTo/Skills --skill author-game-levels
Maintenance
fresh
8d since push
Risk
Safe to try
Quality score needs review
GitHub quality
5.7K
84/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
5.7K GitHub stars
Repo activity
5.7K stars, 685 forks
Maintenance
8d since push
License
MIT
Install
npx skills add MengTo/Skills --skill author-game-levels
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add MengTo/Skills --skill author-game-levelsDo not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mengto-author-game-levels/install
Agent should check
Copy prompt
Task: Use author-game-levels in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20author-game-levels%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mengto-author-game-levels/install
Install command: npx skills add MengTo/Skills --skill author-game-levels
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mengto-author-game-levels/install
LLM text format
/api/skills/mengto-author-game-levels/install?format=text
Find alternatives
/api/skills/search?q=author-game-levels&limit=3
Agent prompt
Use author-game-levels for this task. Review https://www.openagentskill.com/api/skills/mengto-author-game-levels/install, then install with: npx skills add MengTo/Skills --skill author-game-levelsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mengto-author-game-levels
LLM text
/api/registry/manifest/mengto-author-game-levels?format=text
Install alias
/api/registry/install/mengto-author-game-levels
Recommend
/api/registry/recommend?task=Use%20author-game-levels%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
Use-case tags
Platforms
Claude Code, Browser agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Browser automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS5.7K GitHub stars
Stars/forks activity
PASS5.7K stars, 685 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
--- name: author-game-levels description: Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification. ---
# Author Game Levels
Treat architecture as gameplay communication. Every route, arena, gate, prop, and light must help the player read movement, threats, objectives, or state.
## Enforce one gameplay plane
Keep all collision, navigation, encounter routes, objectives, pickups, and player movement on one accessible plane.
- Do not add stairs, ramps, raised platforms, drop-offs, cliffs, bridges, ledges, pits, or vertical traversal. - Do not change gameplay elevation for shortcuts, arenas, hazards, rewards, or visual variety. - If visual height is requested later, keep it non-walkable background dressing. It must not alter navigation, camera occlusion, threat visibility, targetability, or player movement.
## Separate level systems
Maintain explicit, independently testable layers for:
- authored level data and stable zone/anchor IDs; - visual geometry and non-walkable background dressing; - simplified collision geometry; - flat navigation data and movement clearance; - encounter, enemy, gate, reset, pickup, objective, and exit zones.
Share stable IDs and transforms between layers, but never infer collision, navigation, or encounter boundaries from decoration alone.
## Lay out readable play
1. Define the architectural purpose of each space: traversal, orientation, combat, recovery, reward, transition, or objective. 2. Preserve clear movement, camera, and dodge corridors at the intended play distance. 3. Keep threats, pickups, exits, gates, and interaction targets visible before commitment. 4. Telegraph encounters through visible arena shape, approach, state change, and stable zone anchors. 5. Deliberately place arenas, gates, checkpoints, retry spawns, and reset paths. Prevent soft locks, duplicate rewards, hidden re-entry, and enemies pursuing through unrelated zones. 6. Use landmarks, lighting, contrast, and composition to guide without hiding hazards or making the route ambiguous.
## Motivate every local light
Attach every local torch, lantern, brazier, or similar light spatially to a visible emitter. Its position, range, color, intensity, shadowing, and occlusion behavior must match what that emitter appears able to produce.
- Forbid unexplained floating local lights. - Keep a source-to-light inventory with emitter ID, light ID, attachment transform, type, range, color/intensity, occlusion intent, enabled state, and fallback behavior. - Move the light with a moving emitter. Disable or remove its local contribution when the emitter is disabled, hidden, destroyed, or unloaded. - Document ambient or world lighting separately. Use it for deliberate global visibility or mood, never to fake a torch or other local source.
## Validate data and geometry
- Assert that all walkable collision, navigation vertices/links, encounter anchors, gates, objectives, pickups, exits, and reset points remain on the configured gameplay plane within a small tolerance. - Reject walkable slopes, out-of-plane links, vertical shortcuts, elevated spawn points, and level data that implies height-changing traversal. - Check collision/nav agreement, route clearance, zone containment, deterministic gate/reset behavior, stable anchor references, and persistence of level progress. - Validate source-to-light inventory completeness, emitter/light attachment, range/color intent, and moving or disabled emitter state transitions.
## Prove traversal in the browser
Run deterministic route, collision, navigation, encounter, reset, and lighting fixtures, then traverse every critical and optional route in the repository-approved browser.
- On desktop and mobile, verify uninterrupted flat movement, dodge clearance, camera framing, threat/pickup/exit visibility, encounter telegraphs, gates, retry paths, and touch controls. - Exercise moving and disabled emitter cases and confirm no light remains detached or unexplained. - Inspect dense views for occlusion, console health, frame time, draw calls, memory stability, and long-session lighting cost. - Report new failures separately from existing baseline issues.
Source provenance
Decision snapshot
5,689 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for author-game-levels, ready for a manual X post.
author-game-levels: Author or revise readable, flat-world Three.js game levels. Use for movement and camera route... 5.7K stars https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x
Listing + install path for author-game-levels: https://www.openagentskill.com/skills/mengto-author-game-levels?ref=x Install: npx skills add MengTo/Skills --skill author-game-levels
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to MengTo but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mengto-author-game-levels/audit)
[](https://www.openagentskill.com/skills/mengto-author-game-levels?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)MengTo
@mengto
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.5K StarsPermission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness