agentmods.dev

animal-map-review

animal-map-review is a skill for Claude Code, Codex from DuongNAD/mcp-vision. It costs 53 tokens per session (422 once invoked), scanned A, original, MIT.

A review process for realistic Anima Engine maps, including terrain, ecosystems, wildlife, navigation, collisions, water, lighting, and world generation. It uses map evidence views and validation checks before recommending or applying changes.

In plain words
What is it for?
Discovering and validating map files, reviewing available map views, checking terrain and navigation, and rerunning the same checks after changes.
Why use it?
It checks whether a generated map is coherent and realistic instead of accepting unsupported assumptions. It can catch issues such as broken terrain, implausible rivers, incompatible biomes, or mismatched wildlife.

Skill for Claude CodeCodex

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

not rated 7repo 14d ago A scan 53 tokens original MIT

Good fit Discovering and validating map files, reviewing available map views, checking terrain and navigation, and rerunning the same checks after changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/duongnad/mcp-vision/animal-map-review
View source ↗ DuongNAD/mcp-vision
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add DuongNAD/mcp-vision --skill animal-map-review
Clone the repo
git clone --depth 1 https://github.com/DuongNAD/mcp-vision

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for animal-map-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/duongnad/mcp-vision/animal-map-review/github.svg)](https://agentmods.dev/skills/duongnad/mcp-vision/animal-map-review)
Your own site
<a href="https://agentmods.dev/skills/duongnad/mcp-vision/animal-map-review"><img src="https://agentmods.dev/badge/skills/duongnad/mcp-vision/animal-map-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for animal-map-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/duongnad/mcp-vision/animal-map-review"><img src="https://agentmods.dev/badge/skills/duongnad/mcp-vision/animal-map-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 422 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

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

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.00422
Opus 5.5 $0.00021 $0.00169
Sonnet 5.5 $0.00011 $0.00084
Haiku 4.5 $0.00005 $0.00042

Measured 2mo ago against content hash 3d0dd7d3cc70, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

animal-map-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

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

Nothing flagged

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

templates/skills/animal-map-review/SKILL.md · 48 lines

What it actually says

Realistic Anima map review

Use animal-map-vision as an evidence gate, not as an optional reference.

Required order

  1. Call discover_map_artifacts.
  2. Call validate_map_manifest.
  3. Call prepare_team_review.
  4. Call inspect_map_views for overview, navigation, collision, lighting, spawn, water, biome-transition, and ecosystem views that exist.
  5. Only then propose or implement changes.
  6. After changes, rerun the same manifest gate and exact camera views.

If a required tool is unavailable, stop map verification and report that MCP setup must be repaired with the repository doctor command. Never replace a missing tool result with an unsupported claim.

Realism gates

  • Terrain follows large-scale geology and has no holes, seams, floating assets, or implausible slopes.
  • Rivers begin in plausible catchments, flow downhill, join lakes/ocean, and do not terminate or climb without an explicit explanation.
  • Climate follows latitude/elevation/wind/rain-shadow inputs.
  • Incompatible biomes use ecotones instead of hard borders.
  • Species match biome, temperature, land/freshwater/saltwater/air medium, submersion, food, shelter, and predator-prey carrying capacity.
  • Rendered geometry, colliders, navmesh, simulation coordinates, and minimap agree.

Teamwork Preview

Use Gemini 3.6 Flash with these workstreams:

  • Geometry & Collision
  • Navigation & Gameplay
  • Ecology & World Coherence
  • Visual & Lighting
  • Integrator

Every finding includes severity, evidence path/region or manifest field/value, gameplay impact, confidence, proposed fix, and a reproducible before/after check.

Changes

What this file has done since we first saw it

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

  1. 2mo ago First seen · 48 lines · 53 tokens per session scan A 3d0dd7d3cc70

Subscribe to this mod's changes

animal-map-review is a skill published in the GitHub repository DuongNAD/mcp-vision (7 stars, last pushed 14d ago), licensed MIT. It adds 53 tokens to every session and 422 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Game development
Related

animal-map-review compared with skills from other repositories

animal-map-review next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
animal-map-reviewDuongNAD/mcp-visionSkill714d agoMITA53Claude Code, Codex
playtest-reportDonchitos/Claude-Code-Game-StudiosSkill36k19d agoMITA20Claude Code
godot-signals-groupsgamedev-skills/awesome-gamedev-agent-skillsSkill6.9k10d agoApache-2.0A95Claude Code
scenario-unreal-gameplayscenario-labs/skillsSkill3.2kyesterdayMITA121Claude Code
motionOpenDCAI/GameFactory-3ASkill1.0k8d agoApache-2.0A0Claude Code, Codex
unity-addressablesBesty0728/Unity-SkillsSkill1.9kyesterdayMITA25Claude Code, Codex
unity-manual-componentBesty0728/Unity-SkillsSkill1.9kyesterdayMITA36Claude Code, Codex