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.
npx skills add ourobx/agent-ebpf --skill generate2dmapgit clone --depth 1 https://github.com/ourobx/agent-ebpfWrote 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.
[](https://agentmods.dev/skills/ourobx/agent-ebpf/generate2dmap)<a href="https://agentmods.dev/skills/ourobx/agent-ebpf/generate2dmap"><img src="https://agentmods.dev/badge/skills/ourobx/agent-ebpf/generate2dmap/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.
<a href="https://agentmods.dev/skills/ourobx/agent-ebpf/generate2dmap"><img src="https://agentmods.dev/badge/skills/ourobx/agent-ebpf/generate2dmap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00116 | $0.01810 |
| Opus 5.5 | $0.00046 | $0.00724 |
| Sonnet 5.5 | $0.00023 | $0.00362 |
| Haiku 4.5 | $0.00012 | $0.00181 |
Grade A, and why
generate2dmap 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate2dmap
App-builder / Grok environment
| Item | Value |
|---|---|
| Skill dir / scripts | .grok/skills/generate2dmap/, run as python3 .grok/skills/generate2dmap/scripts/<script>.py … |
| Image tools | imagine_text_to_image / imagine_image_to_image (path-based; see imagine); inspect output with read_file on the PNG path (not Codex view_image) |
| Generated image path | imagine_text_to_image → sandbox file_path → copy into assets/map/; Pillow is preinstalled |
Default engine_target |
raw_canvas or Phaser for this TanStack browser sandbox — only use Godot/Unity/Tiled when the user explicitly wants those exports |
| Related skills | generate2dsprite (character/FX sprites; prop packs still use this skill's extract script), building-games, imagine |
Decide the pipeline first
Build the smallest playable map bundle that satisfies the game: choose a
product-level map_mode, then the lower-level axes (visual_model,
runtime_object_model, collision_model, engine_target).
tile_mode— editable tile/grid maps: Pokemon-like routes, top-down RPG towns, platformer tilemaps, or any project already on Tiled/LDtk/Godot/Unity/Phaser tilemaps.scene_mode— foundation base plus separate props: tower defense, survivors-like arenas, cozy top-down showcase maps.side_scroll_mode— parallax side-scroller stages: Mega Man-like, action platformers, Metroidvania rooms, runners, brawlers.grid_mode— rule-heavy grids: tactical RPGs, factory/automation, board/card battlers, build grids.room_chunk_mode— modular rooms/chunks: roguelike dungeons, Metroidvania networks, procedural assembly.baked_scene_mode— explicitly flat, non-playable scenes only: title/menu screens, battle backdrops, visual-novel scenes, concept art.
Use user-specified parameters when present; otherwise infer the lightest playable
pipeline from the existing game, camera, collision needs, map scale, and editing
needs. When mode and axes disagree, the mode's playable/editable contract wins.
Genre routing, per-mode axis defaults, presets, and the escalation heuristic are in
references/map-strategies.md — read it whenever the choice is not obvious.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- LICENSE 1.0 KB
- references/deliverables.md 7.2 KB
- references/layered-map-contract.md 7.9 KB
- references/map-strategies.md 26 KB
- references/object-production-gate.md 3.1 KB
- references/pipeline.md 16 KB
- references/prop-pack-contract.md 9.2 KB
- references/side-scroll-stages.md 5.5 KB
- scripts/compose_layered_preview.py 5.1 KB runs code
- scripts/extract_prop_pack.py 13 KB runs code
- SOURCE.md 303 B
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.
- 1mo ago First seen · 121 lines · 116 tokens per session scan A 992eeaa76ce3
generate2dmap is a skill published in the GitHub repository ourobx/agent-ebpf (1 stars, last pushed 1mo ago), licensed MIT. It adds 116 tokens to every session and 1,810 once invoked, about $0.0005 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 2026-09-04.
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