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 devinilabs/pro-skill --skill implement-fog-of-wargit clone --depth 1 https://github.com/devinilabs/pro-skillWrote 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/devinilabs/pro-skill/implement-fog-of-war)<a href="https://agentmods.dev/skills/devinilabs/pro-skill/implement-fog-of-war"><img src="https://agentmods.dev/badge/skills/devinilabs/pro-skill/implement-fog-of-war/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/devinilabs/pro-skill/implement-fog-of-war"><img src="https://agentmods.dev/badge/skills/devinilabs/pro-skill/implement-fog-of-war.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 | $0.00083 | $0.01278 |
| Opus 5 | $0.00042 | $0.00639 |
| Sonnet 5 | $0.00017 | $0.00256 |
| Haiku 4.5 | $0.00008 | $0.00128 |
Grade A, and why
implement-fog-of-war 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 12d 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.
This is a copy
100% identical to implement-fog-of-war — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Fog of War
Build fog of war as a shared perception system with a restrained presentation layer.
Preserve the architecture
Keep these responsibilities separate:
- Let deterministic CPU perception own ranges, obstacle intersections, and gameplay visibility.
- Encode the player's angular visible distances into one fixed-size lookup texture.
- Let one full-screen shader turn that lookup into soft radial and wall-aware fog.
- Let gameplay consume perception results directly; never infer combat truth from fog pixels.
Read mechanics.md before implementing or changing the algorithm. It records the proven obstacle model, shader composition, calibrated values, state rules, enemy behavior, lighting constraints, and telemetry.
Inspect before editing
- Locate the authoritative player position, camera, obstacles, scene lifecycle, frame loop, targeting, enemy perception, and render layers.
- Confirm the gameplay ground model. The proven corner-unprojection method assumes a locally horizontal plane; use authoritative surface or terrain reconstruction when elevation varies across the visible area.
- Find existing scene fog, post-processing, renderer creation, review modes, menus, captures, transitions, and disposal paths.
- Read the current tests and recent fog/perception commits before changing constants or ownership.
- Confirm whether obstacle state changes at runtime. Reuse the same active obstacle collection used by navigation or collision when its geometry is suitable for sight.
- Check the working tree early and preserve unrelated changes.
Implement in dependency order
1. Define perception truth
- Represent each sight blocker with a stable footprint, vertical span, and active state.
- Intersect a 3D sight segment or ray with both the horizontal footprint and vertical span.
- Ignore inactive blockers and cover below the eye-to-target sight line.
- Expose:
- point-to-point perception for enemies, targeting, and attacks;
- a deterministic angular distance fill for the fog lookup.
- Use explicit player and enemy ranges. Allow them to differ when the encounter design needs enemies to acquire beyond the player's reveal edge.
What ships with it
3 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.
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.
- 12d ago First seen · 115 lines · 83 tokens per session scan A 9b425e603f33
implement-fog-of-war is a skill published in the GitHub repository devinilabs/pro-skill (24 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,278 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to implement-fog-of-war, differing in 0 lines, and is treated as a copy.
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