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 EthanM2025/game-design-lenses --skill lens-reviewgit clone --depth 1 https://github.com/EthanM2025/game-design-lensesWrote 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/ethanm2025/game-design-lenses/lens-review)<a href="https://agentmods.dev/skills/ethanm2025/game-design-lenses/lens-review"><img src="https://agentmods.dev/badge/skills/ethanm2025/game-design-lenses/lens-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.
<a href="https://agentmods.dev/skills/ethanm2025/game-design-lenses/lens-review"><img src="https://agentmods.dev/badge/skills/ethanm2025/game-design-lenses/lens-review.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 | $0.00264 | $0.02636 |
| Opus 5 | $0.00132 | $0.01318 |
| Sonnet 5 | $0.00053 | $0.00527 |
| Haiku 4.5 | $0.00026 | $0.00264 |
Grade A, and why
lens-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 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.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lens Review Skill
Apply 100 classic game design lenses to a design artifact for multi-angle review.
What makes this skill different: it does NOT give you "the right answer". It surfaces perspectives + multiple directions + trade-offs, leaving the decision to you. Tone is hedged, not preaching.
Language detection
Always match the user's language in the response:
- If the artifact content is mostly Chinese → read
lib/100-lenses.zh.md, respond in Chinese. - If the artifact content is mostly English → read
lib/100-lenses.en.md, respond in English. - If the user's invocation message is in a different language than the artifact, prefer the invocation language for the response (user is the audience).
- If unclear, default to English.
The lens library files are equivalent in content; pick the one matching the response language so the lens names / questions match the rest of the output.
Data source
Lens library (single source of truth, shared across Claude Code and Codex):
lib/100-lenses.zh.md— Chineselib/100-lenses.en.md— English
100 lenses each, 5 static fields per lens: Category / How to use / Key questions / Elaboration / When to use. This skill consumes the library; it does not modify it.
Users can extend the library with their own lenses (custom project-specific lenses) by appending entries in the same format.
Invocation modes
| Form | Behavior |
|---|---|
/lens-review <artifact> |
Read artifact + auto-pick 3-5 most relevant lenses + full pass |
/lens-review <artifact> #N |
Apply a single specific lens (for deep dive on one angle) |
/lens-review <artifact> #N,M,K |
Apply multiple specified lenses |
/lens-review <artifact> --category <cat> |
Restrict to a category (Designer/Player/Experience/Process/Game) |
/lens-review <artifact> --count 5 |
Explicit count (default 3-5, can go 3-8) |
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 · 200 lines · 264 tokens per session scan A fee1fe881e05
lens-review is a skill published in the GitHub repository EthanM2025/game-design-lenses (5 stars, last pushed 3mo ago), licensed MIT. It adds 264 tokens to every session and 2,636 once invoked, about $0.0013 per session on Opus 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.
Other skills, from other repositories
ux-design
Guided, section-by-section UX spec authoring for a screen, flow, or HUD. Reads game concept, player journey, and relevant GDDs to provide context-aware design guidance. Produces ux-spec.md (per screen/flow) or hud-design.md using the studio templates.
ux-review
Validates a UX spec, HUD design, or interaction pattern library for completeness, accessibility compliance, GDD alignment, and implementation readiness. Produces APPROVED / NEEDS REVISION / MAJOR REVISION NEEDED verdict with specific gaps.
difficulty-and-balancing
Use when designing, tuning, or reviewing the difficulty or balance of any game system — encounter tuning, cost curves, dominant-strategy audits, dynamic difficulty adjustment (DDA), difficulty settings, and accessibility assists. Also use to diagnose "this is too hard/too easy", "everything feels overpowered", a…
onboarding-and-teaching
Use when designing, reviewing, or generating the introduction to ANY mechanic in ANY genre — the four-beat teaching pattern (introduce→test→combine→twist), show-don't-tell level design, progressive disclosure and scaffolding, the mechanic dependency graph, FTUE (first-time user experience), early retention funnels…
ui-ux-and-feedback
Use when designing, auditing, or generating a HUD, menu system, or feedback vocabulary for any genre — information hierarchy, diegetic vs. non-diegetic classification, cognitive load budgeting, feedback channel design, menu flow, input modality, and accessibility. Also use to diagnose a UI that is cluttered, that…
aigd-ui-capture
AIGD · UI screenshot → UI DSL (knowledge-base capture / tool 1). Use this when you want to turn a game UI screenshot (a competitor's or your own) into structured md (type/hierarchy/geometry/interaction/state), so uirender.py can restore it to a wireframe and it can be accumulated into the patterns/UI-paradigm…