develop-web-game

develop-web-game is a skill for Claude Code, Codex from tercumantanumut/selene. It costs 64 tokens per session (2,021 once invoked), scanned A, a copy of develop-web-game, MIT.

A development and testing process for browser-based games made with HTML and JavaScript. It uses short automated interactions and observations to check how the game behaves.

In plain words
What is it for?
Use it while building or changing a web game, especially when checking controls, game state, rendering, timing, and browser console errors.
Why use it?
It catches problems during small changes instead of waiting until the whole game is built. The process also makes automated checks less dependent on timing.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Codex.

Good fit Use it while building or changing a web game, especially when checking controls, game state, rendering, timing, and browser console errors.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tercumantanumut/selene/develop-web-game
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 tercumantanumut/selene --skill develop-web-game
Clone the repo
git clone --depth 1 https://github.com/tercumantanumut/selene

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 develop-web-game

README.md
[![agentmods](https://agentmods.dev/badge/skills/tercumantanumut/selene/develop-web-game/github.svg)](https://agentmods.dev/skills/tercumantanumut/selene/develop-web-game)
Your own site
<a href="https://agentmods.dev/skills/tercumantanumut/selene/develop-web-game"><img src="https://agentmods.dev/badge/skills/tercumantanumut/selene/develop-web-game/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 develop-web-game

Your own site · 80×15
<a href="https://agentmods.dev/skills/tercumantanumut/selene/develop-web-game"><img src="https://agentmods.dev/badge/skills/tercumantanumut/selene/develop-web-game.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,021 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 91% copy Near-identical to another mod 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 $0.00064 $0.02021
Opus 5.5 $0.00026 $0.00808
Sonnet 5.5 $0.00013 $0.00404
Haiku 4.5 $0.00006 $0.00202

Measured 1mo ago against content hash 199863e35981, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

develop-web-game 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.

Origin

This is a copy

91% identical to develop-web-game — 11 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.

lib/skills/catalog/bundled/develop-web-game/SKILL.md · 148 lines

How it starts

The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Develop Web Game

Build games in small steps and validate every change. Treat each iteration as: implement → act → pause → observe → adjust.

Skill paths (set once)

export WEB_GAME_CLIENT="${SELENE_SKILL_ROOT}/scripts/web_game_playwright_client.js"
export WEB_GAME_ACTIONS="${SELENE_SKILL_ROOT}/references/action_payloads.json"

Workflow

  1. Pick a goal. Define a single feature or behavior to implement.
  2. Implement small. Make the smallest change that moves the game forward.
  3. Ensure integration points. Provide a single canvas and window.render_game_to_text so the test loop can read state.
  4. Add window.advanceTime(ms). Strongly prefer a deterministic step hook so the Playwright script can advance frames reliably; without it, automated tests can be flaky.
  5. Initialize progress.md. If progress.md exists, read it first and confirm the original user prompt is recorded at the top (prefix with Original prompt:). Also note any TODOs and suggestions left by the previous agent. If missing, create it and write Original prompt: <prompt> at the top before appending updates.
  6. Verify Playwright availability. Ensure playwright is available (local dependency or global install). If unsure, check npx first.
  7. Run the Playwright test script. You must run $WEB_GAME_CLIENT after each meaningful change; do not invent a new client unless required.
  8. Use the payload reference. Base actions on $WEB_GAME_ACTIONS to avoid guessing keys.
  9. Inspect state. Capture screenshots and text state after each burst.
  10. Inspect screenshots. Open the latest screenshot, verify expected visuals, fix any issues, and rerun the script. Repeat until correct.
  11. Verify controls and state (multi-step focus). Exhaustively exercise all important interactions. For each, think through the full multi-step sequence it implies (cause → intermediate states → outcome) and verify the entire chain works end-to-end. Confirm render_game_to_text reflects the same state shown on screen. If anything is off, fix and rerun. Examples of important interactions: move, jump, shoot/attack, interact/use, select/confirm/cancel in menus, pause/resume, restart, and any special abilities or puzzle actions defined by the request. Multi-step examples: shooting an enemy should reduce its health; when health reaches 0 it should disappear and update the score; collecting a key should unlock a door and allow level progression.
  12. Check errors. Review console errors and fix the first new issue before continuing.
  13. Reset between scenarios. Avoid cross-test state when validating distinct features.
  14. Iterate with small deltas. Change one variable at a time (frames, inputs, timing, positions), then repeat steps 7–13 until stable.

Read the full file on GitHub · 148 lines

Files

What ships with it

1 file 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.

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. 1mo ago First seen · 148 lines · 64 tokens per session scan A 199863e35981

Subscribe to this mod's changes

develop-web-game is a skill published in the GitHub repository tercumantanumut/selene (172 stars, last pushed 2d ago), licensed MIT. It adds 64 tokens to every session and 2,021 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to develop-web-game, differing in 11 lines, and is treated as a copy.

Related

Other skills, from other repositories

browserwing-executor

Control browser automation through HTTP API. Supports page navigation, element interaction (click, type, select), data extraction, accessibility snapshot analysis, screenshot, JavaScript execution, and batch operations.

MemTensor/MemOS · 42 tokens

dev-browser

Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website"…

MemTensor/MemOS · 84 tokens

ask-user-question

Ask users questions via the UI. Use when you need clarification, user preferences, or confirmation before proceeding. The user CANNOT see CLI output - this tool is the ONLY way to communicate with them.

MemTensor/MemOS · 44 tokens

memos-memory-guide

Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query…

MemTensor/MemOS · 131 tokens

browserwing-admin

Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.

MemTensor/MemOS · 47 tokens

computer-history

Answer questions about what the user recently did on their computer — who contacted them, what they were working on, where they left off — by reading the local Computer History summaries and raw event streams.

MemTensor/memmy-agent · 42 tokens