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 seaworld008/Commonly-used-high-value-skills --skill develop-web-gamegit clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-skillsWrote 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/seaworld008/commonly-used-high-value-skills/develop-web-game)<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/develop-web-game"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/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.
<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/develop-web-game"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/develop-web-game.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 | $0.00029 | $0.02192 |
| Opus 5.5 | $0.00012 | $0.00877 |
| Sonnet 5.5 | $0.00006 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
develop-web-game scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 27d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to develop-web-game — 34 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Develop Web Game
Build games in small steps and validate every change. Treat each iteration as: implement → act → pause → observe → adjust.
Skill paths (set once)
export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export WEB_GAME_CLIENT="$CODEX_HOME/skills/develop-web-game/scripts/web_game_playwright_client.js"
export WEB_GAME_ACTIONS="$CODEX_HOME/skills/develop-web-game/references/action_payloads.json"
User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills).
Workflow
- Pick a goal. Define a single feature or behavior to implement.
- Implement small. Make the smallest change that moves the game forward.
- Ensure integration points. Provide a single canvas and
window.render_game_to_textso the test loop can read state. - 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. - Initialize progress.md. If
progress.mdexists, read it first and confirm the original user prompt is recorded at the top (prefix withOriginal prompt:). Also note any TODOs and suggestions left by the previous agent. If missing, create it and writeOriginal prompt: <prompt>at the top before appending updates. - Verify Playwright availability. Ensure
playwrightis available (local dependency or global install). If unsure, checknpxfirst. - Run the Playwright test script. You must run
$WEB_GAME_CLIENTafter each meaningful change; do not invent a new client unless required. - Use the payload reference. Base actions on
$WEB_GAME_ACTIONSto avoid guessing keys. - Inspect state. Capture screenshots and text state after each burst.
- Inspect screenshots. Open the latest screenshot, verify expected visuals, fix any issues, and rerun the script. Repeat until correct.
- 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_textreflects 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. - Check errors. Review console errors and fix the first new issue before continuing.
- Reset between scenarios. Avoid cross-test state when validating distinct features.
- Iterate with small deltas. Change one variable at a time (frames, inputs, timing, positions), then repeat steps 7–13 until stable.
What ships with it
7 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.
- 27d ago Changed · +6 lines 4c0590c080eb
- 1mo ago Changed · -35 tokens per session 4bdf0629d8b4
- 1mo ago First seen · 159 lines · 64 tokens per session scan A eecd17a801d2
develop-web-game is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (73 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 2,192 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to develop-web-game, differing in 34 lines, and is treated as a copy.
Other skills, from other repositories
develop-web-game
Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with rendergametotext.
input-simulation
Skill for AI agents testing a game in the Unity Editor by simulating input — clicks, drags, swipes, key presses, and touches — through the unity-open-mcp MCP server.
game-playtesting
Test a game end to end like a player across boot, controls, gameplay loops, UI, save state, failure recovery, rendering, audio, performance, and platform behavior. Use after game implementation or for regression investigation.
e2e-testing-patterns
Master end-to-end testing with Playwright and Cypress to build reliable test suites that catch bugs, improve confidence, and enable fast deployment. Use when implementing E2E tests, debugging flaky tests, or establishing testing standards.
javascript-testing-patterns
Implement comprehensive testing strategies using Jest, Vitest, and Testing Library for unit tests, integration tests, and end-to-end testing with mocking, fixtures, and test-driven development. Use when writing JavaScript/TypeScript tests, setting up test infrastructure, or implementing TDD/BDD workflows.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.