LobsterAI is an open-source desktop AI agent that operates files, commands, browsers, documents, spreadsheets, slides, messaging channels, and scheduled jobs in a user's working environment. It supports office work, research, and custom multi-agent workflows, while catalogue add-ons extend the agent with additional skills and workflows.
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 netease-youdao/LobsterAI --skill develop-web-gamegit clone --depth 1 https://github.com/netease-youdao/LobsterAIWrote 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/netease-youdao/lobsterai/develop-web-game)<a href="https://agentmods.dev/skills/netease-youdao/lobsterai/develop-web-game"><img src="https://agentmods.dev/badge/skills/netease-youdao/lobsterai/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/netease-youdao/lobsterai/develop-web-game"><img src="https://agentmods.dev/badge/skills/netease-youdao/lobsterai/develop-web-game.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 127 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00064 | $0.02113 |
| Opus 5 | $0.00032 | $0.01056 |
| Sonnet 5 | $0.00013 | $0.00423 |
| Haiku 4.5 | $0.00006 | $0.00211 |
Grade A, and why
develop-web-game scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- develop-web-game — 95% identical, 4 lines differ
- develop-web-game — 91% identical, 11 lines differ
- develop-web-game — 88% identical, 28 lines differ
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Develop Web Game
Build games in small steps and validate every change. Treat each iteration as: implement → act → pause → observe → adjust.
Skill paths (set once)
export SKILLS_ROOT="${LOBSTERAI_SKILLS_ROOT:-${SKILLS_ROOT:-$HOME/Library/Application Support/LobsterAI/SKILLs}}"
export WEB_GAME_CLIENT="$SKILLS_ROOT/develop-web-game/scripts/web_game_playwright_client.js"
export WEB_GAME_ACTIONS="$SKILLS_ROOT/develop-web-game/references/action_payloads.json"
Installed skills resolve from $LOBSTERAI_SKILLS_ROOT / $SKILLS_ROOT (production default: app userData/SKILLs, macOS usually ~/Library/Application Support/LobsterAI/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
6 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.
- 9d ago First seen · 151 lines · 64 tokens per session scan A 29b745be7676
develop-web-game is a skill published in the GitHub repository netease-youdao/LobsterAI (5,999 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 2,113 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
test-reporting
Run the Level 2 dummy agent integration test suite and produce a detailed HTML report with per-test input → outcome analysis.
develop-web-game
Build or iterate on HTML/JS web games with Playwright input bursts, gameplay screenshots, state inspection, and console checks.
test-runner
Write and run tests across languages and frameworks.
ha-test-strategy
Hope-native test strategy for features, fixes, and refactors: select test-first, regression-first, characterization, integration, E2E, or manual evidence according to risk and repository rules.
ha-verify
Hope-native completion and verification discipline: map each requirement to current direct evidence, choose the smallest sufficient checks, and distinguish proven, failed, blocked, stale, or unverified claims.
playwright-pro
Production-grade Playwright testing skill for E2E suites, flaky test diagnosis, browser automation, migration from Cypress/Selenium, CI integration, visual checks, and regression validation.