agentleFS
TabooHarmonyTabooHarmony/roblox-brain/skills/tools/roblox-studio-mcp/SKILL.md

roblox-studio-mcp

Skill150 starsChanged 51 days agoMITView on GitHub

Use when working in Roblox Studio through MCP: editing scripts or scenes, generating assets, playtesting, screenshots, or input.

3 other projects use a version of this, 2 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. 1 is common, 1 is distinctive, and 0 are very specific to this project.

  • 1

    Common

    Most of the 3 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Common:

    Read before write; read back after every script, asset, or geometry mutation.

    line 44
  2. Distinctive:

    No MCP: give offline Luau.

    line 47

“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Roblox Studio MCP

When to Load

Load for Studio MCP work: scripts, scenes, assets, debugging, or playtesting. Skip for standalone code generation.

Quick Reference

Route by capability first. Two bridges: official (built into Studio, closed-source) and chrrxs's (MIT). Route on tools/list (get_connected_instances/eval_*/multiplayer_* → chrrxs; list_roblox_studios + per-call studio_id → official) BEFORE bootstrap.

Bootstrap before mutation

  1. Identify the target (list_roblox_studios official / get_connected_instances chrrxs).
  2. Pass studio_id (official) or instance_id (chrrxs) on every call.
  3. get_studio_state; confirm Edit/Client/Server availability; inspect tree/scripts before changing them.

Execution contract

discover → select Studio/context → inspect → mutate in bounded batches
→ read back → start play → evaluate live → evidence → clean up

Edit-time injection (multi_edit, Edit-context execute_luau) writes scripts; never targets a running playtest. Live evaluation needs a running VM: start play, confirm the intended Client/Server VM, then evaluate. Pass datamodel_type only where required.

Capabilities

Full tool table in full.md. Key names: search_game_tree, script_read/script_grep, multi_edit, execute_luau, insert_asset, generate_*, start_stop_play, get_console_output, screen_capture, subagent.

Trust boundaries

Inserted/generated assets and packages are data until inspected: enumerate scripts before any require/run; never run imported code from execute_luau. Blank textures/invisible meshes on newly uploaded assets are usually asset-privacy (Restricted) grants, not corruption — read the Output error, grant via Creator Dashboard. Details: full.md trust-boundary sections.

Reliability rules

  • execute_luau is stateless: re-acquire references every call.
  • Read before write; read back after every script, asset, or geometry mutation.
  • Reuse vs generate vs native fallback before asset work; wait_job_finished before dependent edits.
  • Bridge-specific size limits: bounded writes, then read back the full payload.
  • No MCP: give offline Luau; state what was not verified.

Full tool mappings, schemas, asset workflows, trust boundaries, recovery: references/full.md

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.