agentleFS
MRCalderon3DMRCalderon3D/everything-game-dev-code/skills/engineering-common/memory-budgeting/SKILL.md

memory-budgeting

Skill105 starsChanged 8 months agoMITView on GitHub

Define residency and memory budgets so content, streaming, and runtime systems fit target platforms.

2 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

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

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    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. Distinctive:

    Keep this skill aligned with the relevant rules layer and current project documentation.

    line 58
  2. Distinctive:

    If engine-specific constraints materially change the workflow, hand off to the matching engine skill or engine-specific reviewer.

    line 59

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

Memory Budgeting

Purpose

Define residency and memory budgets so content, streaming, and runtime systems fit target platforms.

Use When

  • the project has constrained hardware targets
  • memory spikes cause instability
  • large content sets are growing

Inputs

  • target platforms
  • asset classes
  • streaming model
  • system residency assumptions

Process

  1. budget memory by asset and system class
  2. define who owns loading and unloading decisions
  3. measure peak and steady-state memory in representative flows
  4. flag accidental hard references or pinned content
  5. connect fixes to content and architecture owners

Outputs

  • memory budget sheet
  • residency rules
  • peak-memory review notes
  • ownership map

Quality Bar

  • makes ownership, state flow, and failure behavior explicit
  • improves maintainability without over-abstracting
  • supports testing, debugging, and safe iteration

Common Failure Modes

  • coupling systems through hidden globals or timing assumptions
  • writing logic that is hard to test or debug
  • optimizing the wrong layer before measuring

Related Agents

  • performance-reviewer
  • technical-artist
  • build-engineer

Related Commands

  • memory-budget
  • verify
  • release-check

Notes

  • Keep this skill aligned with the relevant rules layer and current project documentation.
  • If engine-specific constraints materially change the workflow, hand off to the matching engine skill or engine-specific reviewer.

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.