Automates throughput optimization research for emulator performance.
Install
mkdir -p .claude/skills/autoresearch-speed && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13349" && unzip -o skill.zip -d .claude/skills/autoresearch-speed && rm skill.zipInstalls to .claude/skills/autoresearch-speed
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Unified Super Mario Bros emulator speed-improvement workflow for this repo. Use when Codex is asked to optimize, profile, benchmark, or coordinate self-improvement research for Super Mario Bros NES throughput, including N-agent proposal and implementation waves, Modal benchmark tournaments, stale candidate replay, merge adjudication, or cleanup of speed-research worktrees.Key capabilities
- →Optimize emulator speed around env_steps_per_sec.
- →Profile code performance for throughput gains.
- →Benchmark throughput gains without regressions.
- →Iterate on speed improvements with cumulative rounds.
- →Manage mutable artifacts outside the repository.
- →Report artifacts and next baseline upon completion.
How it works
The skill runs an optimization loop for Super Mario Bros NES emulator throughput, using a fixed baseline for comparison and specific commands for profiling, screening, and accepting changes.
Inputs & outputs
When to use autoresearch-speed
- →Optimize emulator speed
- →Profile code performance
- →Benchmark throughput gains
About autoresearch-speed
Runs iterative improvement loops for emulator environment steps per second. It profiles and benchmarks changes to ensure speed gains without regressions in accuracy.
Unified Super Mario Bros emulator speed-improvement workflow for this repo. Use when Codex is asked to optimize, profile, benchmark, or coordinate self-improvement research for Super Mario Bros NES throughput, including N-agent proposal and implementation waves, Modal benchmark tournaments, stale ca
When not to use it
- →When the task is not related to Super Mario Bros NES throughput optimization.
- →When the task involves feature or accuracy regressions.
- →When the task requires creating or switching branches, forking workers, or using cloud services without explicit approval.
Limitations
- →It only counts valid env_steps_per_sec gains.
- →It requires a concrete speed mechanism and cheap falsification path for each candidate.
- →It does not permit changes to API shape, deterministic lanes, observation bytes, rewards, resets, terminations, infos, state loading, or benchmark workload.
How it compares
This skill provides a structured, iterative workflow for emulator speed optimization with strict guardrails and reporting, unlike ad-hoc performance tuning.
Compared to similar skills
autoresearch-speed side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| autoresearch-speed (this skill) | 0 | 1mo | Review | Advanced |
| python-performance-optimization | 27 | 2mo | No flags | Intermediate |
| python-repl | 6 | 4mo | Review | Beginner |
| optimizing-performance | 1 | 2mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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