benchmark-optimization-loop
Has your agent improve something's speed in a measured way. It tests multiple versions and chooses the fastest one that still works correctly.
What it does
This tool helps make a program or process faster in an organized way. It tests different versions to find the most efficient one without breaking what already works.
When to use it
- ✓When you want something to run faster.
- ✓When trying many improvements at once.
- ✓To find the best way to make something work.
Example of what to ask your AI
“Try to make this code 10x faster, ensuring it still works correctly.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI first measures the current performance. Then, it creates and tests several versions with small changes, one at a time. It selects the fastest version that still passes all quality tests.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill benchmark-optimization-loopOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/pi/core/skills/benchmark-optimization-loop/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Benchmark Optimization Loop
Use this skill to convert "make it 20x faster" or "try 50 recursive optimizations" into a bounded measured loop that can actually improve a system.
Required Baseline
Do not optimize until these exist:
- the operation being optimized;
- the correctness gate that must stay green;
- the metric: wall time, p95 latency, rows/sec, cost/run, memory, error rate;
- the current baseline;
- the search budget: max variants, max time, max spend, max data impact.
If the user asks for an unrealistic target, keep the ambition but make the loop bounded and measurable.
Loop
- Measure the baseline.
- Identify bottlenecks from evidence.
- Generate variants that test one hypothesis each.
- Run variants with the same input shape.
- Reject variants that fail correctness, safety, or reproducibility.
- Promote the fastest safe variant.
- Codify the winning path in a script, command, test, config, or doc.
- Rerun the baseline and winner to confirm the delta.
Variant Table
Track variants like this:
Variant | Hypothesis | Command | Time | Correct? | Notes
baseline | current path | npm run job | 120s | yes | stable
batch-500 | fewer round trips | npm run job -- --batch 500 | 42s | yes | winner
parallel-8 | more workers | npm run job -- --workers 8 | 31s | no | rate limited
Recursive Search
For recursive or hyperparameter work:
- persist every run to a ledger;
- compare against the prior accepted winner, not only the previous run;
- keep a holdout or replay check;
- stop when improvement is within noise, correctness fails, cost exceeds the budget, or the search starts changing more variables than it can explain.
Use phrases like "best measured safe variant" instead of "global optimum" unless the search space was actually exhaustive.
Promotion Gate
A variant cannot become the new default until:
- correctness tests pass;
- the performance delta is repeated or explained;
- rollback is obvious;
- the change is encoded in source control or a durable runbook;
- the final summary includes exact commands and measurements.
FAQ
What is a 'baseline'?
It's the initial performance of your program before any optimization attempts.
What is a 'correctness gate'?
It's a test that ensures the program continues to function as expected after changes.
How does the AI know which version is best?
It compares the speed and correctness of each tested version, promoting the fastest one that still works correctly.
Details
Original description by the author
Convert 'make it faster' requests into a bounded measured optimization loop — baseline first, generate one-hypothesis variants, benchmark each against a correctness gate, and promote the fastest safe variant with reproducible commands. Use when asked to speed something up, try many variants, run recursive optimization, benchmark latency/throughput/cost, or pick the best implementation by repeated
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #16
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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