/benchmark-optimization-loop
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
Claude Code
$ npx -y skills add affaan-m/ECC --skill benchmark-optimization-loop --agent claude-code
Codex
$ npx skills add affaan-m/ECC --skill benchmark-optimization-loop -a codex -y
Cursor
$ npx skills add affaan-m/ECC --skill benchmark-optimization-loop -a cursor -y
Gemini CLI
$ npx skills add affaan-m/ECC --skill benchmark-optimization-loop -a gemini-cli -y
GitHub Copilot
$ npx skills add affaan-m/ECC --skill benchmark-optimization-loop -a github-copilot -y
OpenCode
$ npx skills add affaan-m/ECC --skill benchmark-optimization-loop -a opencode -y
Windsurf
$ npx skills add affaan-m/ECC --skill benchmark-optimization-loop -a windsurf -y
SKILL.md
benchmark-optimization-loop.SKILL.mdname: benchmark-optimization-loop
description: 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 measured tests.
license: MIT
metadata:
origin: ECC
tools: Read, Write, Edit, Bash, Grep, Glob
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
1. Measure the baseline. 2. Identify bottlenecks from evidence. 3. Generate variants that test one hypothesis each. 4. Run variants with the same input shape. 5. Reject variants that fail correctness, safety, or reproducibility. 6. Promote the fastest safe variant. 7. Codify the winning path in a script, command, test, config, or doc. 8. 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.
Read more
name: benchmark-optimization-loop description: 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 measured tests. license: MIT metadata: origin: ECC tools: Read, Write, Edit, Bash, Grep, Glob
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
1. Measure the baseline. 2. Identify bottlenecks from evidence. 3. Generate variants that test one hypothesis each. 4. Run variants with the same input shape. 5. Reject variants that fail correctness, safety, or reproducibility. 6. Promote the fastest safe variant. 7. Codify the winning path in a script, command, test, config, or doc. 8. 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.
Your agent can write code, but ECC gives it a coordinated engineering system and toolbox: it plans before it builds, verifies changes with tests, reviews its own work from a fresh context, remembers what matters, and turns repeated wins into reusable skills
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Questions about benchmark-optimization-loop.
- Which coding agents can use benchmark-optimization-loop?
- benchmark-optimization-loop installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

