/benchmark-optimization-loop
Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.
$ npx -y skills add affaan-m/ECC --skill benchmark-optimization-loop --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/benchmark-optimization-loop
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.
SKILL.md
benchmark-optimization-loop.SKILL.mdname: benchmark-optimization-loop
description: Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose 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: Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose 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
Repo: affaan-m/ECC
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