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Loop Optimization: Hand vs Compiler · athola | DecimalAI

codingby athola

Loop Optimization: Hand vs Compiler

athola/loop-optimization

Source athola/claude-night-market ↗License MIT

Decides hand-vs-compiler for loop transforms (unrolling, SIMD, fusion, hoisting).

SkillScore100Maintained

Automatic

Coding pack

Last verified 8/20/2026

What it changes

Passed 22 of 22 test cases with the skill, and 21 of 22 without it.

Without skill95%

With skill100%

Checks it passed that the model alone missed

Warns against manual loop unrolling because it routinely defeats auto-vectorization

1 fixed21 passed either way0 still failing0 broke

Pass-rate lift: +5 pp
Tokens: +39%
Agent turns: 0%
Response time: -39%

Model gemini-3.6-flashMeasured 8/20/2026v222 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/athola-loop-optimization/SKILL.md https://app.decimal.ai/s/athola-loop-optimization/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add athola-loop-optimization, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/athola-loop-optimization/ and pins it in .decimal/skills.lock

decimalai skills add athola-loop-optimization

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/athola-loop-optimization/SKILL.md https://app.decimal.ai/s/athola-loop-optimization/SKILL.md

Raw file: /s/athola-loop-optimization/SKILL.md ↗ · machine-readable summary at /s/athola-loop-optimization.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

A test case the skill fixed. Same model, same prompt, with and without the skill.

Prompt

I profiled our image processing service and found a hot loop that isn't being auto-vectorized by the compiler. Before I jump straight to writing hand-crafted AVX intrinsics, can you provide a step-by-step optimization strategy explaining how to address potential compiler obstacles first, when manual SIMD or loop fusion is actually justified, and what traps could invalidate my micro-benchmark results?

What was checked, without and with the skill
Checked	Without skill	With skill
Advises fixing aliasing with restrict qualifiers or adjusting loop shape before writing intrinsics	Passed	Passed
Warns against manual loop unrolling because it routinely defeats auto-vectorization	Missed	Passed
Warns that synthetic micro-benchmarks fail to capture production data distributions and branch behavior	Passed	Passed

case-01 · gemini-3.6-flash · 8/20/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md136 linessingle file

Loop Optimization: Hand vs Compiler

A decision rule for the five common loop transformations. Its value is knowing when manual application is redundant (the compiler already does it) or harmful (it defeats the vectorizer or fools your benchmark).

When To Use

Reviewing a hot loop and a hand-rolled transform appears (unrolled body, shift-instead-of-multiply, bespoke SIMD).
Authoring a loop that profiling proved hot, deciding whether to optimize it by hand.
Pushing back on a "this is faster" claim about a loop micro-opt.

When NOT To Use

The loop is not proven hot by a profiler. Optimize nothing first.
Architecture-level performance (caching layers, sharding): use Skill(pensive:architecture-review).
Detecting complexity hotspots (O(n^2) shapes): Skill(pensive:performance-review).

The decision rule

Profile first. No loop transform without a hot loop proven by a profiler.
In compiled languages (C, C++, Rust), trust the compiler for loop-invariant code motion and strength reduction: both run automatically at -O2/-O3, so the manual form is redundant. Leave unrolling to the compiler as well. Unlike the other two it is not on by default (GCC needs -funroll-loops), but the compiler owns the profitability decision and manual unrolling routinely defeats the auto-vectorizer.
If a loop will not vectorize, fix aliasing (restrict / __restrict__) and loop shape first. Confirm with an optimization report (-fopt-info-vec-missed, -Rpass-missed=loop-vectorize). Reach for intrinsics last and accept the portability cost.
The manual transforms that still pay: explicit SIMD on loops the compiler misses, loop fusion (guard against register and cache

Track record

Benchmark

22 / 22

test cases passed, vs 21 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

athola / claude-night-market

plugins/leyline/skills/loop-optimization/SKILL.md

View on GitHub ↗

License MITImported 8/17/2026Version v2

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Loop Optimization: Hand vs Compiler

When To Use

When NOT To Use

The decision rule

Track record

Benchmark

Adoption

SkillSafety

Source

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