pedronauck/kodebase-go · Archived
extreme-software-optimization
>- Profile-driven performance optimization with behavior proofs. Use when: optimize, slow, bottleneck, hotspot, profile, p95, latency, throughput, or algorithmic improvements.
First seen Apr 15, 2026
Installation
$
npx skills add pedronauck/kodebase-go --skill extreme-software-optimization
SKILL.md
Extreme Software Optimization
The One Rule: Profile first. Prove behavior unchanged. One change at a time.
The Loop (Mandatory)
1. BASELINE → hyperfine --warmup 3 --runs 10 'command'
2. PROFILE → cargo flamegraph / py-spy / clinic flame
3. PROVE → Golden outputs + isomorphism proof per change
4. IMPLEMENT → Score ≥ 2.0 only, one lever per commit
5. VERIFY → sha256sum -c golden_checksums.txt
6. REPEAT → Re-profile (bottlenecks shift)
Opportunity Matrix
| Hotspot |
Impact (1-5) |
Confidence (1-5) |
Effort (1-5) |
Score |
| func:line |
× |
× |
÷ |
Impact×Conf/Effort |
Rule: Only implement Score ≥ 2.0
Isomorphism Proof Template
For EVERY change, document:
## Change: [description]
- Ordering preserved: [yes/no + why]
- Tie-breaking unchanged: [yes/no + why]
- Floating-point: [identical/N/A]
- RNG seeds: [unchanged/N/A]
- Golden outputs: sha256sum -c golden_checksums.txt ✓
Pattern Tiers (Quick Reference)
Tier 1: Low-Hanging Fruit
| Pattern |
When |
Isomorphism |
| N+1 → Batch |
Sequential fetches |
Same results, fewer round-trips |
| Linear → HashMap |
Keyed lookups |
O(n)→O(1), order may change |
| Lazy eval |
Maybe-unused values |
Same final values |
| Memoization |
Repeated pure calls |
Cached = recomputed |
| Buffer reuse |
Alloc per iteration |
Zero-copy in loop |
Tier 2: Algorithmic
| Pattern |
Change |
Check |
| Binary search |
O(n)→O(log n) |
Sorted input |
| Two-pointer |
O(n²)→O(n) |
Structured input |
| Prefix sums |
O(n)→O(1) query |
Static data |
| Priority queue |
O(n)→O(log n) |
Top-k/scheduling |
Tier 3: Data Structures
| Structure |
Use Case |
| HashMap |
Point lookups |
| BTreeMap |
Range queries |
| SmallVec |
Usually-small collections |
| Arena |
Many allocations, bulk free |
| Bloom filter |
Membership pre-filter |
Full catalog: [TECHNIQUES.md](references/TECHNIQUES.md)
Language Cheatsheet
| Lang |
CPU Profile |
Trouble Spot Grep |
| Rust |
cargo flamegraph |
rg '\.clone\(\)' --type rust |
| Go |
go tool pprof /debug/pprof/profile |
rg 'interface\{\}' --type go |
| TS |
clinic flame -- node app.js |
`rg 'JSON\.(parse\ |
stringify)' --type ts` |
| Python |
py-spy record -o flame.svg -- python script.py |
rg '\.iterrows\(\)' --type py |
Full language guides: [LANGUAGE-SPECIFIC.md](references/LANGUAGE-SPECIFIC.md)
Anti-Patterns (Never Do)
| ✗ |
Why |
| Optimize without profiling |
Wastes effort on non-hotspots |
| Multiple changes per commit |
Can't isolate regressions |
| Assume improvement |
Must measure before/after |
| Change behavior "while we're here" |
Breaks isomorphism guarantee |
| Skip golden output capture |
No regression detection |
Checklist (Before Any Optimization)
Tool Commands
# Benchmark
hyperfine --warmup 3 --runs 10 'command'
# Profile
cargo flamegraph # Rust CPU
heaptrack ./binary # Allocation
strace -c ./binary # Syscalls
# Verify
sha256sum golden_outputs/* > golden_checksums.txt
sha256sum -c golden_checksums.txt # After changes
References
| Need |
Reference |
| Complete technique catalog |
[TECHNIQUES.md](references/TECHNIQUES.md) |
| Step-by-step methodology |
[METHODOLOGY.md](references/METHODOLOGY.md) |
| Language-specific guides |
[LANGUAGE-SPECIFIC.md](references/LANGUAGE-SPECIFIC.md) |
| Advanced (Round 2+) |
[ADVANCED.md](references/ADVANCED.md) |
Iteration Rounds
- Round 1: Standard (N+1, indexes, batching, memoization)
- Round 2: Algorithmic (DP, convex, semirings) → [ADVANCED.md](references/ADVANCED.md)
- Round 3: Exotic (suffix automata, link-cut trees)
Each round: fresh profile → new hotspots → new matrix.
