/accelerate — Performance Profiling

/accelerate — Performance Profiling

Profile and optimize performance systematically. No optimization without measurement.

Scale /effort to the optimization scope: low for a single hot path, high for cross-cutting work.

Scope the work with the knowledge graph when the repo is indexed: kg query "<slow area>" to locate the hot code and kg blast/kg neighbors to see what it touches — so you profile and change the right thing, in fewer tokens than grepping.

Establish a baseline measurement BEFORE making any changes. Optimization without a baseline is guesswork.

Process

Phase 1: Define the Target

Ask both questions in one message:

"Before I start profiling, two quick questions:

1. What specifically is slow? (page load, API response, query, computation — be as specific as possible)
2. What's the acceptable performance target? (e.g., 'under 200ms', 'p99 < 500ms')"

End your turn. Wait for both answers before proceeding to Phase 2. Do not run any benchmarks until you have a defined target.

Phase 2: Baseline

Measure current performance using the appropriate tool:

API:

wrk -t4 -c100 -d30s http://localhost:8080/api/endpoint
# or: hey -n 1000 -c 50 http://localhost:8080/api/endpoint

Web (browser):

npx lighthouse http://localhost:3000 --output json --output-path baseline.json

Python:

import cProfile, pstats
profiler = cProfile.Profile()
profiler.enable()
# ... run the slow code ...
profiler.disable()
stats = pstats.Stats(profiler).sort_stats('cumulative')
stats.print_stats(20)

Go:

import _ "net/http/pprof"
// Then: go tool pprof http://localhost:6060/debug/pprof/profile

Record baseline numbers.

Phase 3: Profile to Find Bottleneck

Use the profiler to identify the actual bottleneck. The bottleneck is where the most time is spent — not the most obvious place.

Phase 4: Targeted Fix

Fix ONLY the identified bottleneck. Common fixes:

• DB: add index, use JOIN instead of N+1, batch queries
• API: cache response, reduce payload size, move work async
• Frontend: code splitting, lazy loading, image optimization
• Computation: use better algorithm, vectorize, parallelize

Phase 5: Measure Again

Run the same benchmark as Phase 2. Compare: baseline vs. optimized. If target not met: return to Phase 3.

Completion Signal

"Accelerate complete. Baseline: . Optimized: . Improvement: %. Target <met/not met>."