Development Skill
Angular Migration
Migrate from AngularJS to Angular using hybrid mode, incremental component rewriting, and…
$15
Python Performance Optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best…
A measurement-first playbook for finding and fixing Python performance bottlenecks instead of guessing at them. It pairs real profiling tools (cProfile, line_profiler, memory_profiler, py-spy) with proven optimization patterns across CPU, memory, concurrency, and database access, so you cut latency and resource cost where it actually matters. The core discipline: profile before you optimize, fix the algorithm before the micro-detail.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category Development
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
Measure, fix the biggest thing, measure again. The skill never optimizes on instinct, this is the loop it actually runs:
- Profile before touching code: cProfile for function-level CPU time, line_profiler on the suspect functions, memory_profiler for allocation, py-spy for live production processes without restarts
- Attack algorithmic complexity first, because an O(n squared) to O(n) change beats any micro tweak: nested loops become set or dict lookups, repeated scans become single passes
- Swap data structures and patterns where the profiler points: dict membership over list search, join over string concatenation, generators over lists for large datasets so peak memory drops by orders of magnitude
- Add caching at the right layer: functools lru_cache on pure functions in-process, Redis for cross-process results, with an explicit invalidation strategy (TTL or event-driven)
- Pick the concurrency model by workload type: asyncio or threads for I/O-bound work, multiprocessing for CPU-bound work to bypass the GIL, NumPy vectorization for numeric loops
- Benchmark before and after with timeit or pytest-benchmark and record the speedup, an optimization without a measured delta does not count as done
One power source. 6 lines out.
python-performance-optimization · core
core active · 6 lines
- ✓ profile slow python code
Profile slow Python code to find the real bottleneck
- ✓ profile a live production
Profile a live production process with py-spy
- ✓ replace o(n^2) list sear…
Replace O(n^2) list searches with O(1) dict/set lookups
- ✓ pick multiprocessing vs
Pick multiprocessing vs asyncio for CPU- vs I/O-bound work
- ✓ cut peak memory with gen…
Cut peak memory with generators and __slots__
- ✓ cache expensive computat…
Cache expensive computations with lru_cache
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
-
Stop wasting effort on the wrong code: target the 5% of functions that dominate runtime
license: perpetual -
Achieve order-of-magnitude speedups by fixing complexity, not just constant factors
license: perpetual -
Lower compute cost directly by matching the right concurrency model to the workload
license: perpetual -
Drop peak memory dramatically with lazy evaluation and slotted objects
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
CPU, line, and memory profiling recipes plus py-spy flamegraphs for live systems
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
Python developers debugging slow applications or high resource costs who want data-driven optimization, not premature micro-tuning.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
-
Can I use this on a live production service, or only locally?
Both. Local work uses cProfile, line_profiler, and memory_profiler, while py-spy attaches to a running production process without restarting it and produces flamegraphs. The database recipes, batch inserts, indexing, query-plan inspection, apply wherever the queries run.
-
Why profile first instead of just applying the known optimization tricks?
Because runtime is usually dominated by a small fraction of functions, and tuning anything else is wasted effort. The discipline is measurement-first: profile, fix the algorithm (like O(n^2) scans to O(1) dict lookups) before micro-details, then verify with the benchmark decorator.
-
Will it make CPU-bound Python code faster without changing the code?
No. There is no magic flag: the gains come from changes it guides you through, choosing multiprocessing over threading for CPU-bound work given the GIL, NumPy vectorization, lru_cache, generators, and __slots__. Someone still has to apply them.
-
How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
-
One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
-
Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.