Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CHENyiru3/AI-Skills-Collections --skill performancegit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chenyiru3/ai-skills-collections/performance)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/performance"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/performance"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.00859 |
| Opus 5.5 | $0.00024 | $0.00344 |
| Sonnet 5.5 | $0.00012 | $0.00172 |
| Haiku 4.5 | $0.00006 | $0.00086 |
Grade A, and why
optimizing-python-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Performance Optimization
Profiling Quick Start
# PyInstrument (statistical, readable output)
python -m pyinstrument script.py
# cProfile (detailed, built-in)
python -m cProfile -s cumulative script.py
# Memory profiling
pip install memray
memray run script.py
memray flamegraph memray-*.bin
PyInstrument Usage
from pyinstrument import Profiler
profiler = Profiler()
profiler.start()
result = my_function()
profiler.stop()
print(profiler.output_text(unicode=True, color=True))
Memory Analysis
import tracemalloc
tracemalloc.start()
# ... code ...
snapshot = tracemalloc.take_snapshot()
for stat in snapshot.statistics('lineno')[:10]:
print(stat)
Benchmarking (pytest-benchmark)
def test_encode_benchmark(benchmark):
result = benchmark(encode, 37.7749, -122.4194)
assert len(result) == 12
pytest tests/ --benchmark-only
pytest tests/ --benchmark-compare
Common Optimizations
# Use set for membership (O(1) vs O(n))
valid = set(items)
if item in valid: ...
# Use deque for queue operations
from collections import deque
queue = deque()
queue.popleft() # O(1) vs list.pop(0) O(n)
# Use generators for large data
def process(items):
for item in items:
yield transform(item)
# Cache expensive computations
from functools import lru_cache
@lru_cache(maxsize=1000)
def expensive(x):
return compute(x)
# String building
result = "".join(str(x) for x in items) # Not += in loop
Algorithm Complexity
| Operation | list | set | dict |
|---|---|---|---|
| Lookup | O(n) | O(1) | O(1) |
| Insert | O(1) | O(1) | O(1) |
| Delete | O(n) | O(1) | O(1) |
For detailed strategies, see:
- PROFILING.md - Advanced profiling techniques
- BENCHMARKS.md - CI benchmark regression testing
Optimization Checklist
Before Optimizing:
- [ ] Confirm there's a real problem
- [ ] Profile to find actual bottleneck
- [ ] Establish baseline measurements
Process:
- [ ] Algorithm improvements first
- [ ] Then data structures
- [ ] Then implementation details
- [ ] Measure after each change
After:
- [ ] Add benchmarks to prevent regression
- [ ] Verify correctness unchanged
- [ ] Document why optimization needed
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 128 lines · 60 tokens per session scan A e4d766018e91
optimizing-python-performance is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 60 tokens to every session and 859 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-02.
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