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 phuonghx/aim-cli --skill python-patternsgit clone --depth 1 https://github.com/phuonghx/aim-cliWrote 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/phuonghx/aim-cli/python-patterns)<a href="https://agentmods.dev/skills/phuonghx/aim-cli/python-patterns"><img src="https://agentmods.dev/badge/skills/phuonghx/aim-cli/python-patterns/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/phuonghx/aim-cli/python-patterns"><img src="https://agentmods.dev/badge/skills/phuonghx/aim-cli/python-patterns.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 | $0.00089 | $0.01896 |
| Opus 5.5 | $0.00036 | $0.00758 |
| Sonnet 5.5 | $0.00018 | $0.00379 |
| Haiku 4.5 | $0.00009 | $0.00190 |
Grade A, and why
python-patterns 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 1mo 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Design Decisions
This is about reasoning, not memorization. Two projects rarely want the same answer, so avoid defaulting to one framework or one concurrency model. Where the requirements are ambiguous, name the trade-off and ask before deciding.
Choosing a web framework
Let the kind of application steer the choice:
- API-first or microservices: FastAPI -- async-native, fast, modern.
- Full-stack app, CMS, or heavy admin: Django -- everything is included.
- Small tool, script, or teaching example: Flask -- minimal and flexible.
- Serving ML/AI models: FastAPI -- Pydantic models, async, uvicorn.
- Background processing: a queue like Celery alongside any of the above.
How they compare:
| FastAPI | Django | Flask | |
|---|---|---|---|
| Sweet spot | APIs, services | full-stack, CMS | small, learning |
| Async | native | views/ORM (partial) | via extensions |
| Admin UI | build it | built in | via extensions |
| ORM | bring your own | Django ORM | bring your own |
| Ramp-up | gentle | moderate | gentle |
Worth asking first: API-only or full-stack? Do you need an admin? Is the team comfortable with async? What infrastructure already exists?
Async or sync
Reach for async def when the work waits on the outside world and concurrency matters: database round-trips, HTTP calls, file I/O, lots of simultaneous connections, real-time features, service-to-service chatter -- and you are on an ASGI stack.
Stay with plain def when the work computes rather than waits, when the codebase or its libraries are blocking, when it is a simple script, or when the team is not yet fluent in async.
The rule of thumb:
Waiting on something external -> async
Burning CPU -> sync, and parallelize with multiprocessing
Three things to avoid: blending sync and async without care, calling blocking libraries from async code, and forcing async onto CPU-bound work where it only adds overhead.
When you do go async, pick libraries that are actually async:
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.
- 1mo ago First seen · 211 lines · 89 tokens per session scan A 4daaa37da0eb
python-patterns is a skill published in the GitHub repository phuonghx/aim-cli (1 stars, last pushed 3mo ago), licensed MIT. It adds 89 tokens to every session and 1,896 once invoked, about $0.0004 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 2026-08-31.
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