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 pproenca/dot-skills --skill implementation-design-patterns-pythongit clone --depth 1 https://github.com/pproenca/dot-skillsWrote 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/pproenca/dot-skills/implementation-design-patterns-python)<a href="https://agentmods.dev/skills/pproenca/dot-skills/implementation-design-patterns-python"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/implementation-design-patterns-python/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/pproenca/dot-skills/implementation-design-patterns-python"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/implementation-design-patterns-python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00227 | $0.02733 |
| Opus 5 | $0.00113 | $0.01367 |
| Sonnet 5 | $0.00045 | $0.00547 |
| Haiku 4.5 | $0.00023 | $0.00273 |
Grade A, and why
implementation-design-patterns-python 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 5d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Design Patterns Best Practices (Refactoring Guru)
Implementation reference for the 22 Gang of Four design patterns in idiomatic modern Python (3.10+), distilled from refactoring.guru. Each of the 22 pattern files across 3 categories captures intent, problem, solution, applicability (when to use AND when NOT to), a runnable Python example with output, implementation steps, pros/cons, and relations to sibling patterns.
This is the Pythonic-first companion to the TypeScript design-patterns skill. Most GoF patterns shrink to a language feature in Python — a function, a generator, a dataclass, functools.singledispatch, a match statement. Every entry leads with that idiomatic form and keeps the class-based GoF structure only where identity, stored state, runtime registration, or polymorphic dispatch genuinely earn it.
The patterns are a vocabulary for structural decisions, not a prescription. Reach for one only when its applicability criteria match — every entry includes a When NOT to Use section to guard against over-engineering, which is the more common failure with this catalog in Python.
When to Apply
- A constructor has grown to 10+ parameters (telescoping-constructor smell) or subclasses exist only to bake in parameter combinations
- A method branches on
kind/type/mode/statusto pick an algorithm or behavior — amatchorif/elifladder that grows with each variant - Integrating an incompatible third-party API, library, or legacy class whose method names don't match your code
- Modeling a tree-shaped domain (file systems, ASTs, UI trees, org charts) where leaves and branches must be treated uniformly
- Adding cross-cutting behavior at runtime — logging, caching, access control, compression — without subclassing
- Selecting an algorithm or behavior variant at runtime from config, user input, or environment
- Implementing undo/redo, history snapshots, transactional rollback, or queueing/scheduling of operations
- Coordinating many objects whose direct mutual references have become tangled — a hub that brokers communication
- Notifying many subscribers when something changes — event systems, reactive data flows
- Reviewing code that smells like a pattern is implicit (a giant
if isinstance(...), parallel class hierarchies, copy-pasted algorithm skeletons) — make it explicit, or collapse it to a Python idiom
What ships with it
26 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.
- AGENTS.md 10 KB
- gotchas.md 4.0 KB
- metadata.json 2.0 KB
- references/_sections.md 2.5 KB
- references/behavioral-chain-of-responsibility.md 4.9 KB
- references/behavioral-command.md 4.7 KB
- references/behavioral-iterator.md 4.3 KB
- references/behavioral-mediator.md 4.6 KB
- references/behavioral-memento.md 5.0 KB
- references/behavioral-observer.md 4.6 KB
- references/behavioral-state.md 4.6 KB
- references/behavioral-strategy.md 4.4 KB
- references/behavioral-template-method.md 5.0 KB
- references/behavioral-visitor.md 5.0 KB
- references/creational-abstract-factory.md 5.3 KB
- references/creational-builder.md 5.2 KB
- references/creational-factory-method.md 5.5 KB
- references/creational-prototype.md 5.3 KB
- references/creational-singleton.md 4.8 KB
- references/structural-adapter.md 4.8 KB
- references/structural-bridge.md 5.1 KB
- references/structural-composite.md 4.6 KB
- references/structural-decorator.md 5.2 KB
- references/structural-facade.md 4.5 KB
- references/structural-flyweight.md 4.8 KB
- references/structural-proxy.md 5.1 KB
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.
- 5d ago First seen · 95 lines · 227 tokens per session scan A 5a208b328922
implementation-design-patterns-python is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 227 tokens to every session and 2,733 once invoked, about $0.0011 per session on Opus 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 2026-09-03.
Other skills, from other repositories
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
dd-code-generation
Use pup CLI for immediate Datadog operations or generate code for integration into applications.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
holoscan-install-wheel
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.
typing-exclusion-worker
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.