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 adversarial-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/adversarial-python)<a href="https://agentmods.dev/skills/pproenca/dot-skills/adversarial-python"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/adversarial-python.svg" alt="Measured on agentmods" 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 | $0.00224 | $0.02207 |
| Opus 5 | $0.00112 | $0.01104 |
| Sonnet 5 | $0.00045 | $0.00441 |
| Haiku 4.5 | $0.00022 | $0.00221 |
Grade A, and why
adversarial-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 8d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Python Gate
A modern-idiom and code-structure review gate for Python — pass/fail: a single blind reviewer subagent judges the work against this gate's rules with an adversarial mandate, and the work passes only when every rule is PASS or N/A. This skill renders verdicts; it never fixes the work.
The rules target two failure modes with one root cause — the author reproduced a shape instead of designing one. Training-data inertia produces code a modern Python feature deletes outright: the if/elif ladder that match or a registry replaces, the __init__/__repr__/__eq__ triple that @dataclass(slots=True) generates, the TypeVar ritual PEP 695 retired, the chunking helper itertools.batched shipped. Legacy-pattern propagation produces new code faithfully extending the surrounding codebase's bad structure — one more branch on the event ladder, one more method on the pass-through service — instead of tracing the feature end-to-end and modeling it. Each rule carries an Evidence of violation paragraph so a reviewer can decide PASS/FAIL/N/A from artifact evidence alone, and a Requires Python ≥ 3.X gate where the fix depends on a language version.
When to Apply
- A Python feature, endpoint, or module (agent-authored or human) is about to merge and needs an objective PASS/FAIL on whether modern Python and a fresh architectural look would delete or restructure it.
- An agent extended a legacy codebase and you suspect it copied the existing patterns — event/version branch ladders, service layers that only forward, stringly-typed state — instead of re-architecting the feature.
- A codebase raised its Python floor (to 3.12, 3.13, 3.14+) and changed code should be held to the idioms the new floor enables.
- A refactor claims to modernize or simplify and you want the claim verdict-checked rather than diff-skimmed.
Do not apply to targets with no Python source (the reviewer prompt's precondition aborts with "GATE NOT APPLICABLE"), or when the user wants explanations and refactors rather than a verdict. Judgment calls the gate deliberately excludes — naming taste, function length, docstring and test coverage, performance tuning — belong to advisory skills, not this gate.
What ships with it
25 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.
- assets/templates/verdict.md 1.7 KB
- gotchas.md 4.0 KB
- metadata.json 1.4 KB
- references/_sections.md 5.1 KB
- references/alt-collapse-passthrough-layers.md 2.2 KB
- references/alt-compose-over-concrete-inheritance.md 2.4 KB
- references/alt-function-over-single-method-class.md 2.3 KB
- references/alt-no-single-impl-interfaces.md 2.1 KB
- references/disp-match-over-isinstance-ladders.md 2.3 KB
- references/disp-registry-over-branch-ladders.md 3.0 KB
- references/disp-split-boolean-switch-params.md 2.1 KB
- references/disp-strenum-over-string-states.md 2.0 KB
- references/flow-no-silent-broad-except.md 2.4 KB
- references/flow-taskgroup-over-orphan-tasks.md 2.2 KB
- references/model-dataclass-over-boilerplate.md 2.3 KB
- references/model-shape-the-data-clump.md 2.1 KB
- references/model-typed-boundary-payloads.md 2.2 KB
- references/reviewer-prompt.md 8.0 KB
- references/std-aware-utc-datetimes.md 1.9 KB
- references/std-no-hand-rolled-batteries.md 2.3 KB
- references/std-pathlib-over-ospath.md 1.9 KB
- references/std-zip-strict-independent-sources.md 1.8 KB
- references/typing-builtin-generics-and-unions.md 1.7 KB
- references/typing-pep695-generics.md 1.9 KB
- references/typing-self-over-typevar-fluent.md 1.9 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.
- 8d ago First seen · 69 lines · 224 tokens per session scan A 4d0cc97a0feb
adversarial-python is a skill published in the GitHub repository pproenca/dot-skills (203 stars, last pushed 23d ago), licensed MIT. It adds 224 tokens to every session and 2,207 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 2026-08-30.
Other skills, from other repositories
mcore-linting-and-formatting
Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.
plankton-code-quality
Write-time code quality enforcement using Plankton — auto-formatting, linting, and Claude-powered fixes on every file edit via hooks.
coding.make_function_private
Identify private / public functions in a file and rename them with an underscore or not.
code-review-python
Provides Python-specific code review rules for the dh code-reviewer agent. Activates on pyproject.toml or .py file detection — enforces uv, ruff, ty, pytest, type annotation, error handling, and Python 3.11+ idioms including pathlib, match statements, and modern union syntax.
coding_qa.review
Review Python files for bugs, suggest fixes, and provide test cases.
coding.fix_use_helpers
Identify and replace Python code with code in the helpers package.