adversarial-python
Use this skill to gate Python code (floors 3.10+, rules verified through 3.14) with a pass/fail adversarial review — a single blind reviewer subagent judges a…
Skill by pproenca
Chalk Labs
Need help deploying adversarial-python?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/pproenca-adversarial-python
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/adversarial-python
What's inside
- Skill name
adversarial-python- Description
- Use this skill to gate Python code (floors 3.10+, rules verified through 3.14) with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 20 decidable rules hunting two failure modes. First, code modern Python makes unnecessary — branch ladders over match/registries, hand-written init/repr/eq over dataclasses, TypeVar ritual over PEP 695, typing.Optional over PEP 604 unions, os.path over pathlib, hand-rolled stdlib batteries, deprecated utcnow, orphan create_task over TaskGroup. Second, legacy-pattern propagation — single-implementation ABCs, pass-through layers, single-method classes, concrete-inheritance reuse, boolean-forked functions, shapeless payloads and parameter clumps — judged as if greenfield; consistency with legacy code is not PASS evidence. A version probe reads the target's Python floor, marks rules above it N/A, and fetches the official what's-new delta when the floor exceeds the verified version. Verdicts only, never fixes.
- License
- MIT
- Instructions
- 65 lines in SKILL.md · view on GitHub
Files (26)
- SKILL.md
- verdict.md · assets/templates/
- gotchas.md
- metadata.json
- _sections.md · references/
- alt-collapse-passthrough-layers.md · references/
- alt-compose-over-concrete-inheritance.md · references/
- alt-function-over-single-method-class.md · references/
- alt-no-single-impl-interfaces.md · references/
- disp-match-over-isinstance-ladders.md · references/
- disp-registry-over-branch-ladders.md · references/
- disp-split-boolean-switch-params.md · references/
- disp-strenum-over-string-states.md · references/
- flow-no-silent-broad-except.md · references/
- flow-taskgroup-over-orphan-tasks.md · references/
- model-dataclass-over-boilerplate.md · references/
- model-shape-the-data-clump.md · references/
- model-typed-boundary-payloads.md · references/
- reviewer-prompt.md · references/
- std-aware-utc-datetimes.md · references/
- std-no-hand-rolled-batteries.md · references/
- std-pathlib-over-ospath.md · references/
- std-zip-strict-independent-sources.md · references/
- typing-builtin-generics-and-unions.md · references/
- typing-pep695-generics.md · references/
- typing-self-over-typevar-fluent.md · references/
SKILL.md (first lines)
# 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.
## Review Protocol
Follow these steps exactly — the gate's value is that every review runs the same way.
1. **Identify the target.** Pin down exactly what is under review (a diff, a set of files, a PR) and note the ref/paths so the review runs against an unambiguous, fixed target. Include the repo root — several `alt-` and `disp-` rules must search beyond the diff for implementers, substitution sites, test doubles, and call sites (the reviewer prompt lists them).
2. **Probe the Python version.** Read the target's Python floor from `pyproject.toml` (`requires-python`), `.python-version`, `setup.cfg`, or the CI matrix — cite the source; if undeclared, judge as the newest stable Python and say so. Then compare the floor against `verified_python` in [metadata.json](metadata.json):
- floor ≤ verified: proceed; version-gated rules above the floor will be N/A.
- floor > verified: fetch `https://docs.python.org/3/whatsnew/3.{N}.html` for each version in the gap and compose a **delta briefing** — new stdlib batteries (they extend the `std-no-hand-rolled-batteries` table), new syntax and typing forms (they extend the `disp-`/`typing-` rules' reach), each line with its citation. The briefing goes into the reviewer prompt's `{{VERSION_DELTA_BRIEFING}}` slot. After the review, record the delta in [gotchas.md](gotchas.md) so the rules can be re-verified and `verified_python` bumped.
3. **Load the rules.** Read [references/_sections.md](references/_sections.md) and every rule file in `references/` (all `disp-*.md`, `model-*.md`, `alt-*.md`, `typing-*.md`, `std-*.md`, `flow-*.md` files).
4. **Compose the reviewer prompt.** Fill [references/reviewer-prompt.md](references/reviewer-prompt.md) with the rules, the target, the Python floor, and the delta briefing (if any). The composed prompt must be fully self-contained — a reviewer sees no conversation history, so nothing may refer to context outside the prompt.
5. **Dispatch one blind reviewer.** Launch a single Task subagent whose entire input is the composed prompt — no conversation context, no commentary alongside it.
6. **Render fail-closed.** The reviewer's structured output is the verdict — there is no merge step. Overall verdict is PASS only when every rule is PASS or N/A; any single FAIL fails the gate. Never average, weigh severity, or waive a rule — a "minor" FAIL is a FAIL. If the reviewer returns "GATE NOT APPLICABLE" (no Python source in the target), stop and report that instead of a verdict.
7. **Render the verdict.** Fill [assets/templates/verdict.md](assets/templates/verdict.md). On FAIL, aggregate the reviewer's "missing for PASS" suggestions into the fix list, each with its location, ordered by category importance. Every rule whose result is FAIL must appear in the fix list with a change concrete enough to apply as written — if the reviewer's suggestion only restates the violation, derive the fix from the rule's correct example before rendering.
If the same rule flips verdicts across re-reviews of an unchanged target, or a human reads the evidence and overrides the verdict, that is a decidability bug in the rule — record it in [gotchas.md](gotchas.md) and sharpen the rule; do not override the gate.
## Verdict Format
The reviewer returns, per rule: `PASS | FAIL | N/A`, evidence (`file:line` or a quote — required for PASS as well as FAIL), and for every FAIL, the fix that flips the rule to PASS once applied — the named change plus its location, never a restatement of the violation. The final report follows [assets/templates/verdict.md](assets/templates/verdict.md).
## Rule Categories
| # | Category | Prefix | The wrong default it gates |
|---|----------|--------|----------------------------|
| 1 | Dispatch & Control Flow | `disp-` | Branch ladders where the design has a dispatch construct — event/version `if/elif` chains over registries/`match`, isinstance ladders over patterns, repeated state literals over `StrEnum`, boolean params forking whole bodies |
| 2 | Data Modeling | `model-` | Data that never got a shape — hand-written `__init__`/`__repr__`/`__eq__` over `@dataclass(slots=True)`, parameter clumps threaded through signatures, external payloads navigated by string keys past the boundary |
| 3 | Abstraction Altitude | `alt-` | Layers that add indirection without a decision — single-method classes, 1:1 pass-through wrappers, single-implementation ABCs (a consumer-side `Protocol` is the seam), concrete inheritance for reuse |
| 4 | Modern Typing | `typing-` | Pre-3.10 spellings by habit — `typing.List`/`Optional`/`Union` over builtins and `X \| None`, `TypeVar`/`Generic` ritual over PEP 695, class-name returns over `Self` |
| 5 | Stdlib Currency | `std-` | Hand-rolled batteries — `os.path` surgery over `pathlib`, re-implemented `batched`/`pairwise`/`cache`/`tomllib`, deprecated `utcnow()`, unchecked `zip` over independent sources; extended by the version-delta briefing |
| 6 | Async & Error Flow | `flow-` | Failure paths that vanish — fire-and-forget `create_task` over `TaskGroup`, broad `except` bodies that swallow without logging or recording |
## Gotchas
Read [gotchas.md](gotchas.md) before dispatching the reviewer — it pre-records scope guards (the version probe's edge cases, shapes-not-brands, the diff-vs-repo search obligations, what "greenfield judgment" does and does not license) so the reviewer does not judge outside the rules.
## Related Skills
- `adversarial-ts-patterns` — the TypeScript/React sibling gate for the same disease (over-abstraction and under-modeling); same protocol, different language.
- `radical-simplification` — the advisory sibling: cognitive moves for collapsing complexity when you want to *fix* a failed verdict, not judge it.
## Reference Files
| File | Description |
|------|-------------|
| [references/reviewer-prompt.md](references/reviewer-prompt.md) | Self-contained prompt template for each blind reviewer |
| [assets/templates/verdict.md](assets/templates/verdict.md) | Verdict report template |
| [references/_sections.md](references/_sections.md) | Category definitions and ordering |
| [metadata.json](metadata.json) | Version, `verified_python`, and source references |From pproenca/dot-skills, MIT.
More from pproenca/dot-skills
- 0.694score
tanstack-query
community
TanStack Query v5 performance optimization for data fetching, caching, mutations, and query patterns.
SkillCFreshness: C★ 218Claude Code +9 - 0.596score
mlflow-3
pproenca
MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a…
SkillBFreshness: B★ 218Claude Code +9 - 0.596score
ray
pproenca
Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on…
SkillBFreshness: B★ 218Claude Code +9 - 0.596score
mlflow-mlops-migration
pproenca
Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade…
SkillBFreshness: B★ 218Claude Code +9 - 0.596score
ray-llm
pproenca
LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch…
SkillBFreshness: B★ 218Claude Code +9 - 0.550score
expo-ui
pproenca
Library reference for @expo/ui SwiftUI components on iOS — covers Host boundaries, modifier composition, iOS 26 Liquid Glass and Human Interface Guidelines…
SkillCFreshness: C★ 218Claude Code +9 - 0.550score
debug
pproenca
Comprehensive debugging methodology for finding and fixing bugs (formerly debugging).
SkillCFreshness: C★ 218Claude Code +9 - 0.550score
bug-review
pproenca
Use this skill when a user asks to review a pull request for bugs, wants AI code review focused on correctness issues, or runs /bug-review.
SkillCFreshness: C★ 218Claude Code +9 - 0.550score
ios-taste
pproenca
Designs iOS 18+ SwiftUI experiences with real taste — starting from user goals, not pixels.
SkillCFreshness: C★ 218Claude Code +9 - 0.550score
implementation-functional-patterns
pproenca
TypeScript's functional answers to the 22 Gang of Four classes — factory functions (Factory Method, Abstract Factory, Prototype, Memento), module-scope…
SkillCFreshness: C★ 218Claude Code +9 - 0.550score
expo
pproenca
Expo React Native performance optimization guidelines.
SkillCFreshness: C★ 218Claude Code +9 - 0.550score
deterministic-metric-design
pproenca
Inventing deterministic metrics — turning a fuzzy property like 'maintainability', 'risk', or 'how reducible this code is' into a deterministic, computable…
SkillCFreshness: C★ 218Claude Code +9
Signals
- Stars
- 218
- Forks
- 19
- Open issues
- 3
- Last commit (this folder)
- Jul 19, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/pproenca/dot-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from pproenca
- 0.596score
mlflow-3
pproenca
MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a…
SkillBFreshness: B★ 218Claude Code +9 - 0.596score
ray
pproenca
Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on…
SkillBFreshness: B★ 218Claude Code +9 - 0.596score
ray-llm
pproenca
LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch…
SkillBFreshness: B★ 218Claude Code +9 - 0.596score
mlflow-mlops-migration
pproenca
Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade…
SkillBFreshness: B★ 218Claude Code +9 - 0.550score
bug-review
pproenca
Use this skill when a user asks to review a pull request for bugs, wants AI code review focused on correctness issues, or runs /bug-review.
SkillCFreshness: C★ 218Claude Code +9 - 0.550score
code-simplifier
pproenca
Code simplification skill for improving clarity, consistency, and maintainability while preserving exact behavior.
SkillCFreshness: C★ 218Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get adversarial-python running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
adversarial-python skill. Agent Mart. https://agentmart.dev/skills/pproenca-adversarial-python (last updated 2026-10-24)