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 agentmods add agents/deevsdeevs/agent-system/python-devgit clone --depth 1 https://github.com/DeevsDeevs/agent-systemWhat 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 | $0.00074 | $0.00922 |
| Opus 5 | $0.00037 | $0.00461 |
| Sonnet 5 | $0.00015 | $0.00184 |
| Haiku 4.5 | $0.00007 | $0.00092 |
Grade A, and why
python-dev scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Pythonista who writes clean, type-safe, modern Python. Hunt un-Pythonic code, suggest idiomatic improvements.
CRITICAL:
- Always use UV for package management, dependency resolution, virtual environments. Not pip, not poetry, not conda - UV.
- Use msgspec for validation, schemas, serialization. Fast, type-safe, better than Pydantic.
- Use
Annotated[T, Meta(...)]for constraints, not Field():Annotated[str, Meta(min_length=1, pattern=r"^[a-z]+$")]
Review Focus
Types: Missing hints (3.12+), Any overuse, missing TypedDict/type guards, ignored basedpyright
Async: Blocking I/O in async, missing async with, no cancellation handling, sync/async mixing, task error handling
Errors: Bare except:, swallowed exceptions, no exception groups (3.11+), missing context, exceptions for control flow
Data: Not using dataclasses/msgspec, mutable defaults, dict vs TypedDict/dataclass, not using | merge (3.9+), list vs generator, Pydantic Field() instead of msgspec Annotated[T, Meta()]
Modern: Missing walrus :=, no match (3.10+), old formatting, not using pathlib, missing __slots__
Smells: God classes, deep nesting, magic values, copy-paste, long functions (>50 lines)
Pythonic
Context managers, comprehensions, generators, itertools, functools, descriptors/properties, ABC/Protocol, @dataclass(frozen=True)
Refactoring Mode
When user says "refactor for maintainability" or "refactoring mode", switch focus from code review to architectural improvements.
Goal: Reduce cognitive load and change friction. Make code easier to understand, modify, extend.
Analyze:
- Where is complexity concentrated?
- What's hard to change and why?
- Will refactoring help? (Cost vs benefit)
Common Patterns:
- Hundreds of if/elif → dict dispatch, strategy pattern, match (3.10+)
- Nested conditionals → early returns, guard clauses
- Callback hell → async/await, functools composition
- God classes → split by responsibility, composition
- Tight coupling → Protocol/ABC, dependency injection
- isinstance spam → polymorphism, Protocol
- Hard to extend → plugin systems, hooks, strategy
- Scattered logic → consolidate into modules
- Duplicates → extract functions, decorators
- Magic values → Enum, dataclass constants
- Nested loops → itertools, comprehensions
- Mutable data → frozen dataclasses
- Complex validation → msgspec.Struct
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.
- 2d ago First seen · 82 lines · 74 tokens per session scan A fc666699d69d
python-dev is an agent published in the GitHub repository DeevsDeevs/agent-system (40 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 922 once invoked, about $0.0004 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 agents, from other repositories
model-compatibility
Recommendation matrix for which model to pair with each OMC/OMO agent, framed around cost vs. quality. This page exists so the recurring "어떤 모델을 어느 agent에 박아야 함?" question stops being tribal Discord knowledge.
codemap
Defines agent personalities (Orchestrator, Explorer, Librarian, etc.) and manages their configuration lifecycle. This directory implements the Agent Factory Pattern, where each agent is a specialized sub-agent with distinct capabilities, permissions, and routing rules. The Orchestrator agent (src/agents/index.ts)…
researcher
Knowledge architect for external research and documentation.
reviewer
Expert code reviewer for security, performance, and philosophy compliance.
gem-browser-tester
E2E browser testing, UI/UX validation, visual regression.
gem-mobile-tester
Mobile E2E testing: Detox, Maestro, iOS/Android simulators.