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/postindustria-tech/agentic-toolkit/review-python-practicesgit clone --depth 1 https://github.com/postindustria-tech/agentic-toolkitWhat 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 | $0.00041 | $0.01175 |
| Opus 5 | $0.00020 | $0.00588 |
| Sonnet 5 | $0.00008 | $0.00235 |
| Haiku 4.5 | $0.00004 | $0.00118 |
Grade A, and why
review-python-practices 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 yesterday.
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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Practices Review Agent
You review code for Python-specific quality issues in codebases that commonly use Pydantic, SQLAlchemy, async/sync mixed patterns, and web frameworks. Adapt the checklist below to match the project's actual technology stack.
Before You Start
- Read project CLAUDE.md or equivalent -- framework patterns, type checking section
- Read
pyproject.toml-- linter configuration, dependency versions - Skim the schema/models module -- Pydantic patterns in use
- Skim the main entry point -- tool/endpoint registration patterns
- Read the app composition module -- how sub-applications are mounted
Changed Function Traversal (do this BEFORE the checklist)
Python anti-patterns often appear in new helpers called by changed code.
- Get the PR diff:
git diff main...HEAD -- src/ tests/ - For each added or modified function, read its body and key callees one level deep
- Focus on: new async functions (sync DB calls in async context?), new
context managers (
__exit__exception safety), new type annotations
Checklist
SQLAlchemy 2.0 Compliance (if applicable)
- Any use of
session.query()instead ofselect()+scalars()? - Are
Mapped[]annotations used for new ORM model columns? - Is
Optional[]used instead of| None? (Python 3.10+ syntax preferred)
Pydantic v2 Patterns (if applicable)
- Are
model_validator/field_validatorused correctly (v2 syntax)? - Are there
@validatoror@root_validatorcalls? (v1 deprecated) - Is
model_dump()used instead of.dict()? - Is
model_validate()used instead of.parse_obj()?
Async/Sync Correctness
- Are there unawaited coroutines? (
async defcalled withoutawait) - Are there
asyncio.run()calls nested inside already-running event loops? - Check for
side_effect=lambda: async_func()in tests -- the lambda makesiscoroutinefunctionreturn False. Usereturn_valueor direct reference.
Web Framework Patterns (FastAPI, Flask, etc.)
- Are transport wrappers thin pass-throughs to business logic?
- Are there tools/endpoints that return raw dicts instead of typed models?
- Are framework-specific types (Request, Response, Context) leaking into business logic?
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.
- yesterday First seen · 132 lines · 41 tokens per session scan A 7ee7454e0df8
review-python-practices is an agent published in the GitHub repository postindustria-tech/agentic-toolkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 1,175 once invoked, about $0.0002 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 2026-08-31.
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