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 commands/breakds/claude.bds/python-readygit clone --depth 1 https://github.com/breakds/claude.bdsWhat 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 | $0.00000 | $0.00431 |
| Opus 5 | $0.00000 | $0.00216 |
| Sonnet 5 | $0.00000 | $0.00086 |
| Haiku 4.5 | $0.00000 | $0.00043 |
Grade A, and why
python-ready 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.
What it actually says
Prepare CLAUDE CODE with Python-Specific Context
Task
Read the project’s CLAUDE.md. Ensure all Python rules below are present. If partially present, reorganize and edit CLAUDE.md so all rules are included exactly and unambiguously.
Python Rules
Environment
- Manage the dev environment with
flake.nixonly. - Assume
nix developis active. - Do not use
pip,uv, orpoetry. - Run programs as modules:
python -m package.module.
Library Preferences
- Use builtin unittest.
- Discover all tests:
python -m unittest discover - Run verbose/specific:
python -m unittest -v path/to/test_file.py
- Discover all tests:
- Use pydantic v2 for schemas and domain models.
- Use PyTorch and JAX for ML models.
- Use loguru for logging.
- Use click for CLI/arg parsing.
- Prefer pathlib over
os.path. - Use explicit
StrEnum/IntEnumfor enums.
Code Style
- Use absolute imports; do not use relative imports (e.g., avoid
from .x import y). - Prefer specific imports (e.g.,
from pydantic import BaseModel). - Use type hints everywhere:
- Annotate all function parameters and return types.
- Use builtin generics (
list,dict,tuple) instead oftyping.List, etc. - For optionals, use
MyType | Noneinstead ofOptional[MyType].
Documentation
- Write docstrings for all public modules, functions, classes, methods, and public-facing APIs. PEP 8 and PEP 257 recommend docstrings for all public elements.
- In docstrings:
- Do not include types in the
Args:section, type hints in signatures cover that.
- Do not include types in the
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 · 37 lines · 0 tokens per session scan A 5ea798c5898e
python-ready is a command published in the GitHub repository breakds/claude.bds (2 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 431 tokens. 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.