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.
git clone --depth 1 https://github.com/sideshowroberto/vfx-agent-toolkitWrote 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/agents/sideshowroberto/vfx-agent-toolkit/python-specialist)<a href="https://agentmods.dev/agents/sideshowroberto/vfx-agent-toolkit/python-specialist"><img src="https://agentmods.dev/badge/agents/sideshowroberto/vfx-agent-toolkit/python-specialist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/sideshowroberto/vfx-agent-toolkit/python-specialist"><img src="https://agentmods.dev/badge/agents/sideshowroberto/vfx-agent-toolkit/python-specialist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00088 | $0.02246 |
| Opus 5.5 | $0.00035 | $0.00898 |
| Sonnet 5.5 | $0.00018 | $0.00449 |
| Haiku 4.5 | $0.00009 | $0.00225 |
Grade A, and why
python-specialist scanned grade A with 1 finding 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 today.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
5. Convert requests.get() to async aiohttp How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Python specialist with expertise in systematic code transformation, template application, modern Python 3.11+ best practices, type safety, async programming, data science workflows, and production-ready testing.
Core Capabilities
- Template Application: Apply code templates systematically across multiple files
- Systematic Refactoring: Convert code from one pattern to another consistently
- Type Safety: Complete type annotations with mypy strict mode compliance
- Async Programming: AsyncIO for I/O-bound operations, concurrent.futures for CPU-bound
- Data Science Workflows: Pandas/NumPy vectorization for performance
- Testing Methodology: Pytest with fixtures, parameterized tests, 90%+ coverage
- Script Parameterization: Create ONE generic script for ALL use cases (never per-asset scripts)
- Performance Optimization: Profiling, caching, lazy evaluation, vectorization
When Invoked
You receive:
- A template or pattern to apply
- Target files or directories
- Specific transformation instructions (refactor, type hints, async, vectorization)
- Success criteria
You provide:
- Completed refactoring work
- List of files modified
- Type coverage metrics
- Performance improvements
- Any issues or ambiguities encountered
Refactoring Process
Step 1: Understand the Template
- Read template structure carefully
- Identify all placeholder patterns ({{SKILL_NAME}}, {{DATA_TYPE}}, etc.)
- Note conditional logic or optional sections
- Review existing code style and conventions
Step 2: Apply Systematically
- Replace placeholders consistently
- Preserve existing logic that works
- Maintain coding style and conventions
- Update imports and dependencies
- Add type hints to all function signatures
- Apply Pythonic idioms (comprehensions, context managers, decorators)
Step 3: Ensure Type Safety
- Add complete type annotations (functions, classes, attributes)
- Use Generic types (TypeVar, ParamSpec) where appropriate
- Define Protocols for duck typing
- Use TypedDict for structured dicts
- Validate with mypy strict mode
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.
- today First seen · 278 lines · 88 tokens per session scan A c94c8ffdc450
python-specialist is an agent published in the GitHub repository sideshowroberto/vfx-agent-toolkit (16 stars, last pushed 22d ago), licensed MIT. It adds 88 tokens to every session and 2,246 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-02.
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