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 skills add giuseppe-trisciuoglio/developer-kit --skill aws-lambda-python-integrationgit clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kitWrote 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/skills/giuseppe-trisciuoglio/developer-kit/aws-lambda-python-integration)<a href="https://agentmods.dev/skills/giuseppe-trisciuoglio/developer-kit/aws-lambda-python-integration"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/aws-lambda-python-integration/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/skills/giuseppe-trisciuoglio/developer-kit/aws-lambda-python-integration"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/aws-lambda-python-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00094 | $0.02282 |
| Opus 5 | $0.00047 | $0.01141 |
| Sonnet 5 | $0.00019 | $0.00456 |
| Haiku 4.5 | $0.00009 | $0.00228 |
Grade A, and why
aws-lambda-python-integration 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 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.
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 — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS Lambda Python Integration
Patterns for creating high-performance AWS Lambda functions in Python with optimized cold starts and clean architecture.
Overview
AWS Lambda Python integration with two approaches: AWS Chalice (full-featured framework) and Raw Python (minimal overhead). Both support API Gateway/ALB integration with production-ready configurations.
When to Use
Use this skill when:
- Creating new Lambda functions in Python
- Migrating existing Python applications to Lambda
- Optimizing cold start performance for Python Lambda
- Choosing between framework-based and minimal Python approaches
- Configuring API Gateway or ALB integration
- Setting up deployment pipelines for Python Lambda
Instructions
1. Choose Your Approach
| Approach | Cold Start | Best For | Complexity |
|---|---|---|---|
| AWS Chalice | < 200ms | REST APIs, rapid development, built-in routing | Low |
| Raw Python | < 100ms | Simple handlers, maximum control, minimal dependencies | Low |
2. Project Structure
AWS Chalice Structure
my-chalice-app/
├── app.py # Main application with routes
├── requirements.txt # Dependencies
├── .chalice/
│ ├── config.json # Chalice configuration
│ └── deploy/ # Deployment artifacts
├── chalicelib/ # Additional modules
│ ├── __init__.py
│ └── services.py
└── tests/
└── test_app.py
Raw Python Structure
my-lambda-function/
├── lambda_function.py # Handler entry point
├── requirements.txt # Dependencies
├── template.yaml # SAM/CloudFormation template
└── src/ # Additional modules
├── __init__.py
├── handlers.py
└── utils.py
3. Implementation Examples
See the References section for detailed implementation guides. Quick examples:
AWS Chalice:
from chalice import Chalice
app = Chalice(app_name='my-api')
@app.route('/')
def index():
return {'message': 'Hello from Chalice!'}
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 344 lines · 94 tokens per session scan A a4613e4cd68f
aws-lambda-python-integration is a skill published in the GitHub repository giuseppe-trisciuoglio/developer-kit (343 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 2,282 once invoked, about $0.0005 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 2026-09-10.
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