AWS Cost Optimization Analysis
Domain: computing_math — Infrastructure Engineering & Cloud Operations

Summary: Analyze a synthetic AWS billing dashboard screenshot plus usage and pricing tables to identify wasteful resources and produce a structured optimization report with projected monthly savings.

Task prompt:
You are an AWS cost optimization analyst working on Linux.

## Task Directory
`base`

## Visible Inputs
- Task brief: `base/input/task_description.txt`
- Dashboard image: `base/input/aws_billing_dashboard.png`
- Structured usage data: `base/input/resource_usage.csv`
- Pricing table: `base/input/aws_pricing_reference.csv`
- Python runtime manifest: `base/input/runtime_env/pyproject.toml`

- Python runtime lockfile: `base/input/runtime_env/uv.lock`

## Your Task
1. Read the task brief and inspect the dashboard image directly.
2. Use the CSV inputs plus the dashboard-only findings to identify wasteful spend.
3. Apply the EC2, RDS, S3, NAT Gateway, Elastic IP, and CloudWatch optimization rules described in the brief.
4. Write `optimization_report.json` with an `executive_summary` object and a `recommendations` array.
5. Write `savings_summary.csv` with columns:
   `resource_id,resource_type,name,action,current_cost,projected_cost,monthly_savings`

## Environment
- Treat `base/input` as read-only.
- Use `base/software/python_cost_optimization.sh` if you want a task-local Python environment with pandas.
- The benchmark only exposes the visible input/software/output surface while you are solving the task.
- Write final deliverables only under `base/output`.


The agent must:
Read `input/task_description.txt` and inspect the dashboard image directly.
Use the CSV inputs plus the 3 dashboard-only findings to identify wasteful EC2, RDS, S3, NAT Gateway, Elastic IP, and CloudWatch spend.
Apply the explicit pricing and optimization rules from the task brief using `input/aws_pricing_reference.csv`.
Write `output/optimization_report.json` with an `executive_summary` object and a `recommendations` array.
Write `output/savings_summary.csv` with `resource_id,resource_type,name,action,current_cost,projected_cost,monthly_savings`.

Software: Python
pandas
uv

Input files:
name: aws_billing_dashboard.png
format: PNG
description: Synthetic AWS billing dashboard screenshot containing both structured tables and 3 dashboard-only findings that are not present in the usage CSV.
name: resource_usage.csv
format: CSV
description: Structured inventory of EC2, RDS, and S3 resources with utilization, deployment, storage, and cost fields.
name: aws_pricing_reference.csv
format: CSV
description: Deterministic pricing reference used to compute downsizing, storage-tier, and monthly savings recommendations.
name: task_description.txt
format: Text
description: Agent-visible benchmark brief describing the rules, output contract, and dashboard-only requirement.
name: runtime_env
format: uv Python environment metadata
description: Pinned `pyproject.toml` and `uv.lock` files that Stage 4 provisioning uses to build the task-scoped Python runtime.
name: python_cost_optimization.sh
format: Shell wrapper
description: Agent-visible wrapper that launches the preprovisioned task-scoped Python runtime under `.runtime/python-3.10.12-pandas-2.3.3/`.