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 tahirraufkeeyu/software-development-agent-stack--sdas --skill cost-optimizergit clone --depth 1 https://github.com/tahirraufkeeyu/software-development-agent-stack--sdasWrote 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/tahirraufkeeyu/software-development-agent-stack--sdas/cost-optimizer)<a href="https://agentmods.dev/skills/tahirraufkeeyu/software-development-agent-stack--sdas/cost-optimizer"><img src="https://agentmods.dev/badge/skills/tahirraufkeeyu/software-development-agent-stack--sdas/cost-optimizer.svg" alt="Measured on agentmods" 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 | $0.00063 | $0.02760 |
| Opus 5 | $0.00032 | $0.01380 |
| Sonnet 5 | $0.00013 | $0.00552 |
| Haiku 4.5 | $0.00006 | $0.00276 |
Grade A, and why
cost-optimizer 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 8d ago.
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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
- Quarterly finance review of cloud spend.
- Finance flags a month-over-month jump and the owner is unknown.
- Before renewing a reserved capacity commitment, to validate the shape is right.
- After a significant architecture change, to confirm expected savings materialized.
Do not use for real-time cost alerting; that's a budget alarm, not this skill.
Inputs
cloud—aws|azure|gcp.billing_export— path or URL to the billing export:- AWS: Cost and Usage Report (CUR) in S3 (Parquet preferred).
- Azure: Cost Management export in storage (CSV/Parquet).
- GCP: BigQuery billing export dataset.
window— analysis window, typically last 30 or 90 days.account_filter— optional list of account IDs / subscription IDs / project IDs.tag_filter— optional filters (e.g.Env=prod,Owner=team-checkout).min_savings_usd— threshold below which recommendations are skipped (default $50/mo).utilization_source— where to pull CPU/memory/network: CloudWatch, Azure Monitor, GCP Cloud Monitoring, or Prometheus.
Outputs
- A Markdown report with:
- Top-line spend and trend.
- Breakdown by service, env, and owner (when tags allow).
- Ranked recommendations table: resource, action, current $/mo, projected $/mo, effort.
- Quick-win section (anything recoverable within a day).
- Structural section (anything requiring refactor/capacity planning).
- A CSV
recommendations.csvfor import into a ticketing system. - Optional: draft Jira/Linear tickets per recommendation.
Tool dependencies
- AWS:
awsCLI + Athena (or DuckDB over the CUR),aws cefor quick queries. - Azure:
azCLI + Cost Management REST API, Azure Advisor REST API. - GCP:
gcloud+ BigQuery CLI, Recommender API. duckdbfor local analysis of Parquet/CSV exports.jq,csvkitfor post-processing.
Procedure
1. Snapshot current spend
AWS (Cost Explorer + CUR):
# Top-level trend
aws ce get-cost-and-usage \
--time-period Start=2026-01-19,End=2026-04-19 \
--granularity MONTHLY \
--metrics UnblendedCost \
--group-by Type=DIMENSION,Key=SERVICE \
--output table
# CUR in Athena
athena-cli -d cur -q "
SELECT line_item_product_code, SUM(line_item_unblended_cost) AS cost
FROM cur.cur
WHERE line_item_usage_start_date >= DATE '2026-01-19'
GROUP BY 1 ORDER BY cost DESC LIMIT 20;"
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.
- 8d ago First seen · 241 lines · 63 tokens per session scan A 52bd3e6daf4a
cost-optimizer is a skill published in the GitHub repository tahirraufkeeyu/software-development-agent-stack--sdas (18 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 2,760 once invoked, about $0.0003 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 2026-08-30.
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