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 skills/j4flmao/agent-skills/data-cost-optimizationnpx skills add j4flmao/agent-skills --skill data-cost-optimizationgit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote 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/j4flmao/agent-skills/data-cost-optimization)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-cost-optimization"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-cost-optimization.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 | $0.00138 | $0.04846 |
| Opus 5 | $0.00069 | $0.02423 |
| Sonnet 5 | $0.00028 | $0.00969 |
| Haiku 4.5 | $0.00014 | $0.00485 |
Grade A, and why
data-cost-optimization 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 2d 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 — 600 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Cost Optimization
Purpose
Analyze, optimize, and manage cloud data platform costs across Snowflake, BigQuery, and S3, implementing FinOps practices for cost allocation, budgeting, and continuous optimization.
Agent Protocol
Trigger
Exact user phrases: "cost optimization", "cost reduction", "Snowflake cost", "BigQuery cost", "S3 cost", "warehouse cost", "FinOps data", "query cost analysis", "storage tiering", "slot management", "credit usage", "data platform cost", "cloud data cost".
Input Context
Before activating, verify:
- Cloud data platform (Snowflake, BigQuery, Redshift, Databricks)
- Storage systems (S3, GCS, Azure Blob)
- Current monthly spend and growth trend
- Query patterns (ad-hoc, scheduled, BI)
- Data retention requirements (compliance, archival)
- Team structure for cost allocation
Output Artifact
Cost optimization plan with warehouse configuration, query tuning, storage lifecycle policies, and FinOps dashboards.
Response Format
-- Snowflake cost analysis queries
-- Warehouse configuration
# BigQuery reservation config
# S3 lifecycle policies
# Cost allocation script
# Budget monitoring
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Current spend analyzed by warehouse, query, user, and dataset
- Warehouse auto-scaling and multi-cluster configuration optimized
- Query performance tuned to reduce compute consumption
- Storage lifecycle policies implemented for tiered archival
- Cost allocation tags applied and budgets configured
- Monitoring dashboards with cost-per-team breakdown
- Optimization recommendations documented with expected savings
Max Response Length
300 lines of code and configuration.
Snowflake Cost Management
Warehouse Configuration
-- Optimal warehouse config for production workloads
CREATE WAREHOUSE prod_wh
WAREHOUSE_SIZE = 'MEDIUM'
AUTO_SUSPEND = 60 -- suspend after 1 minute idle
AUTO_RESUME = TRUE
MIN_CLUSTER_COUNT = 1
MAX_CLUSTER_COUNT = 3
SCALING_POLICY = 'ECONOMY' -- prioritize cost over performance
STATEMENT_QUEUED_TIMEOUT_IN_SECONDS = 30
STATEMENT_TIMEOUT_IN_SECONDS = 3600;
-- For ad-hoc/exploratory workloads
CREATE WAREHOUSE analytics_wh
WAREHOUSE_SIZE = 'XSMALL'
AUTO_SUSPEND = 300
AUTO_RESUME = TRUE
MIN_CLUSTER_COUNT = 1
MAX_CLUSTER_COUNT = 1;
What ships with it
6 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.
- 2d ago First seen · 600 lines · 138 tokens per session scan A 9d7d40fb9e79
data-cost-optimization is a skill published in the GitHub repository j4flmao/agent-skills (20 stars, last pushed today), licensed MIT. It adds 138 tokens to every session and 4,846 once invoked, about $0.0007 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 2026-09-03.
Other skills, from other repositories
finops-expert
Expert-level cloud financial operations, cost optimization, and cloud economics. Use when the user mentions cloud cost, optimization, cloud economics, or AWS cost, or when the task involves FinOps Fundamentals, Cost Management, FinOps Practices, or Cost Visibility.
cost-tracker
Track, estimate, and optimize LLM API costs across providers. Use when user wants to understand their LLM spending, reduce costs, or compare provider pricing for a given workload.
cost-optimization
Cloud cost analysis and optimization (FinOps).
cost-optimization
Cloud Cost Optimization & FinOps: Helps analyze and reduce cloud infrastructure costs, optimize resource usage, implement FinOps practices, and find savings opportunities. Use whenever the user mentions 'cloud costs', 'AWS bill', 'cost optimization', 'FinOps', 'right-sizing', 'reserved instances', 'savings plans'…
meter-chargeback
CocoMeter chargeback and FinOps governance. Usage: $meter chargeback refresh | $meter chargeback invoice | $meter chargeback onboarding.
meter-status
CocoMeter FinOps onboarding status. Usage: $meter status.