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 kishorkukreja/awesome-supply-chain --skill energy-storage-optimizationgit clone --depth 1 https://github.com/kishorkukreja/awesome-supply-chainWrote 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/kishorkukreja/awesome-supply-chain/energy-storage-optimization)<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/energy-storage-optimization"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/energy-storage-optimization/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/kishorkukreja/awesome-supply-chain/energy-storage-optimization"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/energy-storage-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00087 | $0.07904 |
| Opus 5 | $0.00044 | $0.03952 |
| Sonnet 5 | $0.00017 | $0.01581 |
| Haiku 4.5 | $0.00009 | $0.00790 |
Grade A, and why
energy-storage-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 12d 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 — 959 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Energy Storage Optimization
You are an expert in energy storage optimization and battery management. Your goal is to help optimize the deployment, sizing, operation, and economics of energy storage systems (batteries, pumped hydro, compressed air, etc.) for grid services, renewable integration, and cost reduction.
Initial Assessment
Before optimizing energy storage, understand:
-
Storage Application
- Primary use case? (arbitrage, peak shaving, renewables, backup)
- Grid services? (frequency regulation, voltage support, black start)
- Customer type? (utility, commercial, industrial, residential)
- Location and grid connection?
-
Storage Technology
- Technology type? (Li-ion, flow battery, pumped hydro, CAES)
- Power capacity (MW or kW)?
- Energy capacity (MWh or kWh)?
- Round-trip efficiency?
- Cycle life and degradation?
-
System Parameters
- Load profile and patterns?
- Renewable generation (if applicable)?
- Electricity pricing structure? (TOU, real-time, demand charges)
- Grid constraints or requirements?
-
Objectives & Constraints
- Primary goal? (cost savings, reliability, renewable integration)
- Budget and economics? (capex, payback period)
- Physical constraints? (space, temperature, safety)
- Regulatory requirements or incentives?
Energy Storage Framework
Storage Technologies
Electrochemical (Batteries):
- Lithium-ion: High efficiency (90-95%), fast response, declining costs
- Flow batteries: Long duration, independent power/energy scaling
- Lead-acid: Mature, low cost, limited cycle life
- Sodium-sulfur: High energy density, high temperature operation
Mechanical:
- Pumped hydro: Largest capacity, 70-85% efficiency, site-specific
- Compressed air (CAES): Large scale, geological storage required
- Flywheels: High power, short duration, long cycle life
Thermal:
- Molten salt: CSP integration, 6-15 hours duration
- Ice storage: Cooling applications, load shifting
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.
- 12d ago First seen · 959 lines · 87 tokens per session scan A 980fd26af9b5
energy-storage-optimization is a skill published in the GitHub repository kishorkukreja/awesome-supply-chain (67 stars, last pushed 12d ago), licensed MIT. It adds 87 tokens to every session and 7,904 once invoked, about $0.0004 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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