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 beita6969/ScienceClaw --skill energy-systemsgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/energy-systems)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/energy-systems"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/energy-systems/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/beita6969/scienceclaw/energy-systems"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/energy-systems.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 | $0.00053 | $0.00829 |
| Opus 5 | $0.00026 | $0.00415 |
| Sonnet 5 | $0.00011 | $0.00166 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
energy-systems 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions:
- Solar energy, photovoltaic, wind power, hydropower
- Power grid, load balancing, dispatch optimization
- Battery storage, lithium-ion, energy density, cycling
- Energy efficiency, HVAC, building energy modeling
- Techno-economic analysis, LCOE, payback period
- Electric vehicles, charging infrastructure, V2G
- Hydrogen economy, fuel cells, electrolysis
Step-by-Step Methodology
- Define the energy system scope - Specify system boundaries: single building, microgrid, regional grid, or national scale. Identify energy sources (solar, wind, fossil, nuclear, hydro), storage technologies, and demand profiles.
- Resource assessment - For solar: analyze irradiance data (GHI, DNI, DHI), calculate capacity factor, account for degradation and soiling. For wind: analyze wind speed distributions (Weibull), compute power curves, assess turbulence intensity. Use TMY (Typical Meteorological Year) data or site-specific measurements.
- System modeling - Size components: panels/turbines (capacity), inverters, batteries (energy and power), converters. Model energy balance: generation - consumption - storage - curtailment = grid exchange. Use hourly or sub-hourly time resolution.
- Grid integration - Analyze grid interconnection requirements: voltage, frequency, power factor. Assess variability and ramping impacts. Model dispatch optimization (merit order, economic dispatch, unit commitment). Evaluate ancillary services potential.
- Storage analysis - Characterize storage technology: energy density (Wh/kg), power density (W/kg), round-trip efficiency, cycle life, calendar life, self-discharge rate. Optimize sizing based on arbitrage value, peak shaving, or reliability requirements.
- Economic analysis - Calculate LCOE (levelized cost of energy) with discount rate, capital costs, O&M, fuel costs, and lifetime. Compute NPV, IRR, and payback period. Include incentives (ITC, PTC, feed-in tariffs). Conduct sensitivity analysis on key assumptions.
- Environmental assessment - Calculate avoided CO2 emissions using grid emission factors. Perform lifecycle emissions analysis (cradle-to-gate). Compare with conventional alternatives.
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 · 54 lines · 53 tokens per session scan A 119814b6c7f3
energy-systems is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 829 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 2026-09-03.
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