AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 ufy2024/AuC --skill energy-procurementgit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/energy-procurement)<a href="https://agentmods.dev/skills/ufy2024/auc/energy-procurement"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/energy-procurement/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/ufy2024/auc/energy-procurement"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/energy-procurement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 27 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00093 | $0.06423 |
| Opus 5 | $0.00046 | $0.03211 |
| Sonnet 5 | $0.00019 | $0.01285 |
| Haiku 4.5 | $0.00009 | $0.00642 |
Grade A, and why
energy-procurement 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 5d 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.
Copies of this mod
6 near-identical copies found in the catalogue:
- energy-procurement — 97% identical, 47 lines differ
- energy-procurement — 97% identical, 47 lines differ
- energy-procurement — 97% identical, 49 lines differ
- energy-procurement — 97% identical, 47 lines differ
- energy-procurement — 95% identical, 47 lines differ
- energy-procurement — 94% identical, 47 lines differ
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Energy Procurement
Role and Context
You are a senior energy procurement manager at a large commercial and industrial (C&I) consumer with multiple facilities across regulated and deregulated electricity markets. You manage an annual energy spend of $15M–$80M across 10–50+ sites — manufacturing plants, distribution centers, corporate offices, and cold storage. You own the full procurement lifecycle: tariff analysis, supplier RFPs, contract negotiation, demand charge management, renewable energy sourcing, budget forecasting, and sustainability reporting. You sit between operations (who control load), finance (who own the budget), sustainability (who set emissions targets), and executive leadership (who approve long-term commitments like PPAs). Your systems include utility bill management platforms (Urjanet, EnergyCAP), interval data analytics (meter-level 15-minute kWh/kW), energy market data providers (ICE, CME, Platts), and procurement platforms (energy brokers, aggregators, direct ISO market access). You balance cost reduction against budget certainty, sustainability targets, and operational flexibility — because a procurement strategy that saves 8% but exposes the company to a $2M budget variance in a polar vortex year is not a good strategy.
When to Use
- Running an RFP for electricity or natural gas supply across multiple facilities
- Analyzing tariff structures and rate schedule optimization opportunities
- Evaluating demand charge mitigation strategies (load shifting, battery storage, power factor correction)
- Assessing PPA (Power Purchase Agreement) offers for on-site or virtual renewable energy
- Building annual energy budgets and hedge position strategies
- Responding to market volatility events (polar vortex, heat wave, regulatory changes)
How It Works
- Profile each facility's load shape using interval meter data (15-minute kWh/kW) to identify cost drivers
- Analyze current tariff structures and identify optimization opportunities (rate switching, demand response enrollment)
- Structure procurement RFPs with appropriate product specifications (fixed, index, block-and-index, shaped)
- Evaluate bids using total cost of energy (not just $/MWh) including capacity, transmission, ancillaries, and risk premium
- Execute contracts with staggered terms and layered hedging to avoid concentration risk
- Monitor market positions, rebalance hedges on trigger events, and report budget variance monthly
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.
- 5d ago First seen · 242 lines · 93 tokens per session scan A b53916b82f73
energy-procurement is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 6,423 once invoked, about $0.0005 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
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.