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 nimabahrami/pypsa-skills-kit --skill pypsa-asset-economicsgit clone --depth 1 https://github.com/nimabahrami/pypsa-skills-kitWrote 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/nimabahrami/pypsa-skills-kit/pypsa-asset-economics)<a href="https://agentmods.dev/skills/nimabahrami/pypsa-skills-kit/pypsa-asset-economics"><img src="https://agentmods.dev/badge/skills/nimabahrami/pypsa-skills-kit/pypsa-asset-economics/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/nimabahrami/pypsa-skills-kit/pypsa-asset-economics"><img src="https://agentmods.dev/badge/skills/nimabahrami/pypsa-skills-kit/pypsa-asset-economics.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00136 | $0.01009 |
| Opus 5 | $0.00068 | $0.00504 |
| Sonnet 5 | $0.00027 | $0.00202 |
| Haiku 4.5 | $0.00014 | $0.00101 |
Grade A, and why
pypsa-asset-economics 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 13d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyPSA Asset Economics
- PyPSA default answers "what minimizes SYSTEM cost", not "what does THIS asset earn".
- ! correct (or disclose) three biases in every revenue number.
Bias 1 - perfect foresight
- optimizer sees all prices/weather in advance.
- perfect-foresight storage arbitrage overstates revenue ~10-30% (daily cycling) | more for multi-day strategies.
- Fixes, increasing effort:
- report perfect-foresight numbers WITH label + haircut range.
- rolling horizon dispatch (24-48h windows, limited lookahead) -> native
optimize_with_rolling_horizon+ its 3 traps: pypsa-solve-and-debug/references/performance.md item 4. - dispatch vs FORECAST price series -> settle vs outturn. Multi-market sequence (DA -> ID -> imbalance) -> references/multi-market-dispatch.md.
Bias 2 - system vs merchant optimization
- system-cost run -> asset dispatched to help SYSTEM; merchant asset maximizes own profit vs prices.
- price-TAKING asset setup:
- SOLVE: system model WITHOUT asset (or asset marginal) -> price series.
- BUILD: single-asset network = one bus + ONE bidirectional market-interface Generator (
p_min_pu=-1,marginal_cost = +price(t)) + asset -> solve. Cost linearity: buy (p>0) pays price | sell (p<0) earns price — one value, both directions. Objective = merchant profit max. !-price(t)= sign-INVERTED on an interface (paid to buy);-price(t)correct ONLY in the other encoding: on the asset's OWN output component w/ free sink (revenue-max trick).
- price-MAKING assets (large vs market) -> iterate | accept system-run dispatch as equilibrium approximation. STATE: which.
Bias 3 - wholesale price != asset price
- merchant assets pay grid fees | levies | taxes ON TOP of wholesale — jurisdiction-specific. ! storage double-charging (fees on charge AND discharge) + exemptions w/ SUNSET dates can flip a BESS business case alone. STATE: which non-market cost components included; never quote model arbitrage as investable w/o them.
Revenue accounting (post-solve, any run)
- RUN:
scripts/revenue_report.py solved_network.nc. - per-asset decomposition: energy revenue = sum_t p * lambda_bus(t) * w(t) | energy cost (links' bus0 side) | VOM | annualized capex | net margin.
- USES: n.buses_t.marginal_price -> run must produce meaningful prices (which runs do: pypsa-market-design).
- figures from this output -> pypsa-reporting chart-catalog #7 (diverging net-margin bar).
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.
- 13d ago First seen · 49 lines · 0 tokens per session scan A 4e2fb61f086f
pypsa-asset-economics is a skill published in the GitHub repository nimabahrami/pypsa-skills-kit (23 stars, last pushed 3mo ago), licensed MIT. It adds 136 tokens to every session and 1,009 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 2026-08-30.
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