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-data-pipelinesgit 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-data-pipelines)<a href="https://agentmods.dev/skills/nimabahrami/pypsa-skills-kit/pypsa-data-pipelines"><img src="https://agentmods.dev/badge/skills/nimabahrami/pypsa-skills-kit/pypsa-data-pipelines/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-data-pipelines"><img src="https://agentmods.dev/badge/skills/nimabahrami/pypsa-skills-kit/pypsa-data-pipelines.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 | $0.00099 | $0.00756 |
| Opus 5 | $0.00049 | $0.00378 |
| Sonnet 5 | $0.00020 | $0.00151 |
| Haiku 4.5 | $0.00010 | $0.00076 |
Grade A, and why
pypsa-data-pipelines 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 11d 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyPSA Data Pipelines
Model honesty = input honesty. Maps input class -> canonical open-source pipeline + pitfalls.
Script first
- RUN
python scripts/audit_inputs.py audit network.nc= time-series FORENSICS: solar-at-night timezone bugs | leap/DST artifacts | placeholder/flat profiles | inverted seasonality | negative loads. Boundary: static parameters/structure = pypsa-physical-realism validator; SERIES feeding the model = this script. python scripts/audit_inputs.py convert annuity|ttf|api2 ...= executable unit conversions (owners: references/cost-data.md). ! PyPSA-Eur/Earth project -> these pipelines run INSIDE the workflow (retrieve rules | configured cutouts |data: costs:version pin); override via config, don't rebuild by hand (pypsa-network-modeling/references/framework-workflows.md).
Input class -> pipeline
- wind/solar capacity factors -> atlite (ERA5/SARAH cutouts) -> references/atlite-vre.md
- heat demand, COP series -> atlite heat functionality -> references/atlite-heat.md
- technology costs, annuities, fuel + CO2 price series -> references/cost-data.md
- hydro inflow -> atlite
cutout.runoffaggregated to plants -> NORMALIZE to national annual generation statistics (EIA | national TSO) — raw runoff levels are not generation. ! reservoir vs run-of-river split + calendar alignment w/ the weather year. - existing plant fleet -> powerplantmatching -> below
- load time series -> ENTSO-E | OPSD -> below
powerplantmatching (brownfield fleets)
- RUN:
import powerplantmatching as pm; df = pm.powerplants()= cross-matched EU fleet: capacity, fuel, year. - SET: explicit fuel -> carrier mapping.
- SET: build_year/lifetime for multi-period runs -> pypsa-network-modeling/references/multi-period.md.
- VERIFY: national totals vs statistics before use. ! matching gaps: small CHP, hydro.
Load data
- USE: ENTSO-E transparency (API via entsoe-py) | OPSD time series.
- ! timezone: UTC internally, convert once.
- ! DST duplicate/missing hours.
- ! leap years (2020|2024|2028): 8784 h — weather, load + weightings must agree; silent 8760 truncation drops Feb 29 stress days.
- scaling historical profiles -> scenario annual demand: profile shape | level = separate decisions -> document both.
What ships with it
4 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.
- 11d ago First seen · 45 lines · 0 tokens per session scan A 37510d21f70c
pypsa-data-pipelines is a skill published in the GitHub repository nimabahrami/pypsa-skills-kit (23 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 756 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 2026-08-30.
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