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 spedas/spedas_agent_kit --skill pyspedas-load-planninggit clone --depth 1 https://github.com/spedas/spedas_agent_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/spedas/spedas_agent_kit/pyspedas-load-planning)<a href="https://agentmods.dev/skills/spedas/spedas_agent_kit/pyspedas-load-planning"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/pyspedas-load-planning/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/spedas/spedas_agent_kit/pyspedas-load-planning"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/pyspedas-load-planning.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 | $0.00043 | $0.01211 |
| Opus 5 | $0.00022 | $0.00606 |
| Sonnet 5 | $0.00009 | $0.00242 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade A, and why
pyspedas-load-planning 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 9d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PySPEDAS load planning
Use this skill when a user asks for a SPEDAS/PySPEDAS data load, mission/product selection, quick-look data availability check, or a reproducible first-pass load plan. It translates PySPEDAS loader vocabulary into the SPEDAS Agent Kit workflow without expanding the default MCP tool surface.
MCP/default-surface boundary
This skill adds no MCP tool. When documenting external routes, use the structured marker external_runtime_route.not_an_mcp_tool: true. PySPEDAS loader functions such as pyspedas.themis.fgm, pyspedas.mms.fgm, pyspedas.omni.data, pyspedas.kyoto.dst, or pyspedas.noaa.noaa_load_kp are external runtime routes and are not_an_mcp_tool unless a future Agent Kit tool explicitly exposes them. For MCP-only clients, route through the compact Agent Kit surface:
spedas_overview()when uncertain.create_spedas_analysis_bundle(...)for a run directory andprovenance/run.json.search_spedas_data_sources(...)orbrowse_data_sources(...)for source discovery.plan_spedas_observation(...)before fetching.browse_data_parameters(...),load_data_source(...),fetch_data_product(...), andmanage_data_cache(...)only after the plan is bounded.
Loader contract to preserve
| PySPEDAS concept | Planning rule for agents |
|---|---|
trange |
Use a narrow, explicit UTC range. Do not let an exploratory request become a multi-day fetch unless the user asks. |
time_clip=True |
Prefer or explicitly discuss time_clip=True; PySPEDAS can load whole CDF spans around a requested time range. |
downloadonly |
Use downloadonly or Agent Kit plan/cache discovery for preflight provenance when data volume or source availability is uncertain. |
notplot |
Use notplot or an Agent Kit compact metadata route when the next step is inspection, not plotting; avoid dumping arrays into chat. |
no_update |
Use no_update / cache-only validation for reproducible tests, CI, and cold-cache caveats. |
prefix / suffix |
Require run-scoped prefixes or suffixes when loading overlapping missions/products to avoid tplot name collisions. |
varformat / varnames |
Request only the variables needed for the science question. Record the variable selection in provenance. |
level / datatype / probe / instrument |
Treat these as science choices, not defaults to guess silently. If uncertain, browse or ask; for autonomous work, choose a documented minimal product and label it. |
get_support_data |
Include support data only when the analysis requires it; otherwise keep the first-pass load compact. |
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.
- 9d ago First seen · 60 lines · 43 tokens per session scan A 41b4388cba4b
pyspedas-load-planning is a skill published in the GitHub repository spedas/spedas_agent_kit (3 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,211 once invoked, about $0.0002 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 2026-08-31.
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