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 tplot-data-lifecyclegit 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/tplot-data-lifecycle)<a href="https://agentmods.dev/skills/spedas/spedas_agent_kit/tplot-data-lifecycle"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/tplot-data-lifecycle/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/tplot-data-lifecycle"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/tplot-data-lifecycle.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 | $0.00879 |
| Opus 5.5 | $0.00017 | $0.00352 |
| Sonnet 5.5 | $0.00009 | $0.00176 |
| Haiku 4.5 | $0.00004 | $0.00088 |
Grade A, and why
tplot-data-lifecycle 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 1mo 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.
tplot data lifecycle
Use this skill when a workflow mentions tplot variables, PyTplot, IDL SPEDAS STORE_DATA / GET_DATA, PySPEDAS store_data / get_data, plotting, export, or variable cleanup. The goal is to keep global-ish tplot state understandable and reproducible inside an Agent Kit run.
MCP/default-surface boundary
When documenting tplot runtime routes, use the structured marker external_runtime_route.not_an_mcp_tool: true. tplot_names, store_data, get_data, del_data, tplot, tplotxy, cdf_to_tplot, tplot_save, and related routines are PySPEDAS/PyTplot runtime routines, not default Agent Kit MCP tools. Treat them as not_an_mcp_tool unless a current Agent Kit tool explicitly exposes the action. MCP clients should ask Agent Kit to plan/load/export/plot through existing tools and should receive compact metadata plus artifact paths.
Lifecycle model
- Load or create variables. Use a bounded plan and run-scoped
prefix/suffixso variable names reveal mission, product, and interval. - List and inspect. Record names, shapes, time span, cadence, coordinate metadata, units, support-data status, and fill values. Do not paste raw arrays.
- Normalize metadata. Preserve or set coordinate systems and units before rotations, LMN/MVA, spectra, or particle calculations.
- Derive variables. Copy/rename before destructive operations; name derived products with method and frame, e.g.
mms1_fgm_gsm_lmn_batch1. - Plot/export. Figures, CDF/CSV/JSON summaries, and notebook snippets are artifacts. Return paths, hashes, variable lists, and compact stats.
- Cleanup or checkpoint. Delete scratch variables only after exported artifacts and
provenance/run.jsoncapture what was done.
Inspection checklist
For each important tplot variable, capture:
- Variable name and source product.
- Time range after clipping and number of samples.
- Data shape and component labels.
- Units and coordinate frame (
GSE,GSM,SM,FAC,LMN,RTN, or unknown). - Whether it is original, support data, or derived.
- Fill-value/de-spike/interpolation/smoothing status.
- Artifact path for any plot or exported table.
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.
- 1mo ago First seen · 60 lines · 43 tokens per session scan A b00a04529e53
tplot-data-lifecycle is a skill published in the GitHub repository spedas/spedas_agent_kit (3 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 879 once invoked, about $0.0002 per session on Opus 5.5. 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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