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 particle-velocity-slicegit 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/particle-velocity-slice)<a href="https://agentmods.dev/skills/spedas/spedas_agent_kit/particle-velocity-slice"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/particle-velocity-slice/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/particle-velocity-slice"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/particle-velocity-slice.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 | $0.00113 | $0.02376 |
| Opus 5 | $0.00056 | $0.01188 |
| Sonnet 5 | $0.00023 | $0.00475 |
| Haiku 4.5 | $0.00011 | $0.00238 |
Grade A, and why
particle-velocity-slice 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 8d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
2D velocity-space distribution slice (spd_slice2d)
The canonical IDL/Python SPEDAS spd_slice2d plot: cut a single 2D plane through a
3D particle velocity distribution and render it as a vx–vy heatmap. This is how you
see beams, temperature anisotropy, ring/crescent distributions, and other
non-Maxwellian structure that bulk moments average away and that 1D energy/pitch-angle
spectra collapse. It is downstream of a real 3D distribution: it consumes a
distribution artifact, it does not create one.
When to use
- "Show me a 2D slice of the ion/electron distribution at in the BV plane."
- "Is there a beam / crescent / ring in this distribution? Plot the velocity-space cut."
- "Compare the distribution with and without bulk-velocity subtraction."
- Any single-time (or short-window-averaged) velocity-space inspection of a 3D dist.
Do not use this for energy-time spectrograms or pitch-angle distributions (use
compute_particle_spectra), for moments (use compute_particle_moments), or when you
do not yet have a 3D distribution — build it first (see prerequisite below).
Prerequisite (hard dependency)
This skill requires a real 3D distribution object (the pyspedas particle
distribution dict/array, per energy × theta × phi per time), not a tplot spectrogram.
Produce it first with the particle-distribution bridge skill
(build_particle_distribution_artifact, the #95 bridge): it loads the L2 particle
product (a *-DIST 3D distribution, e.g. MMS FPI dis-dist/des-dist, MMS HPCA, or an
ERG particle product) and emits a distribution artifact. Supported bridge converters
today are MMS FPI/HPCA + ERG only — THEMIS ESA and PSP SPAN-i have no Python
*_get_dist converter in pyspedas yet, so the bridge returns code="unsupported" for
them; treat them as not-yet-available until upstream pyspedas exposes the converters. If
you only have a spectrogram, stop — spd_slice2d cannot use it.
Tool chain (all existing)
particle-distribution bridge (build_particle_distribution_artifact) → small local
slice2d call → write the vx–vy grid to a single .npz → render_tplot
(one spectrogram-style panel), all inside a create_spedas_analysis_bundle.
For BV/BE/perp planes and bulk subtraction you also need aligned B and bulk
velocity time-series — fetch those with load_data_source / browse_data_parameters
/ fetch_data_product.
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.
- 8d ago First seen · 91 lines · 113 tokens per session scan A 7850c2ae66e2
particle-velocity-slice is a skill published in the GitHub repository spedas/spedas_agent_kit (3 stars, last pushed 1mo ago), licensed MIT. It adds 113 tokens to every session and 2,376 once invoked, about $0.0006 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…