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 magnetopause-lmn-analysisgit 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/magnetopause-lmn-analysis)<a href="https://agentmods.dev/skills/spedas/spedas_agent_kit/magnetopause-lmn-analysis"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/magnetopause-lmn-analysis/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/magnetopause-lmn-analysis"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/magnetopause-lmn-analysis.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 | $0.00082 | $0.01227 |
| Opus 5 | $0.00041 | $0.00613 |
| Sonnet 5 | $0.00016 | $0.00245 |
| Haiku 4.5 | $0.00008 | $0.00123 |
Grade A, and why
magnetopause-lmn-analysis 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Magnetopause / bow-shock crossing — full LMN study
The canonical SPEDAS boundary-crossing analysis, end to end. Where
boundary-minimum-variance does the pure MVA step, this skill is the complete study:
it pairs the field rotation with the plasma signature (density/velocity jump) and the
spacecraft position that together identify the boundary and confirm the crossing.
When to use
- "Screen for a magnetopause / bow-shock crossing near ."
- "Characterize this boundary: normal, LMN field, plasma jump, where was the spacecraft?"
- Any single-spacecraft current-sheet / discontinuity study needing field + plasma + geometry.
Tool chain (all existing)
search_spedas_data_sources → plan_spedas_observation → create_spedas_analysis_bundle
→ load_data_source → browse_data_parameters → fetch_data_product (B and ion moments)
→ analyze_minvar_coordinates → transform_timeseries_coordinates (optional, Earth frames)
→ get_ephemeris / calculate_lshell (position) → render_tplot.
Procedure
-
Plan & bundle.
plan_spedas_observation(science_goal)to confirm source + a tight window around the crossing;create_spedas_analysis_bundle(...)fordata/+plots/+provenance/. Keep the window short — one boundary, minutes not hours (the MMS example uses a 2-minute window). -
Pick datasets — field AND plasma. A boundary is identified by both the magnetic rotation and the plasma jump:
- Magnetic field: the mission's FGM/MAG vector (confirm the variable with
browse_data_parameters). - Ion moments: density, bulk velocity, temperature (e.g. FPI-DIS for MMS, SWE/3DP for Wind, SWEAP for PSP).
Fetch each with
fetch_data_product(source_type=..., dataset_id=..., parameters=[...], start, stop, output_dir=<bundle>/data). Check the returnedstats/quality_checksbefore trusting the data.
- Magnetic field: the mission's FGM/MAG vector (confirm the variable with
-
MVA on the field → boundary normal.
analyze_minvar_coordinates(input_file=<B csv>, vector_cols=[Bx,By,Bz], output_dir=<bundle>/data). Gate on reliability: require λ_int/λ_min ≳ 5–10 before trusting the normal; report the ratio. (If the ratio is low, the window likely spans more than one structure — re-scope.)
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 · 53 lines · 82 tokens per session scan A c23c1f9230d5
magnetopause-lmn-analysis is a skill published in the GitHub repository spedas/spedas_agent_kit (3 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 1,227 once invoked, about $0.0004 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…