spedas-workflow

spedas-workflow is a skill for Claude Code, Codex from spedas/spedas_agent_kit. It costs 26 tokens per session (1,093 once invoked), scanned A, original, MIT.

A general workflow for finding, loading, and analyzing space-science data through the SPEDAS Agent Kit. It covers data from CDAWeb, PDS, and SPICE sources, where SPICE provides spacecraft and planetary geometry data.

In plain words
What is it for?
Use it to search datasets, plan observations, compare data sources, build analysis bundles, fetch products, manage cached data, and chain measurements into spacecraft studies.
Why use it?
It gives analysis requests a consistent path from selecting an observation to retrieving data and applying science tools.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/spedas/spedas_agent_kit/spedas-workflow
Any agent
npx skills add spedas/spedas_agent_kit --skill spedas-workflow
Clone the repo
git clone --depth 1 https://github.com/spedas/spedas_agent_kit

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for spedas-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/spedas-workflow.svg)](https://agentmods.dev/skills/spedas/spedas_agent_kit/spedas-workflow)
Your own site
<a href="https://agentmods.dev/skills/spedas/spedas_agent_kit/spedas-workflow"><img src="https://agentmods.dev/badge/skills/spedas/spedas_agent_kit/spedas-workflow.svg" alt="Measured on agentmods" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,093 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00026 $0.01093
Opus 5 $0.00013 $0.00547
Sonnet 5 $0.00005 $0.00219
Haiku 4.5 $0.00003 $0.00109

Measured 5d ago against content hash d888ea4101d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

spedas-workflow 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 5d 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.

src/spedas_agent_kit/resources/skills/spedas-workflow/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SPEDAS Agent Kit workflow

The plugin exposes one MCP server named spedas. Start with spedas_overview() when uncertain.

Prefer the public SPEDAS mental model:

  1. science workflow layer;
  2. unified data layer;
  3. data source categories: cdaweb, pds, spice;
  4. internal backend packages only when maintaining/debugging the MCP.

Preferred tools

  • search_spedas_data_sources
  • plan_spedas_observation
  • compare_cdaweb_pds_spice
  • create_spedas_analysis_bundle
  • browse_data_sources(source_type="all"|"cdaweb"|"pds"|"spice")
  • load_data_source(source_type, source_id)
  • browse_data_parameters(source_type, dataset_id, ...)
  • fetch_data_product(source_type, ...)
  • manage_data_cache(source_type, ...)

Compatibility low-level tools remain available for maintenance/debugging, but new agent workflows should start with the unified data-layer tools.

MMS reconnection events (Batch 006 guardrail)

For MMS reconnection/EDR papers, keep the first pass narrow and explicit: plan with spedas_overview / plan_spedas_observation, fetch burst FGM/FPI/EDP artifacts, then chain into existing analysis helpers before asking for new code. Use analyze_minvar_coordinates plus transform_timeseries_coordinates for LMN or field-aligned panels, and use *-DIST artifacts with compute_particle_spectra(..., spectrum_types=["energy", "pitch_angle"]) for PAD/energy claims. A single-spacecraft e*n_e*(V_i-V_e) current or J·E' is a transparent proxy, not a curlometer or paper-quality heating result; mark it proxy unless the interval, LMN/FAC basis, calibrated E-field, and MMS1-4 curlometer diagnostics are all verified. If the paper/supplement interval cannot be verified, record candidate_interval or availability_failure instead of widening the fetch or claiming reproduction.

Heliospheric ICME/SEP multi-spacecraft events (Batch 007 guardrail)

For Wind/ACE/STEREO/OMNI ICME, magnetic-cloud, CME-CME, or SEP papers, use paper-reproduction as the outer artifact contract and treat docs/examples/stereo_icme_multispacecraft.md as the reduced first-pass recipe. Start with STEREO MAG 1min + PLASTIC proton moments for multi-day events, add Wind/ACE/OMNI only with explicit source/propagation labels, and keep SEP products as reduced_sep_proxy until telescope/species/energy-channel metadata are verified. Batch 007 confirmed that STEREO/PLASTIC/SEPT routing already exists; the repeated gap is discoverability, event seeds, variable-alias provenance, and overclaim prevention. Do not promote shock/sheath/cloud boundaries, SEP onset/fluence, or SECCHI/HI J-map context to paper-quality outputs unless those products and methods are explicitly loaded and documented.

Read the full file on GitHub · 85 lines

Changes

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.

  1. 5d ago First seen · 85 lines · 26 tokens per session scan A d888ea4101d0

Subscribe to this mod's changes

spedas-workflow is a skill published in the GitHub repository spedas/spedas_agent_kit (3 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,093 once invoked, about $0.0001 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.

Related

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…

anthropics/knowledge-work-plugins · 123 tokens

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…

K-Dense-AI/scientific-agent-skills · 83 tokens

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.

K-Dense-AI/scientific-agent-skills · 42 tokens

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.

K-Dense-AI/scientific-agent-skills · 68 tokens

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.

davila7/claude-code-templates · 43 tokens

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…

maziyarpanahi/openmed · 205 tokens