servicex

servicex is a skill for Claude Code from iris-hep/marketplace. It costs 74 tokens per session (890 once invoked), scanned A, original, BSD-3-Clause.

A skill for writing ServiceX queries in func_adl against ATLAS xAOD datasets. ServiceX retrieves selected data from formats such as PHYSLITE and PHYS, which are ATLAS event-data formats.

In plain words
What is it for?
Use it to build, edit, or debug queries, select ATLAS datasets, filter physics objects or events, choose output columns, and retrieve the result with deliver.
Why use it?
It helps structure object and event filters correctly, choose the matching dataset type, and return data in a layout that later analysis code can use.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the iris-hep plugin — 7 skills, 1 command, 2 agents shipped together

Good fit Use it to build, edit, or debug queries, select ATLAS datasets, filter physics objects or events, choose output columns, and retrieve the result with deliver.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/iris-hep/marketplace/servicex
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.

Any agent
npx skills add iris-hep/marketplace --skill servicex
Clone the repo
git clone --depth 1 https://github.com/iris-hep/marketplace

Made for: Claude Code.

Or install iris-hep, the plugin that ships this one along with the rest of its 7 skills, 1 command, 2 agents.

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 servicex

README.md
[![agentmods](https://agentmods.dev/badge/skills/iris-hep/marketplace/servicex/github.svg)](https://agentmods.dev/skills/iris-hep/marketplace/servicex)
Your own site
<a href="https://agentmods.dev/skills/iris-hep/marketplace/servicex"><img src="https://agentmods.dev/badge/skills/iris-hep/marketplace/servicex/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.

agentmods 80×15 button for servicex

Your own site · 80×15
<a href="https://agentmods.dev/skills/iris-hep/marketplace/servicex"><img src="https://agentmods.dev/badge/skills/iris-hep/marketplace/servicex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 890 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00074 $0.00890
Opus 5 $0.00037 $0.00445
Sonnet 5 $0.00015 $0.00178
Haiku 4.5 $0.00007 $0.00089

Measured 9d ago against content hash 5604021e9427, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

servicex 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.

iris-hep/skills/servicex/SKILL.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ServiceX

Overview

Provide concise, correct func_adl query patterns for ServiceX on ATLAS xAOD, with best practices for selections, outputs, and deliver usage.

Workflow

  1. Identify the dataset type and base query.
    • Use FuncADLQueryPHYSLITE for PHYSLITE or OpenData.
    • Use FuncADLQueryPHYS for PHYS or other derivations.
  2. Build a top-level Select that gathers all required collections and singletons.
    • Apply object-level filters with nested .Where inside this Select.
    • Do not pick columns yet.
  3. Apply event-level filtering with a top-level .Where after the collections Select.
  4. Create a final top-level Select that returns a single dictionary of output columns.
    • Convert units to standard LHC units (GeV, meters, etc.).
    • Never return a nested dictionary.
  5. Use deliver once with NFiles=1 by default and appropriate dataset source(s).
  6. Make sure that the layout of the data that will be returned is remembered - downstream tasks that want to work with the data will need to understand it.

Core Rules

  • Prefer two top-level Select calls: collections first, output columns second.
  • Filter objects with nested .Where; filter events with a top-level .Where.
  • Use a single final Select that returns a dictionary of outputs.
  • Do not use awkward functions inside ServiceX queries.
  • Use dataset.Rucio for rucio DIDs and dataset.FileList for URL lists.
  • Always set NFiles=1 by default.
  • For fetches where cache bypass matters, use ignore_local_cache=True in deliver.
  • If a transform fails and logs are required, respond with HELP USER.
  • Ensure func_adl_servicex_xaodr25 is listed as a dependency in the active project and installed in the current virtual environment before running or generating code that uses it.
  • When using xAOD tool helpers, add hep-llm-helpers>=1.0.0b1 to the active project; standalone PEP 723 scripts must list it in their dependency block, and imports use hep_llm_helpers.xaod_hints.
  • When defining an xAOD accessor, the Python variable assigned by make_tool_accessor must exactly match its function_name (for example, tag_weight = make_tool_accessor(..., function_name="tag_weight", ...)); use that same name in the later query. A mismatch causes an unknown-type translation error.
  • In standalone-script metadata, declare jinja2 explicitly if the environment requires it for func_adl_servicex_xaodr25 usage.

Read the full file on GitHub · 55 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. 9d ago First seen · 55 lines · 74 tokens per session scan A 5604021e9427

Subscribe to this mod's changes

servicex is a skill published in the GitHub repository iris-hep/marketplace (4 stars, last pushed 1mo ago), licensed BSD-3-Clause. It adds 74 tokens to every session and 890 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.

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