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Agent skill

Relion

by K-Dense-AI in K-Dense-AI/scientific-agent-skills

Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing.

MITAuto-check passedResearch & Science

Install Relion

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill relion -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills relion --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/relion .claude/skills/relion && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
relion
GitHub stars
48k
Used in
1 other repo
Token cost
~2k tokens
SKILL.md length
797 words
Files
4 (incl. scripts, references)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing.

  • Research & Science work in your project
  • SKILL.md covers Preserve acquisition and…, Refine a selected particle…, Inspect independent half maps and Postprocess with a soft mask, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Relion is an agent skill from K-Dense-AI/scientific-agent-skills. Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing. Supports STAR optics/acquisition checks, particle-stack consistency, gold-standard half sets, soft-mask validation, diagnostic Fourier shell correlation, and restart guidance.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/acquisition-and-restarts.md`, `references/runtime-and-validation.md` and `scripts/spa_workflow.py`). Compatibility notes: Python 3.12+ with numpy, mrcfile and starfile for bundled validation; RELION 5.0.1 CPU/MPI executables for refinement and postprocessing. Native workflows…

It sits in Research & Science. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “Use the relion skill to validate and executes RELION single-particle cryo-EM refinement and half-map postprocessing”
  • “/relion”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Python 3.12+ with numpy, mrcfile and starfile for bundled validation; RELION 5.0.1 CPU/MPI executables for refinement and postprocessing. Native workflows require MPI, OpenMP and an FFT library (FFTW or MKL). GPU builds require their supported accelerator stack. Network is needed for installation only.

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • relion.readthedocs.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Python 3.12+ with numpy, mrcfile and starfile for bundled validation; RELION 5.0.1 CPU/MPI executables for refinement and postprocessing. Native workflows require MPI, OpenMP and an FFT library (FFTW or MKL). GPU builds require their supported accelerator stack. Network is needed for installation only.

    From compatibility in the SKILL.md frontmatter.

Context cost

Relion loads about 2k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 797 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 797 words, ~2,019 tokens.

Download SKILL.mdSave it as .claude/skills/relion/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
relion
description
Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing. Supports STAR optics/acquisition checks, particle-stack consistency, gold-standard half sets, soft-mask validation, diagnostic Fourier shell correlation, and restart guidance.
compatibility
Python 3.12+ with numpy, mrcfile and starfile for bundled validation; RELION 5.0.1 CPU/MPI executables for refinement and postprocessing. Native workflows require MPI, OpenMP and an FFT library (FFTW or MKL). GPU builds require their supported accelerator stack. Network is needed for installation only.
license
MIT
metadata.version
1.1
metadata.skill-author
K-Dense Inc.
metadata.upstream-version
5.0.1
metadata.last-reviewed
2026-10-01

RELION single-particle refinement

Use for a RELION single-particle project, especially extracted particles → homogeneous selected particle subset → gold-standard refinement → half-map validation and postprocessing. The bundled runner starts from CTF-annotated extracted particles and an initial 3D reference. It does not replace motion correction, picking, 2D/3D selection, or a biological interpretation of map quality. For tomography, helical reconstruction, Blush, or heterogeneous-state modeling, use the appropriate upstream workflow rather than forcing those data into this bounded SPA runner.

Preserve acquisition and coordinate conventions

Read references/acquisition-and-restarts.md when starting from movies or resuming jobs. Confirm pixel size in Å/pixel, voltage in kV, spherical aberration in mm, defocus in Å, amplitude contrast as a fraction, and the symmetry justified by the specimen. Do not “correct” a suspicious value by guessing its units.

data_optics describes acquisition/image groups; data_particles references them through _rlnOpticsGroup. Particle filenames use one-based index@stack.mrcs; leading zeros such as 00000001@stack.mrcs are valid. Relative paths resolve from the RELION project directory, not the STAR file's directory. Keep optics groups when merging or subsetting STAR files. _rlnOriginXAngst/_rlnOriginYAngst are Å translations, not pixels.

Run from this skill directory with paths to the real project:

bash
python scripts/spa_workflow.py validate-star project/particles.star --project project

This opens referenced stacks and checks optics membership, finite acquisition/CTF values, indices, box sizes, duplicate particle references and existing half-set assignments. Use --metadata-only only when stacks are genuinely unavailable; the JSON records stack_checks_performed: false. It does not scan every particle pixel for corruption or establish correct image normalization. Physical-range warnings are review prompts, not proof that unusual microscope settings are wrong.

Refine a selected particle population

Before running, inspect representative particles and class averages, defocus distributions, CTF fits, particle orientation distribution, and the initial reference. Ensure the map and particle boxes/pixel sizes agree after any downsampling. The runner deliberately supports one effective box/pixel size across optics groups; handle heterogeneous sampling with an explicit upstream resampling workflow. Use conventionally extracted, normalized particles that have not already been phase-flipped or Wiener-filtered; this runner does not configure those special input cases.

bash
python scripts/spa_workflow.py refine \
  --star project/particles.star --reference project/initial.mrc \
  --project project --diameter 180 --symmetry C1 \
  --initial-lowpass 40 --mpi-ranks 3 --threads 2 --output project/RefinePilot

The diameter and low-pass filter above are illustrative Å values. Use specimen-appropriate values. Refinement executes mpirun -np 3 relion_refine_mpi with --auto_refine, --split_random_halves, --ctf, and a low-pass starting reference. Gold-standard splitting requires MPI; the plain sequential relion_refine executable cannot perform this split. Use odd ranks ≥3 (master plus balanced half-set workers), with a matching MPI installation. The CPU command is useful for a bounded pilot; choose a documented GPU/MPI launch for full data.

The runner keeps the command, native version and log in a new output directory, records an explicit random seed (default 1), surfaces runtime warnings, stops on process failure, and requires converged unfiltered half maps before reporting success. It does not automatically retry expensive jobs or silently discard failed-job artifacts. Keep _optimiser.star, model/sampling STAR files, and referenced particle paths for restart. Use the original job's optimiser rather than starting a new random split from a partially processed table.

Show full SKILL.md (325 more words)Show less

Inspect independent half maps

Use the two independently refined unfiltered half maps, never two copies of the combined, sharpened map. Matching headers cannot establish statistical independence; the independent particle assignments and refinement history provide that evidence. Inspect directional anisotropy, preferred orientation and local resolution as well as a global FSC curve.

bash
python scripts/spa_workflow.py fsc \
  project/RefinePilot/run_half1_class001_unfil.mrc \
  project/RefinePilot/run_half2_class001_unfil.mrc --output diagnostic-fsc.tsv

This checks map dimensions, finite values, pixel size, origin, axis order and duplicate maps, then writes an unmasked diagnostic FSC. The reported 0.143 crossing uses linear interpolation; null means no downward crossing was detected, not infinite resolution. Nyquist resolution is 2 × pixel size. This diagnostic is limited to even cubic maps ≤256³; use RELION's native relion_image_handler --fsc for larger maps. It does not substitute for mask-corrected FSC. The helper requires real-space maps with canonical axes, zero MRC start indices and orthogonal cell angles. Convert other grids explicitly with provenance; merely editing headers can misalign density. Matching headers and FSC cannot determine absolute handedness.

Postprocess with a soft mask

Construct the solvent mask from an appropriately low-pass-filtered density, with an expanded boundary and a smooth edge. Inspect all slices; a tight mask can inflate correlation. Avoid a mask derived from high-frequency noise shared between half maps.

bash
python scripts/spa_workflow.py postprocess \
  --half1 project/RefinePilot/run_half1_class001_unfil.mrc \
  --half2 project/RefinePilot/run_half2_class001_unfil.mrc \
  --mask project/soft_mask.mrc --output project/PostProcessPilot

The helper checks a nonconstant mask in [0,1], soft-edge voxels and matching map grids, then runs relion_postprocess with explicit half maps, mask and pixel size. RELION performs its own mask/randomization correction and writes postprocess.star. The bounded command leaves the B-factor at zero (no automatic B-factor estimation); add automatic/manual sharpening only after choosing a defensible fit range and inspecting map quality. A valid range and some fractional mask voxels do not prove the mask is scientifically appropriate. Inspect the phase-randomized masked FSC near the reported resolution: residual correlation calls for a smoother/wider mask and another postprocessing run.

See references/runtime-and-validation.md for the tested native utilities and the distinction between pipeline execution and reconstruction validation.

Primary references

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/relion of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • references/acquisition-and-restarts.md
  • references/runtime-and-validation.md
  • scripts/spa_workflow.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Last30daysmvanhorn/last30days-skill64k—~7.9kAutomated safety check: NotesMIT

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Categories

Questions about Relion

What does Relion do?

Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing. Relion is an agent skill from K-Dense-AI/scientific-agent-skills. Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing.

When should I use Relion?

Relion fits situations like: research & Science work in your project.

How do I install Relion in Claude Code?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill relion -a claude-code`. Or copy the skill folder (skills/relion in K-Dense-AI/scientific-agent-skills) into .claude/skills/relion in your project. Claude Code loads it when a task matches its description.

How do I install Relion in Codex?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill relion -a codex`. Or copy the skill folder (skills/relion in K-Dense-AI/scientific-agent-skills) into .agents/skills/relion in your project. Codex loads it when a task matches its description.

Can I use Relion in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/scientific-agent-skills --skill relion -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/relion, .gemini/skills/relion, .github/skills/relion and .opencode/skills/relion in your project.

What does Relion need to run?

Going by SKILL.md and its folder, Relion needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Compatibility (from SKILL.md): Python 3.12+ with numpy, mrcfile and starfile for bundled validation; RELION 5.0.1 CPU/MPI executables for refinement and postprocessing. Native workflows require MPI, OpenMP and an FFT library (FFTW or MKL). GPU builds require their supported accelerator stack. Network is needed for installation only..

Does Relion access the network?

SKILL.md names 1 domain. As links in the text: relion.readthedocs.io. This is read from the text; nothing was executed.

Is Relion safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Relion use?

Relion is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Relion use?

About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.3k tokens, read only when the agent opens those files.

What are the alternatives to Relion?

Skills that share tags, products or a category with Relion: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Relion?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.

Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.