viennarna-structure-prediction SKILL.md skillA

viennarna-structure-prediction from jaechang-hits/sciagent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add jaechang-hits/sciagent-skills/viennarna-structure-prediction@git:20260428.81ea2d2

Pins git:20260428.81ea2d2 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
jaechang-hits/sciagent-skills
Path
skills/molecular-biology/viennarna-structure-prediction/SKILL.md
Versions recorded
2
Latest version
git:20260428.81ea2d2, committed Apr 28, 2026
First version recorded
Feb 18, 2026
Size
21,720 bytes
Repository stars
381
License
a license file GitHub does not identify

Predict RNA secondary structure, MFE folding, base-pair probabilities, RNA-RNA interactions via ViennaRNA Python bindings. Pipeline: sequence → MFE → partition function and pair-probability matrix → dot-bracket → duplex. Use for siRNA/sgRNA targeting, ribozyme design, RNA accessibility. Use RNAfold CLI for batch use without Python.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# ViennaRNA Structure Prediction

## Overview

ViennaRNA is the gold-standard toolkit for RNA secondary structure prediction based on thermodynamic nearest-neighbor parameters. It predicts the minimum free energy (MFE) structure and dot-bracket notation for a given RNA sequence, computes the full partition function to obtain base pair probabilities, and models RNA-RNA interactions via co-folding and duplex prediction. The Python bindings (`import RNA`) expose the full ViennaRNA C library with sequence-level and fold-compound APIs. Command-line programs (`RNAfold`, `RNAalifold`, `RNAduplex`) are also available and demonstrated here.

## When to Use

- Predicting the minimum free energy secondary structure of an RNA sequence (mRNA, lncRNA, miRNA precursor, aptamer)
- Computing base pair probability matrices to assess structural uncertainty and identify well-defined stem-loops
- Designing or evaluating siRNA accessibility by folding the target mRNA region and checking for double-stranded structure
- Assessing sgRNA targeting efficiency by predicting guide RNA secondary structure that may reduce on-target activity
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add jaechang-hits/sciagent-skills/viennarna-structure-prediction@sha256:0fe2f843398be645

The line at the top of this page pins the label git:20260428.81ea2d2, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_scf6u74unkou5kct.svg)](https://markdownregistry.com/a/art_scf6u74unkou5kct)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 2 versions of this file, between Feb 18, 2026 and Apr 28, 2026, about one every 69 days.

versioncommittedcommitsizeaudit
git:20260428.81ea2d2 latest2026-04-28 81ea2d2 21,720 BA view · diff
git:20260218.8df16942026-02-18 8df1694 21,874 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (21720 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

jaechang-hits/sciagent-skills · 381 stars · license a license file GitHub does not identify · pushed 2026-10-16 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_scf6u74unkou5kct
GET https://markdownregistry.com/api/v1/resolve?ref=jaechang-hits/sciagent-skills/viennarna-structure-prediction
GET https://markdownregistry.com/api/v1/blob/0fe2f843398be6454839dfc6047cb01f20f9d5dc2857021d752a9f4f74a29917

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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