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Sds Gel Review

Community
BioTender-max
sds-gel-review

Review SDS-PAGE or protein purification gel images using DNA sequence, protein sequence, base-pair length, expected protein size, and lane labels. Use when the user wants to judge whether a gel ran well, whether the main band matches the expected product, or whether there may be impurities, degradation, aggregation, or low expression.

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namesds-gel-review
Stars
204
Forks
36
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BioTender-max on GitHub. Read the source before you install it.

Installation

Install the Sds Gel Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/sds-gel-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BioTender-max/awesome-bio-agent-skills.git /tmp/awesome-bio-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-bio-agent-skills/skills/bioclaw/sds-gel-review .claude/skills/sds-gel-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sds Gel Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sds Gel Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sds Gel Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SDS-Gel Review

Use this skill when the user provides a gel image plus any of:

  • DNA sequence
  • protein sequence
  • coding sequence length in bp
  • expected protein size in kDa
  • tag, construct, host, purification step, or lane labels

This is an interpretation skill, not a definitive assay. Distinguish what is directly observed from what is inferred.

Main goal

Given a gel image and partial sequence/context, produce a practical lab-style judgment:

  • did the gel run cleanly or poorly
  • is there a plausible main band
  • does the main band roughly match the expected target
  • are there obvious impurities, degradation, smearing, or aggregation
  • what extra information would most improve confidence

Inputs to request or infer

Collect as many of these as possible before judging:

  • gel image
  • whether the image is SDS-PAGE, western blot, or native gel
  • DNA sequence or protein sequence
  • if only bp is given, whether it is coding sequence length
  • expected protein name
  • expected molecular weight in kDa
  • expression host
  • tag or fusion partner
  • purification step or lane meaning
  • whether reducing conditions were used
  • whether a ladder is visible

If some are missing, continue with a lower-confidence interpretation.

How to reason

1. Normalize the sequence information

  • If the user provides a protein sequence, use it directly.
  • If the user provides a DNA sequence, treat it as coding sequence only if the context supports that.
  • If the user provides only bp length, estimate protein length as bp / 3 aa only when it is likely a coding region.
  • Estimate theoretical protein mass with the rough rule 110 Da per amino acid.
  • Convert to kDa and note that tags, signal peptides, cleavage, glycosylation, oligomerization, and unusual composition may shift apparent migration.

2. Read the gel image conservatively

Inspect the image for:

  • lane count and lane boundaries
  • visible labels above or below lanes
  • presence or absence of ladder
  • strongest band in each relevant lane
  • approximate relative migration of the strongest band
  • extra lower bands suggesting degradation
  • extra upper bands suggesting dimers, aggregates, uncleaved fusion, or contaminants
  • smearing, overloaded lanes, distorted fronts, or uneven running
  • enrichment or loss across purification lanes if the labels indicate flow-through, wash, elution, pellet, or lysate

If the ladder is missing, use labeled lane order and relative migration only. Do not invent exact kDa values.

3. Compare expectation vs observation

Decide whether the image most supports one of these:

  • likely target band at approximately expected size
  • possible target band but confidence limited
  • target band not clearly visible
  • strong expression but poor purity
  • purified sample but with degradation
  • aggregation or high-MW species likely
  • gel quality too poor for a reliable call

4. Explain confidence

Confidence is higher when:

  • ladder is visible
  • expected protein size is provided
  • lane labels are clear
  • construct/tag information is available
  • the gel has multiple purification steps that tell a consistent story

Confidence is lower when:

  • only bp count is provided
  • ladder is absent
  • lane labels are missing
  • image quality is low
  • the construct may include tags or cleavage not described

Important guardrails

  • Never claim a precise molecular weight from the image when no ladder is visible.
  • Never claim a band is definitely the target protein from DNA length alone.
  • Clearly separate:
    • Observation: what the image visibly shows
    • Inference: what might explain it
  • If the image quality or metadata are insufficient, say so plainly.
  • Prefer practical lab wording over overconfident structural biology language.

Output format

Use this structure:

Overall judgment

  • One short paragraph on whether the gel looks interpretable and whether the target seems present.

What I can directly see

  • lane pattern
  • strongest band(s)
  • impurities, smear, aggregation, or degradation
  • whether purification appears to improve the sample

Expected size estimate

  • from protein sequence, DNA sequence, or bp count
  • state assumptions clearly

Does the observed band roughly match?

  • yes / maybe / unclear / no
  • explain why

Main concerns

  • 1 to 3 likely issues only

What would help next

  • ask for the smallest missing item that would improve confidence, such as:
    • ladder annotation
    • lane labels
    • exact coding sequence length
    • tag/fusion information
    • expected kDa
    • original uncropped image

Practical heuristics

  • Approximate aa count from coding sequence: bp / 3
  • Rough protein mass: aa * 110 Da
  • His-tag or small peptide tags usually shift size only slightly
  • Large fusion tags can meaningfully shift migration
  • Smear below the main band often suggests degradation or proteolysis
  • Signal near the top or stacking boundary can suggest aggregation or incomplete denaturation
  • A very thick single band with little else may still reflect overloading

Example task types

  • "Here is a DNA sequence and SDS-PAGE image. Did my purification work?"
  • "This construct is 978 bp. Does the main band in this gel make sense?"
  • "I expect a 42 kDa protein. Which lane looks best?"
  • "No ladder on this gel, but the lanes are labeled lysate, wash, elution. Please judge whether the elution looks clean."

Frequently asked questions

What does the Sds Gel Review AI skill do?

Review SDS-PAGE or protein purification gel images using DNA sequence, protein sequence, base-pair length, expected protein size, and lane labels. Use when the user wants to judge whether a gel ran well, whether the main band matches the expected product, or whether there may be impurities, degradation, aggregation, or low expression.

Why use Sds Gel Review on TypingMind?

Because you install it once and use it with any model. Sds Gel Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sds Gel Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/sds-gel-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sds Gel Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sds Gel Review?

As many as you like. As long as a model supports skills, you can use Sds Gel Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sds Gel Review AI skill free?

It is published on GitHub by BioTender-max. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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