esm skill for protein language models, including ESM3 generation and ESM C embeddings. Use this esm guide for protein sequence design, inverse folding, function prediction, and code generation workflows with local inference or the Forge API.
This skill scores 68/100, which means it is acceptable to list but should be presented with caveats. The repository shows a real, non-placeholder workflow for protein modeling and design, so directory users have enough evidence to decide it may fit scientific-agent tasks, but they should expect to rely on the embedded examples rather than a broader support ecosystem.
- Strong triggerability for protein-sequence, structure, function, and engineering tasks, with a description that names ESM3 and ESM C use cases.
- Substantial operational content: the SKILL.md is long, has many headings, and includes code examples rather than a stub or demo-only page.
- Covers both local model usage and Forge API inference, giving agents more than one execution path.
- No install command, support files, or references are provided, so adopters get limited guidance beyond the skill text itself.
- The repository appears focused on one skill file only, which may leave setup, prerequisites, and edge-case handling under-specified for first-time users.
Overview of esm skill
What the esm skill does
The esm skill helps you use ESM protein language models for sequence generation, structure-aware design, embeddings, and inverse folding. It is a good fit when you need protein outputs rather than general text: for example, designing variants, filling in missing residues, or producing representations for downstream analysis. This esm skill is most useful when you want a practical starting point for ESM3 or ESM C without reading the full repo first.
Who should use it
Use this esm skill if you are doing protein engineering, computational biology, or model-assisted sequence design and need a clearer workflow than a generic prompt can give. It is especially relevant for users deciding between local inference and the Forge API, or for teams that need a repeatable esm guide for code generation and analysis tasks.
What matters before install
The main decision points are model fit, environment fit, and task fit. ESM3 is aimed at multimodal generation across sequence, structure, and function, while ESM C is better for embeddings and representation learning. If your goal is simple annotation or non-protein NLP, this skill is probably the wrong tool.
How to Use esm skill
Install and inspect first
Install the esm skill with npx skills add K-Dense-AI/claude-scientific-skills --skill esm. After install, open SKILL.md first, then check any linked repo files the skill references. In this repository, the important thing is not a large support tree but the main instruction file itself, so the fastest path is to read the overview, usage example, and any code blocks before you prompt.
Shape a strong request
For best esm usage, give the model the protein job, not just a vague instruction. Include the target task, input type, constraints, and success criteria. Better prompts say things like: “Generate 12 candidate variants of this 180-aa enzyme, preserve the catalytic motif, and optimize for stability without changing the active-site residues.” Weak prompts like “improve this protein” leave too much room for guesswork.
Match the workflow to the model
Use ESM3 when you need generation, design, or structure-aware reasoning. Use ESM C when you need embeddings, similarity search, or compact representations. If you are writing code around the skill, make sure your prompt distinguishes whether you need local inference steps, Forge API usage, or a reusable Python workflow.
Read the repo in the right order
Start with SKILL.md and then jump to the code example nearest your task. For esm for Code Generation, the most useful details are the input object types, inference client setup, and how generated outputs are formatted. If you are porting the skill into another environment, capture the model name, prompt structure, and any preprocessing assumptions before you adapt the code.
esm skill FAQ
Is esm only for advanced protein projects?
No. The esm skill can help beginners get started, but the tasks still require basic domain context. If you know the protein you are working on and can describe the objective clearly, the skill can guide a useful first pass.
How is this different from a normal prompt?
A normal prompt may produce generic advice. The esm skill is centered on ESM-specific workflows, so it is better when you need model selection, protein-aware inputs, and outputs that can be used in code generation or scientific analysis.
Should I use local models or Forge?
Use local models when you want more control, reproducibility, or offline execution. Use Forge when you want managed inference and do not want to handle local model loading. The right choice depends on latency, hardware, and whether your workflow needs to scale.
When should I not use the esm skill?
Do not use it if your task is not protein-related, or if you need a wet-lab protocol rather than computational design. It is also not the best choice if your main goal is only generic sequence formatting with no modeling step.
How to Improve esm skill
Give the model the right biological constraints
The quality of esm results depends on how well you specify residues, motifs, regions to preserve, and the property you want to change. Strong inputs reduce invalid designs and make the output easier to evaluate. If you care about stability, solubility, binding, or function, name that explicitly instead of asking for “better” sequences.
Provide structured input, not a blob
A useful esm prompt usually includes the wild-type sequence, target region, allowed edits, excluded positions, and any scoring preference. For example, mark conserved residues separately from editable positions. This is especially important for esm usage in code generation, where clean structure makes the workflow easier to automate.
Iterate with filters and comparison
Do not stop at the first generated set. Compare candidates by the property you care about, reject sequences that violate constraints, and rerun with tighter instructions if the output drifts. If the first pass is too broad, narrow the design space; if it is too conservative, relax only one constraint at a time.
Watch for the common failure modes
The most common problems are underspecified objectives, mixing generation with evaluation in one prompt, and asking for outputs without enough sequence context. If the result is not usable, revise the prompt with clearer boundaries and stronger examples.