Guide · Agent skill
How to use the NLP Alignment Best Practices agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The NLP Alignment Best Practices skill (skill name nlp-alignment) gives an AI agent a reference guide on aligning large language models, which means training them to follow instructions and behave safely. It is written for ML engineers, students and research agents who are planning or reviewing a fine-tuning or alignment experiment. After reading this guide you will know how to install the skill, how to ask for it, and what it does and does not give you.
NLP Alignment Best Practicesaiming-lab/AutoResearchClaw · MIT Download the skill Quick start
- 1Download the ZIP. Get the nlp-alignment ZIP from this page.
- 2Unzip into a skills folder. Extract it into ~/.claude/skills/ for personal use, or .claude/skills/ inside a project.
- 3Or upload in the Claude apps. Go to Settings, then Capabilities, then Skills, and upload the ZIP.
- 4Ask an alignment question. Mention a topic such as RLHF, DPO, reward model or instruction tuning in your request.
- 5Check values against your setup. Verify any hyperparameter or training order suggestion against your model and data before running it.
What the nlp-alignment skill makes the agent do
An agent skill is a folder of instructions that an AI agent loads when a task matches it. This skill's instructions are a Markdown file called SKILL.md. It is static guidance: it contains no code that trains a model.
Once installed, the agent knows four methods: RLHF (train a reward model, then run PPO fine-tuning), DPO (direct preference optimization, which needs no reward model), GRPO (group relative policy optimization), and SFT (supervised fine-tuning as an alignment baseline). It also knows a suggested training order, starting hyperparameters, and three common failure modes.
- RLHF: train reward model, then PPO fine-tuning (complex but powerful)
- DPO: simpler, no reward model needed
- GRPO: group relative policy optimization
- SFT: supervised fine-tuning as an alignment baseline
- Failure modes to watch: reward hacking, mode collapse, catastrophic forgetting
Install the nlp-alignment skill
Download the ZIP file for the skill from this page. Then install it in one of two ways.
For a personal install, unzip the file into ~/.claude/skills/. For a single project, unzip it into the project's .claude/skills/ folder. Either way, the skill folder should contain SKILL.md.
If you use the Claude apps, you can upload the ZIP instead. Go to Settings, then Capabilities, then Skills, and upload it there.
- Personal: ~/.claude/skills/
- Project only: .claude/skills/ inside your project
- Claude apps: Settings → Capabilities → Skills
Trigger the skill with natural requests
You do not need a special command. The skill's description says to use it when working on alignment or safety, and its metadata lists trigger keywords: alignment, rlhf, dpo, reward model, preference, instruction tuning, and safety. Asking about any of these topics in plain language is the easiest way to load it.
Be specific about your goal. A request that names the method or the problem gives the agent more to work with than a general question.
- "Compare RLHF and DPO for a small instruction-tuning dataset I have."
- "What starting learning rate and beta should I try for DPO?"
- "My reward model scores keep rising but the outputs look repetitive. What could cause this?"
- "Suggest a training order for an alignment experiment on my base model."
What you get back and how to use it
The agent answers from the skill's guidance. For a training plan, that usually means the recommended order: start with SFT on high-quality instruction data, then apply a preference method, using a reference model for the KL penalty. For a safety check, the guidance says to evaluate on safety benchmarks, and names TruthfulQA and BBQ.
Treat the output as a starting point for your own design. The skill lists values without context, so check them against your model and data before you rely on them.
Starting values listed in the skill
The skill quotes these values for the recipe. They are listed without the setup they came from, so do not assume they transfer to your model or data.
- DPO: lr=5e-7, beta=0.1, batch_size=64
- PPO: lr=1e-6, clip=0.2, KL coeff=0.02
Safety notes and requirements
The skill lists no requirements. It does not run scripts and does not use the network. Because it is only a Markdown guide, it cannot run a training job, and it cannot certify a model as safe. The file names safety benchmarks but gives no evaluation procedure, so any safety conclusion needs a method you design and check yourself.
The skill is released under the MIT licence.
- Requires: nothing listed
- Runs scripts: no
- Uses network: no
- Files: SKILL.md
What people use it for
- Choosing between RLHF with PPO, DPO, GRPO and SFT for a fine-tuning project
- Planning a training order that starts with SFT on instruction data
- Diagnosing symptoms such as repetitive outputs or loss of general ability
- Learning the trade-offs between preference methods as a student
- Reviewing a research pipeline stage on model training or safety evaluation
Frequently asked questions
Is the nlp-alignment skill free?
The skill is released under the MIT licence. The source does not list a price, so check this page for current licence details.
What does the nlp-alignment skill require?
The skill lists no requirements. It does not run scripts and does not use the network, so it is only a Markdown guide that the agent reads.
How do I install the nlp-alignment skill?
Download the ZIP from this page and unzip it into ~/.claude/skills/ for personal use or into .claude/skills/ in a project. In the Claude apps, you can upload the ZIP under Settings, Capabilities, Skills.
Does this skill train a model for me?
No. It is static guidance in SKILL.md that describes methods, starting values and pitfalls. You still need your own training pipeline and data.
How does nlp-alignment compare with other alignment resources?
This page does not compare it with other resources. The skill is a short reference covering RLHF, DPO, GRPO and SFT, while a finished, tested training pipeline is a different kind of tool.
Can I use the skill to certify a model as safe?
No. The skill names benchmarks such as TruthfulQA and BBQ but gives no evaluation procedure, so it cannot certify safety.
New agent skills, repos and AI tools — every week
The best new skills and open-source projects, the fastest-growing repos, and one free prompt. Five minutes to read. Unsubscribe anytime.
Our verdict
Use this skill if you are an ML engineer, researcher or student who wants a compact reference on RLHF, DPO, GRPO and SFT, plus common failure modes to watch. Pick something else if you need a tested training pipeline or a method to evaluate model safety, since this skill provides neither.
Download the skill