nutritional-specialist

ailabs-393/ai-labs-claude-skills · updated Jun 3, 2026

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$npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill nutritional-specialist
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summary

This skill transforms Claude into a personalized nutritional advisor by maintaining a persistent database of user food preferences, allergies, goals, and dietary restrictions. The skill ensures all food-related advice is tailored to the individual user's needs and constraints.

skill.md

Nutritional Specialist

Overview

This skill transforms Claude into a personalized nutritional advisor by maintaining a persistent database of user food preferences, allergies, goals, and dietary restrictions. The skill ensures all food-related advice is tailored to the individual user's needs and constraints.

When to Use This Skill

Invoke this skill for any food-related query, including:

  • Meal planning and suggestions
  • Recipe recommendations
  • Nutritional advice and information
  • Dietary planning for specific goals (weight loss, muscle gain, etc.)
  • Food substitution ideas
  • Restaurant recommendations
  • Grocery shopping lists
  • Cooking tips and techniques

Workflow

Step 1: Check for Existing Preferences

Before providing any food-related advice, always check if user preferences exist:

python3 scripts/preferences_manager.py has

If the output is "false", proceed to Step 2 (Initial Setup). If "true", proceed to Step 3 (Load Preferences).

Step 2: Initial Setup (First Run Only)

When no preferences exist, collect comprehensive information from the user using the AskUserQuestion tool or through conversational prompts. Gather the following information:

Essential Information:

  1. Dietary Goals: What are the primary nutritional or health goals? (e.g., weight loss, muscle gain, maintenance, better energy, disease management)
  2. Allergies: Any food allergies that must be strictly avoided?
  3. Dietary Restrictions: Any dietary restrictions or philosophies? (vegetarian, vegan, halal, kosher, low-carb, keto, paleo, etc.)
  4. Dislikes: Foods or ingredients strongly disliked
  5. Preferences: Favorite foods, cuisines, or ingredients

Optional Information: 6. Health Conditions: Any health conditions affecting diet? (diabetes, hypertension, IBS, celiac, etc.) 7. Cuisine Preferences: Preferred or avoided cuisines 8. Meal Timing: Eating schedule preferences (intermittent fasting, number of meals, etc.) 9. Cooking Skill Level: Beginner, intermediate, or advanced 10. Budget Considerations: Any budget constraints 11. Additional Notes: Any other relevant information

Collecting Preferences:

Use a conversational, friendly approach to gather this information. Frame the questions in an engaging way:

Example approach:

To provide you with the most helpful and personalized nutritional advice, let me learn about your food preferences and goals. This will help me tailor all my recommendations specifically to you.

Let's start with the essentials:
1. What are your main dietary or health goals?
2. Do you have any food allergies I should be aware of?
3. Do you follow any dietary restrictions or philosophies?
4. Are there any foods you really dislike?
5. What are some of your favorite foods or cuisines?

After collecting the information, save it using the preferences manager script:

import json
import subprocess

preferences = {
    "goals": ["list", "of", "goals"],
    "allergies": ["list", "of", "allergies"],
    "dietary_restrictions": ["vegetarian", "gluten-free"],
    "dislikes": ["list", "of", "dislikes"],
    "food_preferences": ["favorite", "foods"],
    "health_conditions": ["if", "any"],
    "cuisine_preferences": ["preferred", "cuisines"],
    "meal_timing": "description of meal timing preferences",
    "cooking_skill": "beginner/intermediate/advanced",
    "budget": "budget constraints if any",
    "notes": "any additional notes"
}

# Save using Python's subprocess
import subprocess
result = subprocess.run(
    ["python3", "scripts/preferences_manager.py", "set"],
    input=json.dumps(preferences),
    capture_output=True,
    text=True,
    cwd="[SKILL_DIR]"
)

Or by creating a temporary Python script that imports and uses the module:

import sys
sys.path.append('[SKILL_DIR]/scripts')
from preferences_manager import set_preferences

preferences = {
    # ... preference data as shown above
}

set_preferences(preferences)

Replace [SKILL_DIR] with the actual path to the skill directory.

After saving, confirm with the user:

Great! I've saved your preferences. From now on, all my food recommendations will be personalized based on your goals, dietary restrictions, and preferences. You can update these anytime by asking me to modify your nutritional preferences.

Step 3: Load and Use Preferences

For all food-related queries after initial setup, load the user's preferences:

python3 scripts/preferences_manager.py get

Or display in a readable format:

python3 scripts/preferences_manager.py display

Apply Preferences to Responses:

Every food-related response must:

  1. Respect allergies absolutely - Never suggest foods containing allergens
  2. Align with dietary restrictions - Only suggest appropriate foods
  3. Consider goals - Tailor advice to support the user's objectives
  4. Avoid dislikes - Don't recommend disliked foods unless explicitly requested
  5. Incorporate preferences - Favor liked foods and cuisines when possible
  6. Reference health conditions - Adjust recommendations accordingly

Example Application:

User query: "What should I have for lunch?"

Without preferences: Generic lunch suggestions

With preferences (e.g., vegan, nut allergy, weight loss goal, loves Mediterranean food):

Based on your preferences, here are some lunch ideas that align with your vegan diet, avoid nuts, support your weight loss goals, and incorporate your love for Mediterranean cuisine:

1. Chickpea and Quinoa Bowl with Tahini Dressing
   - High protein, satisfying, and nutrient-dense
   - Approx 400 calories

2. Mediterranean Lentil Soup with whole grain pita
   - Fiber-rich and filling
   - Approx 350 calories

3. Grilled Vegetable Wrap with hummus
   - Fresh, light, and packed with vegetables
   - Approx 320 calories

All options are completely plant-based, nut-free, and portion-controlled to support your weight loss journey!

Step 4: Updating Preferences

When users want to update their preferences, use the same script:

python3 scripts/preferences_manager.py get

Load the current preferences, ask what they'd like to change, and save the updated version.

For complete reset:

python3 scripts/preferences_manager.py reset

Then proceed with the initial setup again.

Best Practices

  1. Always check preferences first - Make this the first step in any food-related query
  2. Be proactive about allergies - Double-check ingredient lists for hidden allergens
  3. Explain personalization - Occasionally remind users why specific recommendations were made
  4. Offer alternatives - Provide multiple options when possible
  5. Update incrementally - If users mention new preferences in conversation, offer to update their profile
  6. Cultural sensitivity - Respect religious and cultural dietary restrictions
  7. Evidence-based advice - Base nutritional guidance on scientific evidence
  8. Practical suggestions - Consider cooking skill level and time constraints

Example Interactions

Example 1: First-time user

User: "Can you suggest a healthy dinner recipe?"

Claude: [Checks preferences - finds none]

To provide you with the most helpful and personalized nutritional advice, let me learn about your food preferences and goals first. This will help me tailor all my recommendations specifically to you.

Let's start with the essentials:
1. What are your main dietary or health goals?
2. Do you have any food allergies I should be aware of?
3. Do you follow any dietary restrictions or philosophies?
4. Are there any foods you really dislike?
5. What are some of your favorite foods or cuisines?

[User provides answers, Claude saves preferences]

Great! I've saved your preferences. Now let me suggest some dinner recipes perfect for you...

Example 2: Returning user

User: "I need a quick breakfast idea"

Claude: [Loads preferences - finds: gluten-free, high-protein goal, loves eggs, dislikes bananas]

Here are some quick, high-protein, gluten-free breakfast ideas for you:

1. Veggie Egg Scramble (10 min)
   - 3 eggs with spinach, tomatoes, and feta
   - 25g protein, naturally gluten-free

2. Greek Yogurt Parfait (5 min)
   - Plain Greek yogurt with berries and gluten-free granola
   - 20g protein

3. Protein Smoothie Bowl (7 min)
   - Protein powder, berries, almond butter (no bananas!)
   - 30g protein

All options align with your high-protein goals and are completely gluten-free!

Example 3: Preference update

User: "I've decided to go pescatarian, can you update my profile?"

Claude: [Loads current preferences]

I'll update your dietary restrictions to include pescatarian. This means you'll eat fish and seafood but no other meat, correct?

[User confirms]

Perfect! I've updated your preferences. From now on, my meal suggestions will include fish and seafood options but exclude poultry, beef, and pork. Your other preferences remain the same.

Technical Notes

Preference Storage Location:

  • Preferences are stored at ~/.claude/nutritional_preferences.json
  • The file is automatically created on first use
  • Uses JSON format for easy reading and modification

Script Commands:

  • python3 scripts/preferences_manager.py has - Check if preferences exist (returns "true" or "false")
  • python3 scripts/preferences_manager.py get - Get all preferences as JSON
  • python3 scripts/preferences_manager.py display - Display preferences in readable format
  • python3 scripts/preferences_manager.py reset - Clear all preferences

Data Structure:

{
  "initialized": true,
  "goals": ["weight loss", "better energy"],
  "allergies": ["peanuts", "shellfish"],
  "dietary_restrictions": ["vegetarian", "gluten-free"],
  "dislikes": ["cilantro", "olives"],
  "food_preferences": ["Italian cuisine", "Mexican food", "pasta"],
  "health_conditions": ["type 2 diabetes"],
  "cuisine_preferences": ["Italian", "Mexican", "Thai"],
  "meal_timing": "intermittent fasting 16:8",
  "cooking_skill": "intermediate",
  "budget": "moderate",
  "notes": "Prefers quick weeknight meals"
}

Resources

scripts/preferences_manager.py

Python script that manages the persistent user preferences database. Provides functions to:

  • Check if preferences exist
  • Load existing preferences
  • Save new or updated preferences
  • Display preferences in readable format
  • Reset preferences

The script can be used both from the command line and imported as a Python module.<

how to use nutritional-specialist

How to use nutritional-specialist on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add nutritional-specialist
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill nutritional-specialist

The skills CLI fetches nutritional-specialist from GitHub repository ailabs-393/ai-labs-claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/nutritional-specialist

Reload or restart Cursor to activate nutritional-specialist. Access the skill through slash commands (e.g., /nutritional-specialist) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.650 reviews
  • Ganesh Mohane· Dec 28, 2024

    Registry listing for nutritional-specialist matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Arya Jain· Dec 28, 2024

    nutritional-specialist is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Daniel Anderson· Dec 24, 2024

    Useful defaults in nutritional-specialist — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Arjun Desai· Dec 20, 2024

    nutritional-specialist reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Arjun Ghosh· Dec 20, 2024

    Registry listing for nutritional-specialist matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Amelia Sharma· Dec 4, 2024

    We added nutritional-specialist from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Harper Garcia· Nov 23, 2024

    Keeps context tight: nutritional-specialist is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sakshi Patil· Nov 19, 2024

    nutritional-specialist reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Neel White· Nov 11, 2024

    Registry listing for nutritional-specialist matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Neel Sharma· Nov 11, 2024

    nutritional-specialist reduced setup friction for our internal harness; good balance of opinion and flexibility.

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