field-suggestions

name	field-suggestions
description	Generate AI-assisted frontmatter field suggestions using para-obsidian LLM utilities. Demonstrates 3-layer architecture (constraints, prompt-builder, orchestration) for metadata extraction.
user-invocable	false
allowed-tools	[]

Field Suggestions Skill

Purpose

This skill demonstrates how to use the para-obsidian LLM utilities for AI-assisted field suggestions in custom slash commands. It shows how to leverage the 3-layer architecture (constraints, prompt-builder, orchestration) to build intelligent metadata extraction that respects vault context and frontmatter rules.

---

Architecture Overview

The para-obsidian plugin provides a 3-layer LLM utility architecture in src/llm/:

Layer 1: Constraints (constraints.ts)

Deterministic extraction with enum/wikilink/vault context awareness:

• buildConstraintSet(): Converts template + frontmatter rules into LLM constraints
• Handles enums, wikilinks, validation rules, and vault context
• Ensures AI suggestions respect PARA structure and Dataview compatibility

Layer 2: Prompt Builder (prompt-builder.ts)

Declarative, composable prompts:

• buildStructuredPrompt(): Assembles system role, task, content, constraints
• Separates concerns: what to extract vs how to format
• Reusable across different templates and use cases

Layer 3: Orchestration (orchestration.ts)

High-level workflows:

• suggestFieldValues(): Single-field suggestions
• convertNoteToTemplate(): Full note conversion with validation
• callOllama(): LLM integration with error handling
• parseOllamaResponse(): Structured response parsing

---

Usage Example

Basic Field Suggestion (Single Field)

import {
  buildConstraintSet,
  buildStructuredPrompt,
  callOllama,
  parseOllamaResponse,
  type VaultContext
} from './llm';
import { getTemplate } from './templates';
import { loadConfig } from './config';

async function suggestProjectMetadata(
  userTitle: string,
  userDescription: string
): Promise<{ args: Record<string, unknown>; title: string }> {
  // 1. Load config and template
  const config = await loadConfig();
  const template = getTemplate(config, 'project');

  // 2. Build vault context (for wikilink validation)
  const vaultContext: VaultContext = {
    areas: ['Health', 'Career', 'Family'], // from Dataview or cache
    resources: ['TypeScript', 'React'],
    projects: ['Website Redesign'],
    tags: ['#development', '#planning']
  };

  
   constraints = (
    template,
    config.,
    vaultContext
  );

  
   prompt = ({
    : ,
    : ,
    : ,
    constraints
  });

  
   response =  (prompt, );
   { args, title } = (response);

   { args, title };
}


 suggestions =  (
  ,
  
);

.(, suggestions.);







 confirmed =  (suggestions);
 (confirmed) {
   (suggestions., suggestions.);
}

Full Note Conversion (Multiple Fields)

import { convertNoteToTemplate } from './llm';
import { loadConfig } from './config';

async function convertExistingNote(
  noteContent: string,
  targetTemplate: 'project' | 'area' | 'resource' | 'task'
): Promise<{ title: string; args: Record<string, unknown> }> {
  const config = await loadConfig();

  // Vault context for wikilink validation
  const vaultContext = {
    areas: await getExistingAreas(),
    resources: await getExistingResources(),
    projects: await getExistingProjects(),
    tags: await getExistingTags()
  };

  // One-line conversion with full validation
  const result = await convertNoteToTemplate(
    noteContent,
    targetTemplate,
    config,
    vaultContext,
    'qwen2.5:7b'
  );

  return result;
}

// Example: Convert plain note to PARA project
const noteContent = ;

 converted =  (noteContent, );
.(converted);

Using suggestFieldValues() Helper

import { suggestFieldValues } from './llm';
import { loadConfig } from './config';

// Suggest single field value (e.g., for interactive prompts)
async function suggestArea(projectTitle: string, projectDescription: string): Promise<string> {
  const config = await loadConfig();
  const vaultContext = {
    areas: ['Health', 'Career', 'Family', 'Personal Growth']
  };

  const suggestion = await suggestFieldValues(
    `Title: ${projectTitle}\nDescription: ${projectDescription}`,
    'project',
    config,
    vaultContext,
    'qwen2.5:7b'
  );

  return suggestion.args.area as string; // Returns '[[Career]]' in wikilink format
}

// Use in interactive command
const suggestedArea = await suggestArea(
  'Learn TypeScript',
  'Master TypeScript for career advancement'
);

console.log(`AI suggests area: `);

---

Benefits

Why This Architecture Beats Monolithic Prompts

Before (Monolithic):

// Hard to maintain, error-prone, inconsistent
const prompt = `Extract metadata from this note. Use wikilinks for areas.
Available areas: Health, Career, Family.
Return JSON with area, status, tags.
Note: ${content}`;

After (Layered):

// Declarative, testable, reusable
const constraints = buildConstraintSet(template, rules, vaultContext);
const prompt = buildStructuredPrompt({ systemRole, task, content, constraints });

Advantages:

1. Separation of Concerns: Constraints ≠ Prompts ≠ Orchestration
2. Testability: Each layer tested independently (see src/llm/*.test.ts)
3. Reusability: Same constraints across different prompts
4. Maintainability: Change validation rules without touching prompts
5. Vault Awareness: Automatically respects existing areas/projects/tags
6. Dataview Compatibility: Enforces wikilink format for relationship fields

---

Use Cases

When to Use suggestFieldValues()

Best for:

• Single-field suggestions in interactive commands
• Quick metadata extraction without full validation
• Progressive disclosure (suggest one field at a time)
• User-guided workflows with confirmations

Example:

// Interactive project creation
const area = await suggestFieldValues(userInput, 'project', config, vault);
const confirmed = await prompt(`Use area: ${area.args.area}?`);
if (!confirmed) {
  area.args.area = await manualAreaSelection();
}

When to Use convertNoteToTemplate()

Best for:

• Batch note conversion
• Full frontmatter extraction with validation
• Automated workflows
• Migration scripts

Example:

// Batch convert all notes in a folder
for (const note of plainNotes) {
  const converted = await convertNoteToTemplate(note.content, 'project', config, vault);
  await updateNoteFrontmatter(note.path, converted.args);
}

---

Advanced Patterns

Custom Validation Post-Processing

import { parseOllamaResponse, callOllama } from './llm';

const response = await callOllama(prompt, model);
const { args, title } = parseOllamaResponse(response);

// Add custom validation
if (args.area && !vaultContext.areas.includes(args.area)) {
  console.warn(`AI suggested non-existent area: ${args.area}`);
  args.area = await promptUserForArea(); // Fallback to manual selection
}

// Add computed fields
args.created_date = new Date().toISOString().split('T')[0];
args.file_path = generateFilePath(title, args.area);

Retry with Refinement

import { callOllama, parseOllamaResponse } from './llm';

let attempts = 0;
let result;

while (attempts < 3) {
  try {
    const response = await callOllama(prompt, model);
    result = parseOllamaResponse(response);

    // Validate critical fields
    if (!result.args.area || !result.title) {
      throw new Error('Missing required fields');
    }

    break; // Success
  } catch (error) {
    attempts++;
    console.warn(`Attempt ${attempts} failed:`, error);

    // Refine prompt for retry
    prompt += '\nIMPORTANT: You must include both "title" and "area" fields.';
  }
}

if (!result) {
  throw new Error('Failed to extract metadata after 3 attempts');
}

---

Integration with Slash Commands

Example: /para-brain:ai-convert Command

// commands/ai-convert.md
import { convertNoteToTemplate } from '../src/llm';
import { loadConfig } from '../src/config';
import { getVaultContext } from '../src/vault';

export async function aiConvertCommand(
  notePath: string,
  targetTemplate: 'project' | 'area' | 'resource' | 'task'
) {
  const config = await loadConfig();
  const vaultContext = await getVaultContext(config.vault_path);
  const noteContent = await fs.readFile(notePath, 'utf-8');

  const result = await convertNoteToTemplate(
    noteContent,
    targetTemplate,
    config,
    vaultContext,
    'qwen2.5:7b'
  );

  console.log('\nAI Suggestions:');
  console.log(`Title: ${result.title}`);
  console.log(`Frontmatter:`, JSON.stringify(result., , ));

   confirmed =  ();
   (confirmed) {
     (notePath, result.);
    .();
  }
}

---

Testing Your AI Integrations

import { describe, test, expect } from 'bun:test';
import { buildConstraintSet, buildStructuredPrompt } from './llm';

describe('AI Field Suggestions', () => {
  test('builds constraints with vault context', () => {
    const template = getTemplate(config, 'project');
    const vaultContext = { areas: ['Career'], resources: [], projects: [], tags: [] };

    const constraints = buildConstraintSet(template, config.frontmatterRules, vaultContext);

    expect(constraints).toContain('area must be a wikilink from: [[Career]]');
    expect(constraints).toContain('status must be one of: active, planning, on-hold, completed, archived');
  });

  test('assembles structured prompt correctly', () => {
    const prompt = buildStructuredPrompt({
      systemRole: 'Extract metadata',
      task: 'Suggest frontmatter',
      sourceContent: 'Title: Test Project',
      constraints: ['area must be wikilink']
    });

    expect(prompt).toContain();
    (prompt).();
    (prompt).();
  });
});

---

Tips for Best Results

1. Always provide vault context - Helps AI suggest existing areas/projects instead of creating new ones
2. Use specific system roles - "Extract project metadata following PARA method" > "Extract metadata"
3. Test with different models - qwen2.5:7b is fast, qwen2.5:32b is more accurate
4. Validate AI output - Always check for required fields before creating notes
5. Provide examples in prompts - Include 1-2 examples of desired output format
6. Handle errors gracefully - LLM calls can fail; always have fallback to manual input

---

Reference

Source Files:

• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/llm/constraints.ts
• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/llm/prompt-builder.ts
• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/llm/orchestration.ts

Tests:

• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/llm/constraints.test.ts
• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/llm/prompt-builder.test.ts
• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/llm/orchestration.test.ts

Config:

• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/defaults.ts - Default frontmatter rules
• /Users/nathanvale/code/side-quest-marketplace/plugins/para-obsidian/src/config.ts - Config loading

---

Next Steps

1. Try the examples - Run code snippets in a test file
2. Build a custom command - Create a slash command using suggestFieldValues()
3. Experiment with prompts - Refine system roles and constraints for your use case
4. Add vault caching - Cache vault context to avoid repeated Dataview queries
5. Contribute patterns - Share successful AI integration patterns with the community