Dive-Memory v2 - AI Persistent Project Memory

Skill Type

Base Skill (Always Active)

Purpose

Dive-Memory v2 provides persistent project memory for AI agents, solving the critical problem of context loss and skill amnesia. This skill automatically injects project context, behavioral rules, and skill triggers into every AI interaction.

When to Use

Always Active - This skill runs automatically for every project that has a PROJECT.memory.md file.

Core Capabilities

1. Context Injection

Automatically prepends project constitution to every AI prompt:

•Project goal and objectives
•AI persona and behavioral guidelines
•Always-on skills that should be considered
•Behavioral rules that MUST be followed
•Current dynamic state (task, file, environment)

2. Skill Auto-Discovery

Intent-based skill triggering system:

•Matches user prompts against configured intent patterns
•Suggests relevant skills automatically
•Provides contextual prompts for skill usage

3. Knowledge Management

File-based knowledge store:

•.known.md files for research and decisions
•Semantic search with vector embeddings
•Automatic knowledge retrieval based on context

4. Task Management

Track work items with acceptance criteria:

•Task status and priority
•Acceptance criteria checklists
•Links to related knowledge documents

File Structure

code

project-root/
├── PROJECT.memory.md          # Behavioral memory (YAML + Markdown)
├── knowns/                    # Knowledge documents
│   ├── auth-strategy.known.md
│   ├── api-design.known.md
│   └── ...
└── tasks/                     # Task documents
    ├── implement-auth.md
    └── ...

PROJECT.memory.md Format

yaml

---
project_goal: "Build a comprehensive e-commerce platform"
persona: "You are a senior full-stack engineer"
always_on_skills:
  - name: "code_reviewer"
    description: "Reviews code for best practices"
behavioral_rules:
  - "ALWAYS write tests before code"
  - "BEFORE implementing, check if .known.md exists"
skill_triggers:
  - intent:
      - "create api"
      - "build endpoint"
    skill_to_suggest: "api_generator"
    suggestion_prompt: "Use API generator for this task"
dynamic_state:
  last_command: "npm test"
  current_task: "#123"
---

Additional project notes...

Usage Instructions

For AI Agents

When you receive a user prompt:

•Check for PROJECT.memory.md in the current project directory
•If found, read and parse it using the memory parser
•Generate meta-prompt with project constitution
•Match skill triggers against the user prompt
•Inject context by prepending meta-prompt to user request
•Follow behavioral rules strictly (MUST/NEVER statements)
•Consider always-on skills for every response
•Update dynamic state after completing actions

Example Workflow

User Prompt:

code

I need to implement user authentication

After Context Injection:

code

[BEGIN PROJECT CONSTITUTION]

Project Goal: Build a comprehensive e-commerce platform

AI Persona: You are a senior full-stack engineer

Available Skills (Always Consider):
  - code_reviewer: Reviews code for best practices
  - security_auditor: Checks for security vulnerabilities

Behavioral Rules (MUST Follow):
  1. ALWAYS write tests before code
  2. BEFORE implementing, check if .known.md exists

Current Context:
  - last_command: npm test
  - current_task: #123

[END PROJECT CONSTITUTION]

[Skill Suggestions]
Based on your request about "authentication", consider using:
  - security_auditor: Run security audit for auth implementation

[BEGIN USER REQUEST]
I need to implement user authentication
[END USER REQUEST]

API Reference

Memory Parser

typescript

import { parseMemoryFile, generateMetaPrompt, matchSkillTriggers } from "./memoryParser";

// Parse PROJECT.memory.md
const { memory, markdownBody } = parseMemoryFile(fileContent);

// Generate meta-prompt for context injection
const metaPrompt = generateMetaPrompt(memory);

// Match skill triggers
const matches = matchSkillTriggers(userPrompt, memory.skillTriggers);

Context Injection

typescript

import { injectContext } from "./contextInjection";

// Inject context into user prompt
const result = injectContext(userPrompt, projectMemory);

console.log(result.finalPrompt);      // Full prompt with context
console.log(result.matchedTriggers);  // Matched skill triggers
console.log(result.suggestions);      // Skill suggestions

Knowledge Store

typescript

import { parseKnownFile } from "./memoryParser";

// Parse .known.md file
const { metadata, content } = parseKnownFile(fileContent);

// Semantic search
import { semanticSearch } from "./vectorSearch";
const results = await semanticSearch(projectId, "authentication best practices", 5);

Integration Points

With Dive AI V20 Orchestrator

python

from v20.coder.orchestrator.orchestrator import Orchestrator
from v20.skills.internal.dive_memory_v2 import DiveMemorySkill

# Initialize orchestrator with Dive-Memory
orchestrator = Orchestrator()
memory_skill = DiveMemorySkill()

# Inject context before every AI call
def process_prompt(user_prompt, project_path):
    # Read PROJECT.memory.md
    memory = memory_skill.load_memory(project_path)
    
    # Inject context
    full_prompt = memory_skill.inject_context(user_prompt, memory)
    
    # Send to AI
    response = orchestrator.execute(full_prompt)
    
    # Update dynamic state
    memory_skill.update_state(project_path, {
        "last_command": get_last_command(),
        "last_file": get_last_file()
    })
    
    return response

With Skill Registry

yaml

# v20/runtime/skills_registry.yml
skills:
  - name: dive-memory-v2
    type: base
    always_active: true
    priority: 1  # Highest priority - runs first
    location: v20/skills/internal/dive-memory-v2
    entry_point: skill.py
    capabilities:
      - context_injection
      - knowledge_management
      - skill_discovery

Behavioral Rules for AI

When this skill is active, you MUST:

•ALWAYS check for PROJECT.memory.md before starting any task
•ALWAYS inject context into every user prompt
•ALWAYS follow behavioral rules defined in PROJECT.memory.md
•BEFORE implementing, check if relevant .known.md exists
•AFTER completing a task, update dynamic_state in PROJECT.memory.md
•WHEN skill triggers match, suggest the appropriate skill
•NEVER skip context injection even if the prompt seems simple

Configuration

Enable for a Project

bash

cd your-project
dive-mem init

This creates:

•PROJECT.memory.md with default configuration
•knowns/ directory for knowledge documents
•tasks/ directory for task management

Customize Behavior

Edit PROJECT.memory.md:

yaml

---
project_goal: "Your project goal"
persona: "Your preferred AI persona"
always_on_skills:
  - name: "your_skill"
    description: "Description"
behavioral_rules:
  - "Your custom rules"
skill_triggers:
  - intent: ["keywords"]
    skill_to_suggest: "skill_name"
---

Testing

Test Context Injection

bash

dive-mem context inject "Your test prompt"

Test Skill Matching

typescript

import { matchSkillTriggers } from "./memoryParser";

const triggers = [
  {
    intent: ["create api", "build endpoint"],
    skill: "api_generator",
  },
];

const matches = matchSkillTriggers("I need to create a new API", triggers);
console.log(matches); // Should match api_generator

Troubleshooting

Context Not Injecting

•Verify PROJECT.memory.md exists
•Check YAML syntax is valid
•Ensure memory parser is imported correctly
•Verify project_goal field is present

Skills Not Triggering

•Check skill_triggers configuration
•Verify intent keywords match user prompt
•Test with matchSkillTriggers() function
•Add more intent variations

Knowledge Not Found

•Verify .known.md files exist in knowns/ directory
•Check file naming follows convention
•Ensure files have YAML front-matter
•Test semantic search with vector store

Best Practices

•Document First, Code Second - Always create .known.md before implementing
•Use Behavioral Rules - Define clear MUST/NEVER rules
•Link Tasks to Knowledge - Reference @knowns/file.known.md in tasks
•Update Dynamic State - Keep AI informed of current context
•Use Skill Triggers - Add triggers for common patterns
•Keep Memory Concise - Focus on essential rules and context

Example Projects

See /home/ubuntu/dive-ai-local for a complete implementation with:

•Web dashboard for memory management
•CLI tool (dive-mem) for local development
•Full tRPC API
•Vector search integration
•Task management system

Dependencies

•yaml - YAML parsing
•chromadb - Vector search (optional)
•mysql2 / drizzle-orm - Metadata storage

License

MIT

Support

For issues or questions:

•GitHub: [repository-url]
•Documentation: See DIVE_MEMORY_V2_GUIDE.md