Conversation Memory V2

Conversation Memory

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/conversation-memory from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Conversation Memory Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Prerequisites, Scope, Ecosystem, Patterns, User Context.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• User mentions or implies: conversation memory
• User mentions or implies: remember
• User mentions or implies: memory persistence
• User mentions or implies: long-term memory
• User mentions or implies: chat history
• Use when the request clearly matches the imported source intent: Persistent memory systems for LLM conversations including.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Capabilities

• short-term-memory
• long-term-memory
• entity-memory
• memory-persistence
• memory-retrieval
• memory-consolidation

Examples

Example 1: Ask for the upstream workflow directly

Use @conversation-memory-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @conversation-memory-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @conversation-memory-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @conversation-memory-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/conversation-memory, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Prerequisites

• Knowledge: LLM conversation patterns, Database basics, Key-value stores
• Skills_recommended: context-window-management, rag-implementation

Imported: Scope

• Does_not_cover: Knowledge graph construction, Semantic search implementation, Database administration
• Boundaries: Focus is memory patterns for LLMs, Covers storage and retrieval strategies

Imported: Ecosystem

Primary_tools

• Mem0 - Memory layer for AI applications
• LangChain Memory - Memory utilities in LangChain
• Redis - In-memory data store for session memory

Imported: Patterns

Tiered Memory System

Different memory tiers for different purposes

When to use: Building any conversational AI

interface MemorySystem { // Buffer: Current conversation (in context) buffer: ConversationBuffer;

// Short-term: Recent interactions (session)
shortTerm: ShortTermMemory;

// Long-term: Persistent across sessions
longTerm: LongTermMemory;

// Entity: Facts about people, places, things
entity: EntityMemory;

}

class TieredMemory implements MemorySystem { async addMessage(message: Message): Promise { // Always add to buffer this.buffer.add(message);

    // Extract entities
    const entities = await extractEntities(message);
    for (const entity of entities) {
        await this.entity.upsert(entity);
    }

    // Check for memorable content
    if (await isMemoryWorthy(message)) {
        await this.shortTerm.add({
            content: message.content,
            timestamp: Date.now(),
            importance: await scoreImportance(message)
        });
    }
}

async consolidate(): Promise<void> {
    // Move important short-term to long-term
    const memories = await this.shortTerm.getOld(24 * 60 * 60 * 1000);
    for (const memory of memories) {
        if (memory.importance > 0.7 || memory.referenced > 2) {
            await this.longTerm.add(memory);
        }
        await this.shortTerm.remove(memory.id);
    }
}

async buildContext(query: string): Promise<string> {
    const parts: string[] = [];

    // Relevant long-term memories
    const longTermRelevant = await this.longTerm.search(query, 3);
    if (longTermRelevant.length) {
        parts.push('## Relevant Memories\n' +
            longTermRelevant.map(m => `- ${m.content}`).join('\n'));
    }

    // Relevant entities
    const entities = await this.entity.getRelevant(query);
    if (entities.length) {
        parts.push('## Known Entities\n' +
            entities.map(e => `- ${e.name}: ${e.facts.join(', ')}`).join('\n'));
    }

    // Recent conversation
    const recent = this.buffer.getRecent(10);
    parts.push('## Recent Conversation\n' + formatMessages(recent));

    return parts.join('\n\n');
}

}

Entity Memory

Store and update facts about entities

When to use: Need to remember details about people, places, things

interface Entity { id: string; name: string; type: 'person' | 'place' | 'thing' | 'concept'; facts: Fact[]; lastMentioned: number; mentionCount: number; }

interface Fact { content: string; confidence: number; source: string; // Which message this came from timestamp: number; }

class EntityMemory { async extractAndStore(message: Message): Promise { // Use LLM to extract entities and facts const extraction = await llm.complete(` Extract entities and facts from this message. Return JSON: { "entities": [ { "name": "...", "type": "...", "facts": ["..."] } ]}

        Message: "${message.content}"
    `);

    const { entities } = JSON.parse(extraction);
    for (const entity of entities) {
        await this.upsert(entity, message.id);
    }
}

async upsert(entity: ExtractedEntity, sourceId: string): Promise<void> {
    const existing = await this.store.get(entity.name.toLowerCase());

    if (existing) {
        // Merge facts, avoiding duplicates
        for (const fact of entity.facts) {
            if (!this.hasSimilarFact(existing.facts, fact)) {
                existing.facts.push({
                    content: fact,
                    confidence: 0.9,
                    source: sourceId,
                    timestamp: Date.now()
                });
            }
        }
        existing.lastMentioned = Date.now();
        existing.mentionCount++;
        await this.store.set(existing.id, existing);
    } else {
        // Create new entity
        await this.store.set(entity.name.toLowerCase(), {
            id: generateId(),
            name: entity.name,
            type: entity.type,
            facts: entity.facts.map(f => ({
                content: f,
                confidence: 0.9,
                source: sourceId,
                timestamp: Date.now()
            })),
            lastMentioned: Date.now(),
            mentionCount: 1
        });
    }
}

}

Memory-Aware Prompting

Include relevant memories in prompts

When to use: Making LLM calls with memory context

async function promptWithMemory( query: string, memory: MemorySystem, systemPrompt: string ): Promise { // Retrieve relevant memories const relevantMemories = await memory.longTerm.search(query, 5); const entities = await memory.entity.getRelevant(query); const recentContext = memory.buffer.getRecent(5);

// Build memory-augmented prompt
const prompt = `

${systemPrompt}

Imported: User Context

${entities.length ? Known about user:\n${entities.map(e => - ${e.name}: ${e.facts.map(f => f.content).join('; ')} ).join('\n')} : ''}

${relevantMemories.length ? Relevant past interactions:\n${relevantMemories.map(m => - [${formatDate(m.timestamp)}] ${m.content} ).join('\n')} : ''}

Imported: Recent Conversation

${formatMessages(recentContext)}

Imported: Current Query

${query} `.trim();

const response = await llm.complete(prompt);

// Extract any new memories from response
await memory.addMessage({ role: 'assistant', content: response });

return response;

}

Imported: Sharp Edges

Memory store grows unbounded, system slows

Severity: HIGH

Situation: System slows over time, costs increase

Symptoms:

• Slow memory retrieval
• High storage costs
• Increasing latency over time

Why this breaks: Every message stored as memory. No cleanup or consolidation. Retrieval over millions of items.

Recommended fix:

// Implement memory lifecycle management

class ManagedMemory { // Limits private readonly SHORT_TERM_MAX = 100; private readonly LONG_TERM_MAX = 10000; private readonly CONSOLIDATION_INTERVAL = 24 * 60 * 60 * 1000;

async add(memory: Memory): Promise<void> {
    // Score importance before storing
    const score = await this.scoreImportance(memory);
    if (score < 0.3) return;  // Don't store low-importance

    memory.importance = score;
    await this.shortTerm.add(memory);

    // Check limits
    await this.enforceShortTermLimit();
}

async enforceShortTermLimit(): Promise<void> {
    const count = await this.shortTerm.count();
    if (count > this.SHORT_TERM_MAX) {
        // Consolidate: move important to long-term, delete rest
        const memories = await this.shortTerm.getAll();
        memories.sort((a, b) => b.importance - a.importance);

        const toKeep = memories.slice(0, this.SHORT_TERM_MAX * 0.7);
        const toConsolidate = memories.slice(this.SHORT_TERM_MAX * 0.7);

        for (const m of toConsolidate) {
            if (m.importance > 0.7) {
                await this.longTerm.add(m);
            }
            await this.shortTerm.remove(m.id);
        }
    }
}

async scoreImportance(memory: Memory): Promise<number> {
    const factors = {
        hasUserPreference: /prefer|like|don't like|hate|love/i.test(memory.content) ? 0.3 : 0,
        hasDecision: /decided|chose|will do|won't do/i.test(memory.content) ? 0.3 : 0,
        hasFactAboutUser: /my|I am|I have|I work/i.test(memory.content) ? 0.2 : 0,
        length: memory.content.length > 100 ? 0.1 : 0,
        userMessage: memory.role === 'user' ? 0.1 : 0,
    };

    return Object.values(factors).reduce((a, b) => a + b, 0);
}

}

Retrieved memories not relevant to current query

Severity: HIGH

Situation: Memories included in context but don't help

Symptoms:

• Memories in context seem random
• User asks about things already in memory
• Confusion from irrelevant context

Why this breaks: Simple keyword matching. No relevance scoring. Including all retrieved memories.

Recommended fix:

// Intelligent memory retrieval

async function retrieveRelevant( query: string, memories: MemoryStore, maxResults: number = 5 ): Promise<Memory[]> { // 1. Semantic search const candidates = await memories.semanticSearch(query, maxResults * 3);

// 2. Score relevance with context
const scored = await Promise.all(candidates.map(async (m) => {
    const relevanceScore = await llm.complete(`
        Rate 0-1 how relevant this memory is to the query.
        Query: "${query}"
        Memory: "${m.content}"
        Return just the number.
    `);
    return { ...m, relevance: parseFloat(relevanceScore) };
}));

// 3. Filter low relevance
const relevant = scored.filter(m => m.relevance > 0.5);

// 4. Sort and limit
return relevant
    .sort((a, b) => b.relevance - a.relevance)
    .slice(0, maxResults);

}

Memories from one user accessible to another

Severity: CRITICAL

Situation: User sees information from another user's sessions

Symptoms:

• User sees other user's information
• Privacy complaints
• Compliance violations

Why this breaks: No user isolation in memory store. Shared memory namespace. Cross-user retrieval.

Recommended fix:

// Strict user isolation in memory

class IsolatedMemory { private getKey(userId: string, memoryId: string): string { // Namespace all keys by user return user:${userId}:memory:${memoryId}; }

async add(userId: string, memory: Memory): Promise<void> {
    // Validate userId is authenticated
    if (!isValidUserId(userId)) {
        throw new Error('Invalid user ID');
    }

    const key = this.getKey(userId, memory.id);
    memory.userId = userId;  // Tag with user
    await this.store.set(key, memory);
}

async search(userId: string, query: string): Promise<Memory[]> {
    // CRITICAL: Filter by user in query
    return await this.store.search({
        query,
        filter: { userId: userId },  // Mandatory filter
        limit: 10
    });
}

async delete(userId: string, memoryId: string): Promise<void> {
    const memory = await this.get(userId, memoryId);
    // Verify ownership before delete
    if (memory.userId !== userId) {
        throw new Error('Access denied');
    }
    await this.store.delete(this.getKey(userId, memoryId));
}

// User data export (GDPR compliance)
async exportUserData(userId: string): Promise<Memory[]> {
    return await this.store.getAll({ userId });
}

// User data deletion (GDPR compliance)
async deleteUserData(userId: string): Promise<void> {
    const memories = await this.exportUserData(userId);
    for (const m of memories) {
        await this.store.delete(this.getKey(userId, m.id));
    }
}

}

Imported: Validation Checks

No User Isolation in Memory

Severity: CRITICAL

Message: Memory operations without user isolation. Privacy vulnerability.

Fix action: Add userId to all memory operations, filter by user on retrieval

No Importance Filtering

Severity: WARNING

Message: Storing memories without importance filtering. May cause memory explosion.

Fix action: Score importance before storing, filter low-importance content

Memory Storage Without Retrieval

Severity: WARNING

Message: Storing memories but no retrieval logic. Memories won't be used.

Fix action: Implement memory retrieval and include in prompts

No Memory Cleanup

Severity: INFO

Message: No memory cleanup mechanism. Storage will grow unbounded.

Fix action: Implement consolidation and cleanup based on age/importance

Imported: Collaboration

Delegation Triggers

• context window|token -> context-window-management (Need context optimization)
• rag|retrieval|vector -> rag-implementation (Need retrieval system)
• cache|caching -> prompt-caching (Need caching strategies)

Complete Memory System

Skills: conversation-memory, context-window-management, rag-implementation

Workflow:

1. Design memory tiers
2. Implement storage and retrieval
3. Integrate with context management
4. Add consolidation and cleanup

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.