DiscordSign up
SKILLS.SH

agent-memory-systems

Memory architecture for agents: retrieval strategies that determine whether agents remember or forget. Covers five memory types: short-term (context window), long-term (vector stores), working memory, episodic memory, and semantic memory, each suited to different information patterns Emphasizes retrieval as the core challenge; provides chunking strategies, embedding quality guidance, and metadata filtering to surface the right memories at decision time Includes anti-patterns like storing everything forever and chunking without testing retrieval, plus sharp edges around contextual chunking, temporal scoring, and embedding model tracking Designed to integrate with autonomous agents, multi-agent orchestration, and agent tool builders

INSTALLATION
npx skills add https://github.com/davila7/claude-code-templates --skill agent-memory-systems
Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

Agent Memory Systems

You are a cognitive architect who understands that memory makes agents intelligent.

You've built memory systems for agents handling millions of interactions. You know

that the hard part isn't storing - it's retrieving the right memory at the right time.

Your core insight: Memory failures look like intelligence failures. When an agent

"forgets" or gives inconsistent answers, it's almost always a retrieval problem,

not a storage problem. You obsess over chunking strategies, embedding quality,

and

Capabilities

  • agent-memory
  • long-term-memory
  • short-term-memory
  • working-memory
  • episodic-memory
  • semantic-memory
  • procedural-memory
  • memory-retrieval
  • memory-formation
  • memory-decay

Patterns

Memory Type Architecture

Choosing the right memory type for different information

Vector Store Selection Pattern

Choosing the right vector database for your use case

Chunking Strategy Pattern

Breaking documents into retrievable chunks

Anti-Patterns

❌ Store Everything Forever

❌ Chunk Without Testing Retrieval

❌ Single Memory Type for All Data

⚠️ Sharp Edges

Issue

Severity

Solution

Issue

critical

Contextual Chunking (Anthropic's approach)

Issue

high

Test different sizes

Issue

high

Always filter by metadata first

Issue

high

Add temporal scoring

Issue

medium

Detect conflicts on storage

Issue

medium

Budget tokens for different memory types

Issue

medium

Track embedding model in metadata

Related Skills

Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder

BrowserAct

Let your agent run on any real-world website

Bypass CAPTCHA & anti-bot for free. Start local, scale to cloud.

Explore BrowserAct Skills →

Related skills

azure-ai
3/3

Access Azure AI Search, Speech, OpenAI, and Document Intelligence services through unified MCP tools. AI Search supports full-text, vector, hybrid, and semantic search with built-in AI enrichment for entity extraction and OCR Speech service handles real-time and batch speech-to-text transcription, text-to-speech synthesis with neural voices, and speaker diarization MCP tools provide direct access: azure__search for index queries and azure__speech for transcription and synthesis SDK references available for Python, TypeScript, .NET, and Java across all services; enable via /azure:setup or /mcp if MCP is not active

Verified authormicrosoft
SKILLS.SH
325K1.1K
2026-05-22
microsoft-foundry
2/3

End-to-end deployment, evaluation, and management of AI agents on Microsoft Foundry. Covers the complete agent lifecycle: creation from starter samples, containerization and ACR push, hosted or prompt agent deployment, invocation, batch evaluation, and prompt optimization Includes specialized sub-skills for deploy, invoke, observe (evaluation and prompt optimization), trace analysis, troubleshooting, and dataset curation from production traces Supports project and resource provisioning, RBAC management, quota tracking, and model deployment with intelligent routing across regions and SKUs Requires .foundry/agent-metadata.yaml as the source of truth for environment-specific configuration, datasets, and evaluation test cases

Verified authormicrosoft
SKILLS.SH
323K1.1K
2026-05-22
azure-aigateway
2/3

Configure Azure API Management as an AI Gateway for models, MCP tools, and agents with built-in governance policies. Supports semantic caching (60-80% cost savings), token rate limiting, content safety filtering, and jailbreak detection across AI backends Add Azure OpenAI, AI Foundry models, or convert existing APIs to MCP tools as managed backends with load balancing Includes five core policy categories: authentication, semantic cache lookup, token limits, content safety, and token metrics for observability Requires Azure CLI for configuration and testing; integrates with managed identity for secure backend access

Verified authormicrosoft
SKILLS.SH
310K1.1K
2026-05-22
azure-hosted-copilot-sdk
2/3

Build and deploy GitHub Copilot SDK applications to Azure with flexible model configuration. Three scaffolding paths: create new greenfield projects, add SDK services to existing repos, or deploy existing SDK apps with Azure infrastructure Supports three model configurations: GitHub's default models, specific GitHub models via discovery, or bring-your-own-model (BYOM) on Azure with DefaultAzureCredential authentication Includes complete templates with Express/TypeScript API, React/Vite frontend, Bicep infrastructure, Docker support, and token management scripts Deploy workflow uses azure-prepare, azure-validate, and azure-deploy steps; requires Docker and respects existing AGENTS.md configuration in user repos

Verified authormicrosoft
SKILLS.SH
277K1.1K
2026-05-22

Stop writing automation&scrapers

Install the CLI. Run your first Skill in 30 seconds. Scale when you're ready.

Start free
free · no credit card