Best Agent Setup and Memory Plugins, Skills, and MCP Servers in 2026
Research-backed setup and memory plugins, skills, and MCP servers with platform availability and direct setup routes.
Updated July 25, 2026
These extensions shape the agent itself - how it is configured, the skills it can find and build, how it behaves, and what it remembers between sessions. The real decision is rarely adding another connection; it is whether you want persistent behavior and continuity, and what you will let the agent store.
Find Skills teaches an agent to search the open skill ecosystem and pull in relevant packages on demand. It rides on the shared Skills CLI and skills.sh catalog rather than being the catalog itself, acting as the discovery layer.
Reach for it when a user needs a capability that likely already exists as a skill. It ranks candidates by popularity and publisher signal, but discovery is not a safety check. Every package it surfaces carries its own code and permissions, so review before installing.
Grill Me makes an agent investigate the discoverable facts itself, then ask you one decision question at a time until a plan or idea is genuinely understood. The visible command is a thin wrapper around the collection’s reusable Grilling skill.
Reach for it on non-code plans, product decisions, or genuinely ambiguous work where a wrong assumption is expensive, and it keeps you in control of the real decisions. Skip it for clear, reversible tasks; for codebase design the publisher points to Grill with Docs, which also preserves domain language.
Caveman forces a coding agent into deliberately terse replies, stripping the conversational padding while leaving code, commands, and error output intact. The logic lives entirely in the extension, with no external service behind it.
It earns its place when an agent’s explanations are consistently longer than you need, cutting reading time and trimming output tokens. The trade-off is blunt: it is a poor fit for teaching, stakeholder-facing writing, or any work where visible reasoning is the point.
Skill Creator is a toolkit for building and refining Claude skills across four modes: Create, Eval, Improve, and Benchmark. Instead of writing a skill and hoping it works, you get structured stages for drafting, testing, and measuring it.
The payoff is measurement: graders, blind comparisons, validation, and benchmark reports let you see whether a change actually improved a skill rather than guessing. Worth knowing that repeated evaluation runs consume real model usage and execute local scripts, so heavy iteration has a cost.
CLAUDE.md Management is a project-memory workflow with two moves. An audit skill checks your CLAUDE.md files against the current codebase, and a session command turns durable learnings from a session into proposed diffs you review before they land.
It helps when project instructions drift after code changes, or when a session surfaces a durable command or gotcha worth keeping. It aims to keep instructions useful rather than bloated. Treat its scores and diffs as prompts for judgment, and keep secrets, ephemeral state, and verbose summaries out.
A community meta-skill from Matt Pocock for designing, writing, and improving agent skills - a repeatable method for authoring skill instructions and judging whether one is clear and genuinely useful.
When you are creating or revising a skill and want more than a blank file. Despite the name it operates on the skill itself, not prose; it pairs with Skill Creator, which adds the evaluation and benchmarking loop.
Claude Code Setup is a read-only recommender that scans your repository and suggests a short list of automations to add: MCP servers, skills, hooks, subagents, and slash commands. It reads the project but changes nothing on its own.
It helps most when the extension ecosystem feels overwhelming, because it narrows the field to a shortlist tied to your project’s structure, dependencies, and patterns rather than a generic popularity list. Since it only suggests, verify the source, maintenance, permissions, and compatibility of anything third-party before you add it.
claude-mem is a memory layer that watches an agent session, summarizes what happened through an AI model, and files it in local SQLite and vector indexes. Later sessions can pull back the relevant pieces. The MCP search tools are one part of this larger system.
It shines on long projects where sessions keep losing decisions, and it retrieves history progressively instead of dumping everything into context. The catch: it records substantial session activity and sends it to your chosen model, so vet retention, privacy exclusions, and cloud sync before pointing it at sensitive repositories.
Graphiti gives an agent a temporal knowledge graph for long-term memory - episodes, entities, relationships, and facts that change over time - queried with hybrid, time-aware search. The agent route is Zep’s official but explicitly experimental MCP server, running on the broader Graphiti library and a graph database.
When flat memory notes are not enough and you want provenance and relationships an agent can reason over, and your team can operate the infrastructure. It is the most capable memory option here, and the most demanding to run and secure.
Mem0 is a persistent memory layer for agents and AI applications, reachable through a hosted MCP server, plugins with lifecycle hooks, portable skills, SDKs, or a self-hostable open-source library. Agents can explicitly add, search, update, and delete memories.
The simple path gives an agent direct memory commands; the plugin route goes further, pulling context at task start and capturing learnings before they scroll away. The hosted free tier allows 10,000 additions and 1,000 retrievals monthly. Because automatic capture can store sensitive context, set retention and access rules first.
A community continuity plugin for Claude Code that extracts and compresses each session into persistent daily memory and handoff files, so project context survives across separate sessions without a hosted service.
When you want local, file-based continuity rather than sending session activity to a cloud memory provider. It overlaps with claude-mem and Mem0 but keeps everything on disk - review what its hooks persist.
A first-party Claude behavior plugin that changes how Claude works a coding task: it explains the reasoning behind implementation choices and pauses at decision points for you to write small, meaningful pieces of the code yourself.
When understanding and practice matter more than the fastest possible completion. The trade-off is direct: the added explanations and hand-offs cost tokens and time, so it is a poor fit for routine production work or urgent fixes.
A community skill that has the agent interview you one focused question at a time about an ambiguous task, continuing until it is roughly 95% confident, then restating the work as a clear specification before starting.
Before complex or high-stakes work where requirements are incomplete and a wrong assumption is expensive. For simple tasks it is overkill - and note the individual skill is the install to want, not the 24-skill collection it ships in.
Trail of Bits’ focused behavior plugin: the agent spots material ambiguity, asks a small set of must-have questions - roughly one to five - and pauses until they are answered or you approve reasonable assumptions.
When a few unanswered requirements could change the implementation but you do not want a full discovery interview. It is the lighter, more bounded cousin of Interview Me; skip it for routine tasks or when you have already authorized assumptions.
