Stop spending tokens re-explaining the same patterns.
Save a pattern once. Your agent recalls it before the next task — no re-prompting, no wasted tokens.
Works with
Claude Code
Cursor
Any MCP Client
Saving to Your Library
It'll be recalled automatically next time it's relevant.
Extracting the reusable pattern from this conversation
Saving privately to My Library
Ready to recall automatically next time
Every session starts blank
Ask anyone shipping real work with AI agents and you will hear the same complaint: the agent gets a convention right once, then forgets it exists by the next session. Multiply that across a team and the same rules, gotchas, and fixes get re-explained constantly — this is a known pain across the agent-building ecosystem, not a one-off annoyance.
Every session starts from scratch
Your auth flow, your infra conventions, your API design rules — the agent gets told once, then forgets by the next chat. You end up typing the same instructions over and over.
Memory plugins record everything, which helps nothing
Generic memory retrieves whatever is topically close, not what is actually relevant. The signal-to-noise ratio makes it unreliable for injecting the specific, structured knowledge a task needs.
Expertise stays stuck in one person's head
One engineer solves the tricky auth bug. Three weeks later, someone else on the team hits the same bug and solves it again from zero — because there was never a way to hand that knowledge to their agent.
Your expertise. Captured once. Available everywhere.
We call the unit of captured knowledge a booster — structured, shareable context your agent can inject the moment a task matches it.
Private boosters are yours alone. Capture your expertise from any session and AI Boost embeds it for instant retrieval — your agent surfaces it automatically the next time context matches.
01
Capture any expertise
Tell your agent what you want to save. It reads your current context, proposes metadata, and creates a private booster in seconds. Alternatively, you can manually curate a booster as a text file or a whole git repository complete with README, documentation, and sample code.
02
Indexed and embedded
AI Boost processes the booster for keyword and semantic search so it can be retrieved accurately across all your future sessions.
03
Suggested automatically
When you start a task that matches a saved pattern, your agent surfaces the booster before it begins. You approve, it injects.
How a booster works
01
Save
Tell your agent about a convention, pattern, or workflow. AI Boost packages it as a booster and indexes it using keywords and semantic embeddings.
Private by default; publish when you are ready02
Suggest
On your next session, your agent queries AI Boost automatically before starting a task. If a relevant booster exists (yours or from the community) it is surfaced for your approval.
Proactive, not passive03
Inject
You approve. The full context payload flows directly into the agent's context window. Conventions, patterns, examples: available immediately. No copy-paste, no setup.
Re-injectable at any point in a sessionConnect in minutes
AI Boost is an MCP server. Add it to your agent client once and it will be available across every project automatically.
Open your Cursor MCP config
Create or edit .cursor/mcp.json in your home directory or project root.
Add the AI Boost server entry
{
"mcpServers": {
"ai-boost": {
"url": "https://mcp.ai-boost.io/mcp",
"transport": "streamable-http"
}
}
}Authenticate with GitHub OAuth
On first use Cursor will open a browser tab. Sign in with GitHub, grant access, and you will be redirected back automatically.
Top up credits and start using boosters
Visit app.ai-boost.io/billing to add credits, then ask Cursor to search for a booster relevant to your task.
Watch the full flow
Three messages to go live
Just tell the agent what you want to publish. It handles the rest.
I would like to publish this as a free public booster
I'll help you publish this as a free public booster. Visibility and price are already set — public means listed in the marketplace; your GitHub repo can stay private.
One question before I proceed:
Link to a GitHub repository
Let me look up the repo to auto-suggest metadata.
Ran Lookup GitHub Repo in ai-boost
Based on the GitHub repo, here are my suggestions — let me know if you'd like to change any of them:
Name: Stripe Payment Integration Guide
Description: A quick-setup guide for Stripe payments
Version: 1.0.0 (no releases found, defaulting)
Branch:
mainTags:
Visibility is public, price is Free — both confirmed from your message. One last question:
Whole repository
Here's a full summary of the booster submission — please confirm before I proceed:
| Name | Stripe Payment Integration Guide |
| Description | A quick-setup guide for Stripe payments |
| Tags | stripe payments python webhook subscription billing |
| Version | 1.0.0 |
| Branch | main |
| Repo URL | github.com/AI-Boost-IO/stripe-payment-integration-guide |
| Delivery | Whole repository |
| Visibility | Public (marketplace listing) |
| Price | Free — pre-confirmed |
Does everything look correct? Reply "confirmed" to submit, or let me know what to change.
Confirmed
Ran Submit Booster From Repo in ai-boost
The booster was submitted and approved. Here's the summary:
| Booster ID | fd904501-cda7-4a9f-aca9-34f539b5fc5a |
| Version ID | ff6135a2-0432-4bca-8fc1-232c9f05d0d5 |
| Slug | stripe-payment-integration-guide |
| Status | Approved |
The Stripe Payment Integration Guide booster is now live on the AI Boost Marketplace and discoverable under the slug stripe-payment-integration-guide.
You are not starting from zero
Every booster below was written by a developer solving a real problem, then passed through automated review and human approval before going live. When your agent surfaces one, it is not guessing — it is drawing on expertise the ecosystem has already vetted.
Developers are increasingly surrounded by AI-generated knowledge. Boosters backed by real human experience are a different category entirely — and the library grows every time someone shares what took them years to learn.
The bar every public booster clears
01
Written by a developer who actually shipped it — not generated boilerplate.
02
Passes automated LLM adversarial review and human approval before it goes live.
03
Indexed and surfaced automatically — your agent finds it, you do not have to search.
04
Creators earn credits for every injection, so the best Boosters stay maintained over time.
What the community is using
Django on AWS EC2 with Terraform
A setup guide for deploying a Dockerised Django app on AWS EC2 using Terraform-managed infrastructure (VPC, EC2, S3, IAM, EIP).
Django Channels + Celery + Next.js + Terraform/AWS
Full-stack skeleton documenting the canonical patterns for a monorepo SaaS backend: Django 5.2 + Strawberry GraphQL + Django Channels 4 + Celery + Next.js 16 + Material UI v9 + Apollo Client 4 + Terraform on AWS EC2. Covers ASGI routing, WebSocket subscriptions with Redis pub/sub, Celery workers and Beat scheduler, Next.js App Router with RSC and Client Components, MUI v9 theming, GraphQL codegen, Docker Compose dev/prod split, and Vercel deployment for the frontend.
Your library.
Your community.
Your call.
Private boosters are only ever visible to you. When you decide to share, they become available to the whole community, and you earn credits for every injection.
You have knowledge that took years to accumulate. AI Boost lets you package it as a structured context booster without any ongoing effort on your part.
Your library is private by default — share individual boosters on your own terms
Submit any structured knowledge: conventions, patterns, guides, annotated snippets
Automated LLM review and human approval keeps quality high and fraud low
Version your boosters freely; diff-focused review makes updates fast
Your booster surfaces proactively in every relevant agent session across the community
Earn credits every time a developer injects your booster — per-injection, ongoing
Your booster becomes part of a growing knowledge commons used by developers worldwide
Built for serious agent workflows
Every design decision optimises for two things: the agent gets the right context instantly, and the user stays in control.
Proactive Discovery
Your agent calls the marketplace autonomously before starting any implementation task. No prompting required — it just works, like a reflex.
Semantic + Keyword Search
Boosters are ranked by relevance using embeddings and full-text search. The right booster surfaces based on what your agent is actually trying to do.
Review Pipeline + Signing
Every booster passes automated LLM adversarial review and human approval before going live. Cryptographic signing ensures content integrity at inject time.
Open Knowledge Commons
Every booster you publish keeps working for the whole community, and every credit you earn compounds that motivation over time.
Versioning & Model Tags
Every booster is semantically versioned. Model compatibility tags tell you which LLMs the creator tested against. Pin to a version for reproducible results.