



























Our Commitment
Experts are being asked to hand more and more of their knowledge to tools they do not control. We think that trust has to be earned, not assumed. These are not terms of service. They are commitments from one developer to another.
Private boosters are never indexed, sold, surfaced to others, or used to train any model. What you save stays entirely in your account.
You can remove any booster, any version, or your entire account at any point. Deletion is permanent and immediate. There is no archive.
Pricing changes, policy updates, and platform decisions are announced in advance. We will not change the rules without giving you time to act.
We believe the em-dash is a valid — and useful — punctuation mark, and we will continue to use it regardless of what anyone else says.
Your Personal Library
Private boosters are yours alone. Capture your expertise from any session. AI Boost embeds it for instant retrieval. Your agent surfaces it the moment context matches.
01
Tell your agent what you want to save. It reads your current context, proposes metadata, and creates a private booster in seconds.
02
AI Boost processes the booster for keyword and semantic search so it can be retrieved accurately across all your future sessions.
03
When you start a task that matches a saved pattern, your agent surfaces the booster before it begins. You approve, it injects.
See it in action
Tell your agent what to save. It handles the rest — including reminding you next time.
Step 1 — Saving a pattern
Save our custom auth middleware pattern as a private booster
Thought for 2s
Let me read the current auth middleware file to extract the pattern.
Ran Read file in filesystem
Got it. Based on the file, here are my suggested booster details:
Name: Custom Auth Middleware Pattern
Description: JWT validation, role-based guards, request context injection
Tags:
auth
middleware
jwt
express
typescript
Visibility will be set to private. Confirm to save, or let me know what to change.
| Name | Custom Auth Middleware Pattern |
| Description | JWT validation, role-based guards, and request context injection for Express services |
| Tags | auth middleware jwt express typescript |
| Version | 1.0.0 |
| Visibility | Private |
| Price | 0 credits (personal use) |
Thought for 1s
Ran create_booster in ai-boost
| Booster ID | a3f2c901-7b44-4d1e-bc82-19e047f6a312 |
| Slug | custom-auth-middleware-pattern |
| Status |
Your Custom Auth Middleware Pattern booster has been saved to your private library under custom-auth-middleware-pattern. It is only visible to you and will be suggested automatically in future sessions.
Step 2 — Auto-suggested in a new session
AI Boost Agent — New session
Add protected routes to the new payments service
Thought for 2s
Before I start, let me check if there are any relevant boosters in your library.
Ran search_boosters in ai-boost
Found in your library
Custom Auth Middleware Pattern
auth
middleware
jwt
express
typescript
I found your saved auth middleware booster. Would you like me to inject it before I begin?
Thought for 1s
Ran get_booster in ai-boost
Booster injected into context
Your auth middleware conventions are loaded. I will follow your JWT validation and role-based guard patterns throughout this task.
The Problem
Memory systems capture history, but they cannot curate it. The expertise your agent needs (your conventions, your gotchas, lessons that took years to learn) has nowhere to live between sessions.
Patterns you use every day — your auth flow, your infra conventions, your API design rules — live in human memory. Your agent has no access to them.
Memory plugins record everything, which means they retrieve everything. The signal-to-noise ratio makes them unreliable for injecting the specific, structured knowledge your agent actually needs.
Domain experts have invaluable knowledge. There is currently no mechanism to package, distribute, and inject that expertise into an LLM at the moment it is needed.
How It Works
01
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 ready
→
02
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 passive
→
03
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 session
Get Connected
AI Boost is an MCP server. Add it to your agent client once and it will be available across every project automatically.
1
Open your Cursor MCP config
Create or edit .cursor/mcp.json in your home directory or project root.
2
Add the AI Boost server entry
{
"mcpServers": {
"ai-boost": {
"url": "https://mcp.ai-boost.io/mcp",
"transport": "streamable-http"
}
}
}3
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.
4
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.
See it in action
See it in action
Just tell the agent what you want to publish. It handles the rest.
I would like to publish this repo as a booster
Thought for 3s
I'll help you publish this as a booster on the AI Boost Marketplace. Let me start with the first step of the submission flow.
Answer
How would you like to submit the booster content?
Link to a public GitHub repository
Thought for 2s
Let me check the GitHub remote URL for this repo.
Ran Check git remote URL in git
origin git@github.com:AI-Boost-IO/stripe-payment-integration-guide.git (fetch)
origin git@github.com:AI-Boost-IO/stripe-payment-integration-guide.git (push)
Thought for 1s
The repo is AI-Boost-IO/stripe-payment-integration-guide. Let me fetch the GitHub API to auto-suggest details.
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)
Tags:
stripe
payments
python
webhook
subscription
billing
Now for the remaining details:
Answers
What price (in credits) would you like to set for this booster?
0 credits (free)
How should the booster be delivered?
Whole repository
Thought for 2s
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 |
| Price | 0 credits (free) |
Does everything look correct? Reply "confirmed" to submit, or let me know what to change.
Thought for 1s
Let me check the submission tool schema first.
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 |
The Stripe Payment Integration Guide booster is now live on the AI Boost Marketplace and discoverable under the slug stripe-payment-integration-guide.
Features
Every design decision optimises for two things: the agent gets the right context instantly, and the user stays in control.
Your agent calls the marketplace autonomously before starting any implementation task. No prompting required — it just works, like a reflex.
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.
Every booster passes automated LLM adversarial review and human approval before going live. Cryptographic signing ensures content integrity at inject time.
Share your expertise with the community. Your boosters surface proactively in relevant sessions across every member's workspace — knowledge that compounds over time.
Every booster is semantically versioned. Model compatibility tags tell you which LLMs the creator tested against. Pin to a version for reproducible results.
Publishing
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
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。