Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/spring-ai-community/spring-ai-agentcore/agents-mdgit clone --depth 1 https://github.com/spring-ai-community/spring-ai-agentcoreWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/spring-ai-community/spring-ai-agentcore/agents-md)<a href="https://agentmods.dev/instructions/spring-ai-community/spring-ai-agentcore/agents-md"><img src="https://agentmods.dev/badge/instructions/spring-ai-community/spring-ai-agentcore/agents-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01370 | $0.01370 |
| Opus 5 | $0.00685 | $0.00685 |
| Sonnet 5 | $0.00274 | $0.00274 |
| Haiku 4.5 | $0.00137 | $0.00137 |
Grade A, and why
spring-ai-agentcore AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides context for AI coding assistants working on this project.
Project Overview
Spring Boot starter that enables Spring Boot applications to conform to the Amazon AgentCore Runtime contract. Provides auto-configuration for AgentCore endpoints and Spring AI integration with AgentCore Memory service.
Architecture
spring-ai-agentcore/
├── spring-ai-agentcore-artifact-store/ # Shared artifact storage
├── spring-ai-agentcore-runtime-starter/ # Runtime starter (invocations, ping, SSE)
├── spring-ai-agentcore-memory/ # Memory integration (STM + LTM)
├── spring-ai-agentcore-browser/ # Browser automation tools
├── spring-ai-agentcore-code-interpreter/ # Code interpreter tools
├── examples/ # Working examples
└── scripts/ # Helper scripts
Key Components
| Module | Purpose | Entry Point |
|---|---|---|
artifact-store |
Session-scoped artifact storage | ArtifactStore.java, CaffeineArtifactStore.java |
starter |
AgentCore Runtime contract | AgentCoreAutoConfiguration.java |
memory |
Spring AI ChatMemory integration | AgentCoreShortTermMemoryRepositoryAutoConfiguration.java, AgentCoreLongTermMemoryAutoConfiguration.java |
browser |
Browser automation tools | AgentCoreBrowserAutoConfiguration.java |
codeinterpreter |
Code execution tools | AgentCoreCodeInterpreterAutoConfiguration.java |
Artifact Store Classes
| Class | Purpose |
|---|---|
ArtifactStore<T> |
Interface for session-scoped artifact storage with optional category support |
ArtifactStoreFactory<T> |
Factory interface for creating artifact stores |
CaffeineArtifactStore<T> |
Caffeine-backed implementation with TTL |
CaffeineArtifactStoreFactory<T> |
Factory for creating CaffeineArtifactStore instances |
GeneratedFile |
Immutable artifact record (mimeType, data, name, metadata) |
ArtifactMetadata |
Utility for metadata extraction |
SessionConstants |
Session ID constants |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 135 lines · 1,370 tokens per session scan A 9abab25b115a
spring-ai-agentcore AGENTS.md is an instructions file published in the GitHub repository spring-ai-community/spring-ai-agentcore (59 stars, last pushed yesterday), licensed Apache-2.0. It adds 1,370 tokens to every session, about $0.0068 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
sample-autonomous-cloud-coding-agents AGENTS.md
AGENTS.md instructions for aws-samples/sample-autonomous-cloud-coding-agents, covering agents.md, your role, commands (run these), git workflow and where to make changes.
sample-autonomous-cloud-coding-agents CLAUDE.md
Claude Code instructions for aws-samples/sample-autonomous-cloud-coding-agents, a project described as: Autonomous background coding agents on AWS. Turn tasks into pull requests via isolated runtimes, with built-in orchestration, observability, and governance.
serverless-litellm CLAUDE.md
Instructions for cncoder/serverless-litellm, covering claude.md — litellm on eks 项目指南, 项目概述, 快速部署（一键）, 前置条件 and 部署流程.
sample-agentic-chatbot-accelerator CLAUDE.md
Claude Code instructions for aws-samples/sample-agentic-chatbot-accelerator, covering claude.md, project, common commands, cdk (default) and terraform (experimental).
cc-aws-keepalive CLAUDE.md
Claude Code instructions for GeiserX/cc-aws-keepalive, covering claude.md - cc-aws-keepalive, architecture, config, plugin integration and cross-platform.
langchain-aws AGENTS.md
AGENTS.md instructions for langchain-ai/langchain-aws, covering global development guidelines for the langchain monorepo, project architecture and context, langchain-aws structure, key components and langgraph-checkpoint-aws structure.