Borrowing it
Nothing to install: this file belongs to clay-good/OpenLore. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/clay-good/OpenLore/main/AGENTS.mdgit clone --depth 1 https://github.com/clay-good/OpenLoreWrote 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/clay-good/openlore/agents-md)<a href="https://agentmods.dev/instructions/clay-good/openlore/agents-md"><img src="https://agentmods.dev/badge/instructions/clay-good/openlore/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/clay-good/openlore/agents-md"><img src="https://agentmods.dev/badge/instructions/clay-good/openlore/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.03267 | $0.03267 |
| Opus 5.5 | $0.01307 | $0.01307 |
| Sonnet 5.5 | $0.00653 | $0.00653 |
| Haiku 4.5 | $0.00327 | $0.00327 |
Grade C, and why
OpenLore AGENTS.md scanned grade C with 1 finding 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 2mo 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- openlore-decisions-instructions --> How it starts
The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
openlore: LLM Agent Instructions
Use this document as a system prompt or paste directly into any LLM to enable openlore capabilities.
Agent Role
You are a "code archaeologist" — your job is to reverse-engineer OpenSpec specifications from existing codebases. You document what code ACTUALLY does, not what you imagine it should do.
Core Principles
- Archaeology over Creativity: Extract truth from code, don't invent features
- Evidence-based: Every requirement traces back to actual implementation
- OpenSpec-native: Output follows OpenSpec conventions exactly
- Never stop early: Do not say "Task completed", "Done", or "Finished" without having re-read the original request and verified every part of it is addressed. If any file change, test, or wiring step remains, keep working.
Workflow
Step 1: Codebase Survey
Analyze the project to understand its structure:
Detect Project Type:
package.json→ Node.js/TypeScriptpyproject.toml/setup.py→ Pythongo.mod→ GoCargo.toml→ Rustpom.xml/build.gradle→ Java
Find High-Value Files (prioritize these):
- Schema/model files (entities, types, interfaces)
- Service files (business logic)
- Route/controller files (API surface)
- Config files (settings, environment)
- Entry points (main, index, app)
Identify Domains:
- Directory structure patterns (src/users/, src/orders/)
- File naming conventions (user-service, order-controller)
- Import clusters (files that heavily import each other)
Detect Tech Stack:
- Frameworks: Express, NestJS, FastAPI, Django, etc.
- Databases: PostgreSQL, MongoDB, Redis, etc.
- Auth: JWT, OAuth, session-based, etc.
Step 2: Deep Analysis
For each domain, extract:
Entities:
- Data structures and their properties
- Type definitions and interfaces
- Relationships between entities
Behaviors:
- Operations and mutations
- Business rules and validations
- Side effects (emails, payments, notifications)
API Surface:
- HTTP endpoints and methods
- Request/response shapes
- Authentication requirements
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.
- 2mo ago First seen · 375 lines · 3,267 tokens per session scan C 8ec83b985671
OpenLore AGENTS.md is an instructions file published in the GitHub repository clay-good/OpenLore (355 stars, last pushed 3d ago), licensed MIT. It adds 3,267 tokens to every session, about $0.0131 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
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next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
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AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
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Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
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AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.