Borrowing it
Nothing to install: this file belongs to Emily2040/rapid-domain-mastery. 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/Emily2040/rapid-domain-mastery/main/AGENTS.mdgit clone --depth 1 https://github.com/Emily2040/rapid-domain-masteryWrote 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/emily2040/rapid-domain-mastery/agents-md)<a href="https://agentmods.dev/instructions/emily2040/rapid-domain-mastery/agents-md"><img src="https://agentmods.dev/badge/instructions/emily2040/rapid-domain-mastery/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/emily2040/rapid-domain-mastery/agents-md"><img src="https://agentmods.dev/badge/instructions/emily2040/rapid-domain-mastery/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 | $0.00340 | $0.00340 |
| Opus 5 | $0.00170 | $0.00170 |
| Sonnet 5 | $0.00068 | $0.00068 |
| Haiku 4.5 | $0.00034 | $0.00034 |
Grade A, and why
rapid-domain-mastery 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 9d 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.
What it actually says
Repository Purpose
This repository packages a portable, source-heavy learning workflow for AI coding agents.
The core capability is rapid-domain-mastery, a reusable skill for:
- corpus audit
- field mapping
- mental model extraction
- debate mapping
- assumption mining
- prerequisite planning
- diagnostic questioning
- oral-exam tutoring
- compressed study sprints
Source of truth
Treat these as canonical:
AGENTS.md.agents/skills/rapid-domain-mastery/support/support-matrix.json
All vendor-specific wrappers should stay semantically aligned with the canonical skill.
How to work in this repo
- Prefer editing the canonical skill under
.agents/skills/rapid-domain-mastery/. - After semantic changes, sync mirrored skill folders.
- Do not silently change behavior in one wrapper and not the others.
- Keep wrappers thin. They should adapt placement and host format, not rewrite the workflow.
- Keep support claims conservative. If vendor docs are unclear, mark the adapter as manual or compatibility-based.
Guidance for agents
When asked to improve this pack:
- first update the canonical skill
- then update references, templates, and validation scripts
- then update wrappers
- then update the support matrix
When asked whether a client is supported:
- check
VERIFIED-SUPPORT-MATRIX.md - distinguish official-docs vs directory-listed vs manual
- never claim that one file layout works everywhere if the docs do not say that
Editing rule
Do not turn AGENTS.md into a giant workflow dump.
Persistent guidance belongs here.
Deep procedures belong in the skill.
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.
- 9d ago First seen · 52 lines · 340 tokens per session scan A 7d320e81e0ed
rapid-domain-mastery AGENTS.md is an instructions file published in the GitHub repository Emily2040/rapid-domain-mastery (13 stars, last pushed 3mo ago), licensed MIT. It adds 340 tokens to every session, about $0.0017 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
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.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
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.
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.