Borrowing it
Nothing to install: this file belongs to dkothule/ai-context. 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/dkothule/ai-context/main/AGENTS.mdgit clone --depth 1 https://github.com/dkothule/ai-contextWrote 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/dkothule/ai-context/agents-md)<a href="https://agentmods.dev/instructions/dkothule/ai-context/agents-md"><img src="https://agentmods.dev/badge/instructions/dkothule/ai-context/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/dkothule/ai-context/agents-md"><img src="https://agentmods.dev/badge/instructions/dkothule/ai-context/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 | $0.01296 | $0.01296 |
| Opus 5.5 | $0.00518 | $0.00518 |
| Sonnet 5.5 | $0.00259 | $0.00259 |
| Haiku 4.5 | $0.00130 | $0.00130 |
Grade A, and why
ai-context 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 1mo 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Shared agent adapter
This file is intentionally thin. The single source of truth is .ai-context/, loaded on demand.
Read First (Every Session)
All paths below are relative to .ai-context/.
Always read:
project.overview.md— what this project isproject.tasks.md— what is in flight, blocked, next- Session logs in
sessions/— never_archive/, never_template.md: a. Newest by filename date. If several share that date, order by thetime:frontmatter field; where absent, fall back to filename order. b. Scan the# Session:heading of the other same-date logs and read any whose topic relates to the current task. c. If that log's "Next Steps" or "Notes For Next Agent" points at earlier work you are continuing, follow the reference. Read at most 3 logs unless continuation references in (c) require more.
Then read based on task:
- Writing/modifying code →
standards/project.rules.base.md,standards/project.rules.md - Release/history context →
project.changelog.md - Understanding layout →
project.structure.md - Planning non-trivial work →
project.tasks.md,plans/ - Language/testing → files in
standards/
Planning
Before non-trivial work (multi-session, architectural change, external dependency), write a plan to .ai-context/plans/YYYY-MM-DD-<topic>.md using _template.md. Reference the plan from project.tasks.md so it's discoverable. After plan approval, write the file immediately — before any implementation begins.
Execution Contract
- Follow
.ai-context/standards/project.rules.base.mdandproject.rules.md. - One logical change per commit; tests run before commit.
- Keep
.ai-context/in sync with project state — route each change to the correct file:- New architectural decision →
project.decisions.md - User-visible change →
project.changelog.md - Task transition (new/done/blocked) →
project.tasks.md - Plan authored →
plans/YYYY-MM-DD-<topic>.md - Session close →
sessions/YYYY-MM-DD-<topic>.md
- New architectural decision →
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.
- 1mo ago Changed · +15 lines · +361 tokens per session 7263587cb22f
- 2mo ago First seen · 56 lines · 935 tokens per session scan A f75914d6cd30
ai-context AGENTS.md is an instructions file published in the GitHub repository dkothule/ai-context (11 stars, last pushed 1mo ago), licensed MIT. It adds 1,296 tokens to every session, about $0.0052 per session on Opus 5.5. 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
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).
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.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
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.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
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.