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/vilkovgr/0dte-strategies/agents-mdgit clone --depth 1 https://github.com/vilkovgr/0dte-strategiesWrote 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/vilkovgr/0dte-strategies/agents-md)<a href="https://agentmods.dev/instructions/vilkovgr/0dte-strategies/agents-md"><img src="https://agentmods.dev/badge/instructions/vilkovgr/0dte-strategies/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 5.1 | $0.02643 | $0.02643 |
| Opus 5 | $0.01321 | $0.01321 |
| Sonnet 5 | $0.00529 | $0.00529 |
| Haiku 4.5 | $0.00264 | $0.00264 |
Grade A, and why
0dte-strategies 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — AI Agent Onboarding
Context file for AI coding agents (GitHub Copilot, OpenAI Codex, Claude, etc.) working inside this repository.
Mission
This is the public replication package for the paper "0DTE Trading Rules: Tail Risk, Implementation, and Tactical Timing" (Vilkov, 2026). The repo enables three tiers of engagement:
- Instant replication — run analysis scripts against shipped derived data to reproduce every table and figure.
- Rebuild from source — use Massive or ThetaData API adapters to reconstruct raw data from scratch.
- Explore and extend — use AI-optimized context to understand, critique, or extend the analysis.
Paper Summary (One Paragraph)
The paper studies realized payoffs of S&P 500 zero-days-to-expiration (0DTE) options and standard multi-leg structures from 09/2016 to 01/2026. A positive 0DTE variance risk premium exists but is small after realistic frictions. Strategy PNL distributions are wide, tail-heavy, and regime-dependent — dominated by directional and skewness realizations rather than stable mean carry. Yet disciplined 10:00 ET conditional rules under a strict out-of-sample protocol deliver economically meaningful net performance for selected strategies (put ratio spreads SR ≈ 1.26, iron butterflies SR ≈ 0.82) and diversified baskets (SR 1.01–1.27). The practical implication: 0DTE is better viewed as a tightly risk-budgeted tactical overlay than a standing carry strategy.
Three-Tier Data Model
Tier 1 (shipped): data/*.parquet, data/*.csv
↓ analysis scripts read these directly
output/tables/*.tex, output/figures/*.pdf
Tier 2 (rebuild): API key → code/ingest/{massive,thetadata}/
→ raw snapshots → code/build_data.py
→ data/*.parquet (same schema as Tier 1)
Tier 3 (explore): docs/agent-context/* + AGENTS.md + CLAUDE.md
→ LLM understands paper claims, methods, variables
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 · 181 lines · 2,643 tokens per session scan A 07f065adf085
0dte-strategies AGENTS.md is an instructions file published in the GitHub repository vilkovgr/0dte-strategies (54 stars, last pushed 9d ago), licensed MIT. It adds 2,643 tokens to every session, about $0.0132 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
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).
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.
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.
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 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).