SolvingMicroDSOPs AGENTS.md

Repository instructions for SolvingMicroDSOPs, a set of economics lecture notes and code about how households choose spending and saving under uncertainty.

In plain words
What is it for?
Use them to navigate the LaTeX book, Python or Stata replication code, Jupyter notebook, figures, tables, and test suite.
Why use it?
They explain where the main document, equations, figures, replication code, notebook, and tests are located before an agent edits or builds the project.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/llorracc/solvingmicrodsops/agents-md
Clone the repo
git clone --depth 1 https://github.com/llorracc/SolvingMicroDSOPs

Made for: Codex, OpenCode.

Per session 783 This file is loaded in full into every session.
When invoked 783 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00783 $0.00783
Opus 5 $0.00392 $0.00392
Sonnet 5 $0.00157 $0.00157
Haiku 4.5 $0.00078 $0.00078

Measured 2d ago against content hash f272be187cb0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

SolvingMicroDSOPs 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 2d 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.

AGENTS.md · 49 lines

How it starts

The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Orientation for AI-Assisted Work

This repository contains SolvingMicroDSOPs: LaTeX lecture notes plus replication code (Python, Stata) for numerical methods in consumption/saving problems under uncertainty. The main artifact is the book PDF built from root-level .tex files.

The document teaches how to solve consumption-saving problems efficiently, progressing from brute-force value function iteration through the transformation trick (inverse marginal value), the Endogenous Grid Method (EGM), and a modular stage architecture for composing complex periods. It concludes with structural estimation matching model predictions to Survey of Consumer Finances data.

Quick orientation

Path Purpose
SolvingMicroDSOPs.tex Main document entry
_sectn-*.tex Section subfiles
Equations/ Equation fragments (included by main doc)
Tables/, Figures/ Tables and figures
Code/Python/, Code/Stata/ Replication code
Code/Python/tests/ Test suite (pytest)
SolvingMicroDSOPs.ipynb Jupyter notebook implementing the methods
prompts/ Canonical prompts for AI-assisted work
@resources/, @local/ LaTeX search path and macros (needed for build)
CONTRIBUTION.md What this document contributes, audience, HARK comparison
ROADMAP.md How to read this — suggested reading paths
Notation.md Glossary of all mathematical notation and macros
FIGURES.md Figure regeneration manifest (Python vs. legacy)
.github/workflows/ci.yml CI pipeline (tests, notebook, lint)

What this document contributes

See CONTRIBUTION.md for a concise statement of what the document covers, who it is for, and how it relates to HARK and standard textbooks.

Critical rules

  • Matsya: When the user says to "ask Matsya" (or equivalent), show the full output that Matsya produces—do not replace it with a summary you construct.
  • docs/ is a reference copy. Do not edit it. All edits use root-level files only.
  • Build: latexmk on root .tex (uses LuaLaTeX via .latexmkrc)
  • Code mirrors math: Python identifiers parallel LaTeX macros (e.g. DiscFac\DiscFac, vEndPrd\vEndPrd)
  • verbatimwrite pattern: Many equation files in Equations/ are generated at compile time by \begin{verbatimwrite} blocks inside _sectn-*.tex files. Always edit the equation in the section file, not the generated .tex fragment in Equations/.

Read the full file on GitHub · 49 lines

Changes

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.

  1. 2d ago First seen · 49 lines · 783 tokens per session scan A f272be187cb0

Subscribe to this mod's changes

SolvingMicroDSOPs AGENTS.md is an instructions file published in the GitHub repository llorracc/SolvingMicroDSOPs (22 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 783 tokens to every session, about $0.0039 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.

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