turbo_quant_memory: Instructions file for Codex

AGENTS.md

turbo_quant_memory AGENTS.md is an instructions file for Codex, OpenCode from Lexus2016/turbo_quant_memory. It costs 1,162 tokens per session, scanned A, original, MIT.

Project instructions for a quantitative-trading codebase, covering agent memory, session continuity, and access to project secrets.

In plain words
What is it for?
Use them when working in turbo_quant_memory, especially at the start of a session, after context compaction, or when project secrets are needed.
Why use it?
They help the agent preserve context between sessions and follow the repository's rules for memory and sensitive information.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md.

This is Lexus2016/turbo_quant_memory's own configuration. It tells Codex and OpenCode how to work on turbo_quant_memory itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything turbo_quant_memory configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Lexus2016/turbo_quant_memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Lexus2016/turbo_quant_memory/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/Lexus2016/turbo_quant_memory

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for turbo_quant_memory AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/lexus2016/turbo_quant_memory/agents-md/github.svg)](https://agentmods.dev/instructions/lexus2016/turbo_quant_memory/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/lexus2016/turbo_quant_memory/agents-md"><img src="https://agentmods.dev/badge/instructions/lexus2016/turbo_quant_memory/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.

agentmods 80×15 button for turbo_quant_memory AGENTS.md

Your own site · 80×15
<a href="https://agentmods.dev/instructions/lexus2016/turbo_quant_memory/agents-md"><img src="https://agentmods.dev/badge/instructions/lexus2016/turbo_quant_memory/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 1,162 This file is loaded in full into every session.
When invoked 1,162 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.01162 $0.01162
Opus 5.5 $0.00465 $0.00465
Sonnet 5.5 $0.00232 $0.00232
Haiku 4.5 $0.00116 $0.00116

Measured 1mo ago against content hash 12e3366b3a19, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

turbo_quant_memory 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.

AGENTS.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Project Identity

  • Project name: Turbo Quant Memory for AI Agents
  • Project slug: turbo_quant_memory
  • Memory user_id: tq_memory_agents_20260325_default
  • Default sessionId: default

Memory Usage Policy

  • Use project-scoped memory by default.
  • Use global memory only for reusable cross-project knowledge.
  • Prefer hybrid search with project bias when reading.
  • Require explicit promotion from project to global memory.
  • Every memory result must preserve provenance.
  • Before editing an existing area, search project memory first.
  • Use hydrate only when compact retrieval is not enough.
  • After important decisions, debugging outcomes, or handoffs, store one concise note.
  • After README, config, workflow, or architecture changes, refresh indexed docs.
  • When old knowledge is replaced, write the new note and deprecate the old one.
  • Do not keep smoke, temporary, or validation notes active.
  • Treat .planning, .serena, and generated benchmark artifacts as historical workflow context, not as primary user-facing memory.
  • Use Knowledge Graph relations (link_entities, unlink_entities, get_related_entities) to build associations between notes, source files, issues, or tasks, enabling association-based semantic retrieval.

Session Continuity (start-of-session and compaction)

  • At session start, or right after a context compaction, call recent_context FIRST. It is query-free and returns the most recently updated notes, including session handoff notes — the reliable "where did I leave off" entry point when you do not yet know what to search for. In hybrid scope the current project's notes fill the window first; other projects' promoted notes only backfill leftover slots (pass scope="global" for cross-project news).
  • Before a long pause or an expected compaction, write one handoff note (remember_note(kind="handoff", ...)) summarising mission, decisions, files touched, current state, and open questions. Handoffs land in the episodic tier.
  • semantic_search excludes episodic by default so session noise does not drown durable knowledge. To retrieve handoffs by query, pass tier_filter=["episodic"]; recent_context includes them automatically.
  • Asking "what did we decide/learn about X"? Pass source_filter="notes" — in doc-heavy projects the indexed markdown blocks otherwise crowd decision/lesson notes out of the top ranks. source_filter="markdown" narrows to docs only.
  • Use tier="durable" on remember_note only when a session-bridging note must also appear in ordinary searches.

Read the full file on GitHub · 73 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. 1mo ago First seen · 73 lines · 1,162 tokens per session scan A 12e3366b3a19

Subscribe to this mod's changes

turbo_quant_memory AGENTS.md is an instructions file published in the GitHub repository Lexus2016/turbo_quant_memory (9 stars, last pushed 5d ago), licensed MIT. It adds 1,162 tokens to every session, about $0.0046 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 2026-08-31.

Related

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).

microsoft/vscode · 9,715 tokens

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.

vercel/next.js · 7,461 tokens

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).

microsoft/vscode · 5,262 tokens

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.

langchain-ai/langchain · 4,680 tokens

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).

microsoft/vscode · 2,731 tokens

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.

deepseek-ai/deepseek-harness · 4,261 tokens