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.
curl -O https://raw.githubusercontent.com/Lexus2016/turbo_quant_memory/main/AGENTS.mdgit clone --depth 1 https://github.com/Lexus2016/turbo_quant_memoryWrote 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/lexus2016/turbo_quant_memory/agents-md)<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.
<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>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 | $0.01162 | $0.01162 |
| Opus 5.5 | $0.00465 | $0.00465 |
| Sonnet 5.5 | $0.00232 | $0.00232 |
| Haiku 4.5 | $0.00116 | $0.00116 |
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.
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
hydrateonly 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_contextFIRST. It is query-free and returns the most recently updated notes, including sessionhandoffnotes — the reliable "where did I leave off" entry point when you do not yet know what to search for. Inhybridscope the current project's notes fill the window first; other projects' promoted notes only backfill leftover slots (passscope="global"for cross-project news). - Before a long pause or an expected compaction, write one
handoffnote (remember_note(kind="handoff", ...)) summarising mission, decisions, files touched, current state, and open questions. Handoffs land in theepisodictier. semantic_searchexcludesepisodicby default so session noise does not drown durable knowledge. To retrieve handoffs by query, passtier_filter=["episodic"];recent_contextincludes 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"onremember_noteonly when a session-bridging note must also appear in ordinary searches.
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 First seen · 73 lines · 1,162 tokens per session scan A 12e3366b3a19
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.
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.