Borrowing it
Nothing to install: this file belongs to tonydzi/sqlite-graph-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/tonydzi/sqlite-graph-memory/main/AGENTS.mdgit clone --depth 1 https://github.com/tonydzi/sqlite-graph-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/tonydzi/sqlite-graph-memory/agents-md)<a href="https://agentmods.dev/instructions/tonydzi/sqlite-graph-memory/agents-md"><img src="https://agentmods.dev/badge/instructions/tonydzi/sqlite-graph-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/tonydzi/sqlite-graph-memory/agents-md"><img src="https://agentmods.dev/badge/instructions/tonydzi/sqlite-graph-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 | $0.01236 | $0.01236 |
| Opus 5.5 | $0.00494 | $0.00494 |
| Sonnet 5.5 | $0.00247 | $0.00247 |
| Haiku 4.5 | $0.00124 | $0.00124 |
Grade A, and why
sqlite-graph-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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — working in this repo
Written for AI coding agents, and equally readable by a human contributor. Short on purpose.
What this repo is
The extracted memory layer of a personal second-brain agent setup: vector retrieval for entry
points, hand-curated [[wikilinks]] for association, a cross-encoder rerank, and SQLite for the
only two things worth persisting — a per-turn ledger and A/B telemetry.
Status: pilot. It runs daily in one real setup and it is deliberately minimal. It is not a framework and should not grow into one. The interesting claim here is what it does not need: no graph database, no ETL, no entity-extraction pass.
Stack and layout
- Python + SQLite (stdlib driver).
requirements.txtcovers the embedding/rerank models only. index_notes.py— chunk + embed a markdown folder (e5-base) into a.npy/.pklindex.brain_ask.py— the recall pipeline: dense retrieve → optional--graph1-hop wikilink expansion → cross-encoder rerank → top-12.--abruns vector-only and vector+graph, diffs them, and logs the delta to SQLite.turnstate_hook.py— agent Stop-hook; appends one row per assistant turn. Zero tokens, pure stdlib.turnstate_show.pyis the read-only viewer.eval/build_gold.py+eval/run_eval.py— the retrieval ruler: a gold set built from the vault's own[[wikilinks]](four question classes), scored in both modes as Recall@12 / MRR / nDCG@12.run_eval.pyimportsbrain_ask.pyrather than re-implementing it, so the eval and the agent cannot drift apart.schema.sql— documents both tables (turns,ab_recall);run_eval.py --sqliteadds a third,gold_eval, one row per class and mode per run.examples/claude-code-stop-hook.json— how the hook gets wired.
How to verify a change
Start with pytest -q — the suite needs no model download and no network. Then run the thing
you changed and paste the output:
pytest -q # file rules, entity gate, link parsing, eval metrics
python index_notes.py <folder-of-markdown> # build an index over a small sample
python brain_ask.py "<question>" --graph # recall, with graph expansion
python brain_ask.py "<question>" --ab # both arms + the logged delta
python turnstate_show.py # what the ledger captured
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 · +20 lines · +297 tokens per session 996774e67ffe
- 1mo ago First seen · 73 lines · 939 tokens per session scan A f0bd7c7d23e5
sqlite-graph-memory AGENTS.md is an instructions file published in the GitHub repository tonydzi/sqlite-graph-memory (12 stars, last pushed yesterday), licensed MIT. It adds 1,236 tokens to every session, about $0.0049 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.