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/sandst1/remind/agents-mdgit clone --depth 1 https://github.com/sandst1/remindWhat 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 | $0.03149 | $0.03149 |
| Opus 5 | $0.01574 | $0.01574 |
| Sonnet 5 | $0.00630 | $0.00630 |
| Haiku 4.5 | $0.00315 | $0.00315 |
Grade A, and why
remind 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 yesterday.
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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remind - Development Guide for AI Agents
This guide is for AI agents developing Remind itself. For using Remind as a memory layer, see docs/AGENTS.md.
Project Overview
Remind is an agent-driven memory layer for LLMs. It provides temporal facts, semantic retrieval, and structured curation — the calling agent is the only intelligence. There are no internal LLM calls.
Core architecture: Episodes → Agent curation via apply → Concepts with relations
Architecture
src/remind/
├── models.py # Data models (Concept, Episode, Entity, Relation, Fact, Conflict)
├── store.py # SQLAlchemy persistence layer (SQLite, PostgreSQL, MySQL)
├── interface.py # MemoryInterface - main public API
├── config.py # Configuration loading (config file, env vars, defaults)
├── facts.py # Deterministic fact processing (clustering, collision detection)
├── apply.py # Batch write engine (op vocabulary, transaction support)
├── snapshot.py # Batch read engine (combinable scopes)
├── retrieval.py # Spreading activation retrieval
├── reranker.py # Optional cross-encoder reranking (requires [rerank] extra)
├── cli.py # Command-line interface (project-aware)
├── mcp_server.py # MCP (Model Context Protocol) server
├── background.py # Background recall worker spawning
├── background_worker.py # Subprocess entry point for recall worker
├── api/ # REST API for web UI
│ ├── __init__.py # Exports api_routes
│ └── routes.py # Starlette route handlers
├── static/ # Web UI assets (compiled)
│ ├── index.html # Entry point
│ └── assets/ # CSS/JS bundles
├── skills/ # Bundled agent skills (installed via `remind skill-install`)
│ ├── remind-capture/ # When/how to write memories while working
│ ├── remind-context/ # When/how to recall before acting
│ └── remind-curate/ # Consolidation procedure, conflict triage, label upkeep
└── providers/ # Embedding provider implementations
├── base.py # EmbeddingProvider ABC
├── local.py # Local embeddings via fastembed (default)
├── openai.py # OpenAI embeddings
├── azure_openai.py # Azure OpenAI embeddings
└── ollama.py # Ollama embeddings
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.
- yesterday First seen · 318 lines · 3,149 tokens per session scan A 44626315c240
remind AGENTS.md is an instructions file published in the GitHub repository sandst1/remind (83 stars, last pushed 22d ago), licensed Apache-2.0. It adds 3,149 tokens to every session, about $0.0157 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
optillm CLAUDE.md
Instructions for algorithmicsuperintelligence/optillm, covering claude.md, project overview, core architecture, main components and development commands.
agentor CLAUDE.md
Instructions for lonetis/agentor, covering agent orchestrator (agentor), architecture, detailed documentation, tech stack and dev commands.
promptPrimer AGENTS.md
Instructions for SeidSmatti/promptPrimer, covering promptprimer — agentic task prompt generator, role, supported harnesses, supported task types and workflow.
nocturnusai AGENTS.md
AGENTS.md instructions for Auctalis/nocturnusai, covering agents.md, project overview, build & run commands, build all modules and run the http api server (port 9300).
nocturnusai CLAUDE.md
Claude Code instructions for Auctalis/nocturnusai, covering claude.md, project overview, build & run commands, build all modules and run the http api server (port 9300).
promptPrimer CLAUDE.md
Instructions for SeidSmatti/promptPrimer, covering promptprimer — agentic task prompt generator, role, supported harnesses, supported task types and workflow.