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/dpbg/engram.ai/claude-mdgit clone --depth 1 https://github.com/DPBG/Engram.AIWrote 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/dpbg/engram.ai/claude-md)<a href="https://agentmods.dev/instructions/dpbg/engram.ai/claude-md"><img src="https://agentmods.dev/badge/instructions/dpbg/engram.ai/claude-md.svg" alt="Measured on agentmods" 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 | $0.02346 | $0.02346 |
| Opus 5 | $0.01173 | $0.01173 |
| Sonnet 5 | $0.00469 | $0.00469 |
| Haiku 4.5 | $0.00235 | $0.00235 |
Grade A, and why
Engram.AI CLAUDE.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 4d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — Engram Architecture & Contributor Guide
This file is the authoritative source for Engram's non-negotiable architectural constraints. It is cited by CONTRIBUTING.md, DESIGN-PRINCIPLES.md, and the issue templates. It also serves as the working context for Claude Code and other agents operating in this repo.
If a proposed change conflicts with anything here, stop and open an issue before implementing.
1. What Engram Is
Engram is a self-aware, continuously-learning neuromorphic AI system. Its
intelligence lives in a spiking neural network (the neuromorphic/ "brain"),
surrounded by microservices that provide sensory input, safety governance,
memory, planning, and a web dashboard. Services are independent processes that
communicate over NATS and persist to SQLite (+ Qdrant for vectors).
Two ways to run the same system:
- Pure Python (
python run.py) — the launcher downloads NATS and runs each service as a subprocess. Best for local development. See RUN-LOCAL.md. - Docker Compose (
docker compose up) — each service is a container. The Hetzner deployment layersdocker-compose.yml+deploy/docker-compose.1m.yml.
2. The Six Architectural Invariants (non-negotiable)
All neuromorphic code MUST conform to these. They define what Engram is; a change that violates one is a change to a different system. Primary implementation files are listed for each.
Invariant 1 — Integrated Multi-Mechanism Learning
All 6 learning mechanisms operate together and are never individually disabled: STDP, eligibility traces, BCM metaplasticity, 4-channel neuromodulation (DA/ACh/NE/5-HT), homeostatic scaling, and R-STDP.
- Files:
neuromorphic/src/neuromorphic/synapses.py,neuromodulation.py,network.py - Enforcement note: For performance, some mechanisms update on a fixed interval (e.g. STDP every N steps) using compensated decay so the result is mathematically equivalent to running every step. This is "logically every step." Any change to those intervals MUST preserve equivalence and be covered by an equivalence test. Mechanisms may never be turned off.
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.
- 4d ago First seen · 191 lines · 2,346 tokens per session scan A 1ffbef2678c8
Engram.AI CLAUDE.md is an instructions file published in the GitHub repository DPBG/Engram.AI (5 stars, last pushed 1mo ago), licensed MIT. It adds 2,346 tokens to every session, about $0.0117 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 2026-08-31.
Other instructions, from other repositories
aeon CLAUDE.md
Instructions for aeonfun/aeon, covering aeon, how aeon works, strategy, voice and soul file hierarchy (read in this order).
cognirepo CLAUDE.md
Claude Code instructions for ashlesh-t/cognirepo, covering claude.md, key rules, session start sequence (run in this order), behavioral confirmation rule and personas (cognirepo-402, cognirepo-403).
wayland-core copilot-instructions.md
Copilot instructions for FerroxLabs/wayland-core, covering ijfw rules, output discipline, memory routing, context discipline and cross-audit.
mcp-structured-memory CLAUDE.md
Claude Code instructions for nmeierpolys/mcp-structured-memory, a project described as: Structured Memory MCP Server.
inkwell-memory CLAUDE.md
Instructions for veronchenko/inkwell-memory, covering claude.md — inkwellmemory, layout, multi-tenant mode (inkwellmultitenant=1), conventions and testing.
RNR-Enhanced-Cognee AGENTS.md
AGENTS.md instructions for vincentspereira/RNR-Enhanced-Cognee, covering rnr enhanced cognee implementation for codex, critical requirements, 1. ascii-only output (no unicode encoding), 2. dynamic categories (no hardcoded categories) and 3. standard memory mcp interface.