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/coding-dev-tools/engraphis/claude-mdgit clone --depth 1 https://github.com/Coding-Dev-Tools/engraphisWhat 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 | $0.01082 | $0.01082 |
| Opus 5 | $0.00541 | $0.00541 |
| Sonnet 5 | $0.00216 | $0.00216 |
| Haiku 4.5 | $0.00108 | $0.00108 |
Grade A, and why
engraphis 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 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
The full operating manual for this repo lives in @AGENTS.md — read it first. It is the canonical, vendor-neutral source (architecture, commands, conventions, algorithms, gotchas); everything there applies to Claude Code. This file adds only Claude-specific guidance, so the two never drift.
The one rule that prevents most mistakes
Two codebases share engraphis/: v2 (core/ + backends/ — the target) and the v1
legacy FastAPI server (app.py, routes/, stores/, engines/, flat namespaces). Build new
capability on v2 behind the interfaces in core/interfaces.py. Decide which side a change
belongs to before editing. Full table: AGENTS.md §0.
Before you say "done" — run the canonical gate
Use the exact primary offline gate in AGENTS.md §1; do not maintain a smaller duplicate here.
.github/workflows/ci.yml is authoritative for the current Python matrix and dedicated
typecheck, encryption, and built-artifact jobs. No network or API key is required for the primary
gate. If you changed retrieval, scoring, or ranking, add or update an eval—per AGENTS.md §3.7,
"better" needs a number, not an assertion.
Slash commands available here
/init— regenerate codebase documentation./review— review a GitHub pull request (/code-reviewfor the local working diff)./security-review— review pending changes for vulnerabilities. Run this before finishing any change to the write/ingest path: ingested content is treated as untrusted and memory poisoning is an explicit threat. This now also coversMemoryEngine.index_repo()(reads local files at an agent-supplied path — seeSECURITY.md§5) and the deterministic conflict resolver (core/resolve.py).
Working style in this repo
- Interface-first & dependency-light (AGENTS.md §3): every
core/module, includingcore/engine.py, remains protocol-only and runnable on NumPy. Concrete backend selection lives in the outer composition rootengraphis/factory.py;engraphis/__init__.pyregisters it forMemoryEngine.create(), andengraphis.create_memory_engine()exposes it directly. Gate heavy imports behind backend factories and never import a concrete backend fromcore/. - House style:
ruffline-length 100, Python 3.9-compatible syntax, pure/tested scoring functions, provenance and scope on every memory. - Be concise and direct in chat — explain the why of a change briefly, link the file, and let the diff speak.
- When code and docs disagree, the code wins — then fix the doc in the same change, and
update
AGENTS.md/CLAUDE.mdif a convention or command changed.
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 · 72 lines · 1,082 tokens per session scan A 87de494e009e
engraphis CLAUDE.md is an instructions file published in the GitHub repository Coding-Dev-Tools/engraphis (170 stars, last pushed 2d ago), licensed Apache-2.0. It adds 1,082 tokens to every session, about $0.0054 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
Binder AGENTS.md
Instructions for mpazik/Binder, covering binder, tech stack, monorepo structure, testing and development.
agentmemory-codex-windows AGENTS.md
Instructions for M-T-D-N/agentmemory-codex-windows, covering agentmemory — agent instructions, architecture, windows/codex downstream profile, consistency rules and code patterns.
cli AGENTS.md
Instructions for konteks/cli, covering agent instructions, project overview, tech stack, repository layout and common commands.
mnemic CLAUDE.md
Claude Code instructions for dongtang3/mnemic: This project includes a project-level .mcp.json for the Mnemic memory MCP server.
mnemic AGENTS.md
AGENTS.md instructions for dongtang3/mnemic: Mnemic is a graph-backed long-term memory substrate for coding agents and LLM applications.
m_flow AGENTS.md
AGENTS.md instructions for FlowElement-xinliuyuansu/m_flow, covering m-flow — developer & agent reference, 1. repository map, extension points, 2. local development and python backend (requires python 3.10 – 3.13).