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/stevepridemore/graph-memory/claude-mdgit clone --depth 1 https://github.com/stevepridemore/graph-memoryWhat 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 | $0.00632 | $0.00632 |
| Opus 5 | $0.00316 | $0.00316 |
| Sonnet 5 | $0.00126 | $0.00126 |
| Haiku 4.5 | $0.00063 | $0.00063 |
Grade A, and why
graph-memory 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 2d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Memory
You have access to a graph-based memory system via the graph-memory MCP server.
When to consult the graph
- When the user asks about a person, project, past decision, or preference — call
graph_querybefore answering from general knowledge - When the user references something from a previous conversation — the graph likely has context
- When you're about to write a memory file — check
graph_entitiesfirst to see if related knowledge already exists - When the user starts a new session — consider offering
/graph-briefingif the project has graph data
How to use it
graph_queryfor structured lookups (entities, relationships, weights)graph_cypherwhen you need a custom query (you know the schema — see /graph-ask skill)graph_boostwhen the user confirms something you recalled from the graphgraph_weakenwhen the user corrects something the graph got wrong
Writing to the graph during conversation
You MAY write to the graph during conversation for high-confidence, explicit knowledge — but follow these rules:
When to write (call graph_relate)
- User explicitly states a fact, preference, or decision ("I prefer X", "we decided Y", "Z works for me")
- User introduces a person, project, or tool by name with clear context
- User confirms or corrects something you recalled from the graph
When NOT to write (defer to the dream process)
- Inferred context or things you're guessing from the conversation
- Casual mentions without clear significance
- Anything you're not confident about — the dream process will catch it later
Weight guidelines
| Origin | Weight |
|---|---|
| Explicit user statement ("I prefer X", "we use Y") | 0.7 |
| User confirmed recalled info ("yes, exactly") | boost +0.15 |
| Mentioned in context but not stated directly | don't write — let dream handle at 0.3 |
Use specific relationship types — not just RELATED_TO
The full vocabulary (node types and edge verbs) lives in
GRAPH_SCHEMA.md at the project root. Read that
file before writing edges so you pick a specific verb (ABOUT,
PART_OF, IMPLEMENTS, INSPIRED_BY, DEPENDS_ON, etc.) over
generic RELATED_TO. RELATED_TO is the fallback only — use it when
no specific verb fits.
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.
- 2d ago First seen · 61 lines · 632 tokens per session scan A 8c8f16470aa0
graph-memory CLAUDE.md is an instructions file published in the GitHub repository stevepridemore/graph-memory (2 stars, last pushed 3mo ago), licensed MIT. It adds 632 tokens to every session, about $0.0032 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
Waggle-mcp AGENTS.md
Instructions for Abhigyan-Shekhar/Waggle-mcp, covering repository agent rules, custom rules and waggle automatic memory.
vellis AGENTS.md
Instructions for volantlabs/vellis, covering agents.md, repository purpose, startup checks, installing vellis for a user and model-first component workflow.
dakera-cli CLAUDE.md
Instructions for Dakera-AI/dakera-cli, covering dakera-cli, key commands, architecture and conventions.
pos CLAUDE.md
Instructions for xcota/pos, covering personal os, rules, memory, budget and hard rules.
pos AGENTS.md
Instructions for xcota/pos, covering agents.md — workspace boot rules, session boot, memory, safety and language.
cli AGENTS.md
Instructions for konteks/cli, covering agent instructions, project overview, tech stack, repository layout and common commands.