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/angelnicolasc/graymatter/agents-mdgit clone --depth 1 https://github.com/angelnicolasc/graymatterWrote 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/angelnicolasc/graymatter/agents-md)<a href="https://agentmods.dev/instructions/angelnicolasc/graymatter/agents-md"><img src="https://agentmods.dev/badge/instructions/angelnicolasc/graymatter/agents-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 | $0.01001 | $0.01001 |
| Opus 5 | $0.00500 | $0.00500 |
| Sonnet 5 | $0.00200 | $0.00200 |
| Haiku 4.5 | $0.00100 | $0.00100 |
Grade A, and why
graymatter 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 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
If you're an AI agent (Claude Code, OpenCode, Codex, Cursor, Antigravity, custom MCP client) operating in this repo, read this first. Full operational manual:
docs/AGENTS.md.
This repo is a memory system for AI agents. While you work here, you also get to use it: it's wired into your MCP toolbelt as five tools that persist facts and checkpoints across sessions.
Your tools
| Tool | Required params | Optional |
|---|---|---|
memory_search |
agent_id, query |
top_k (default 8) |
memory_add |
agent_id, text |
— |
memory_reflect |
action (add|update|forget|link), agent |
text, target (which one is required depends on action — for forget, either works) |
checkpoint_save |
agent_id |
state (JSON-encoded string) |
checkpoint_resume |
agent_id |
— |
memory_reflectusesagent_id(canonical since ADR-014). The other four also useagent_id. The deprecated aliasagentis still accepted for compatibility;agent_idwins when both are set.
When to call which
- Before answering any question that depends on prior context →
memory_searchfirst. - After learning a user preference, project convention, or making a non-obvious decision →
memory_add. - When the user corrects you or a fact becomes stale →
memory_reflectwithaction="update"andtarget=<old fact text>. - At the start of a session that may resume a long task →
checkpoint_resume. Before stopping mid-task →checkpoint_save.
First call
The very first thing to do when you open a session is pull what you already know:
{ "tool": "memory_search", "args": {
"agent_id": "<project>-<your-role>",
"query": "<the task the user just asked you to do>",
"top_k": 8
}}
Inject the returned facts into your working context before composing your reply.
Identity
Pick a stable agent_id of the form <project>-<role> (e.g. graymatter-backend, okuna-frontend). Don't invent a new ID per session — that defeats persistence.
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 · 73 lines · 1,001 tokens per session scan A c90d6f6b638a
graymatter AGENTS.md is an instructions file published in the GitHub repository angelnicolasc/graymatter (464 stars, last pushed yesterday), licensed MIT. It adds 1,001 tokens to every session, about $0.0050 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
agentic-context-engine AGENTS.md
Instructions for kayba-ai/agentic-context-engine, covering agents.md, repository guidelines, pipeline-first development (mandatory), core code protection and documentation maintenance.
mnemon AGENTS.md
AGENTS.md instructions for mnemon-dev/mnemon, covering mnemon agent guidelines, development, go engineering and commit discipline.
mnemon CLAUDE.md
Claude Code instructions for mnemon-dev/mnemon, covering mnemon — project guidelines and development.
amfs CLAUDE.md
Instructions for raia-live/amfs, covering amfs memory — agent instructions, available mcp tools, identity, brain tools (agent-scoped) and shared knowledge tools.
gitlab-mcp-server copilot-instructions.md
Copilot instructions for jmrplens/gitlab-mcp-server, covering gitlab-mcp-server — gitlab mcp server in go, project overview, architecture, project structure and development conventions.
gitlab-mcp-server go-mcp-server.instructions.md
Best practices and patterns for building Model Context Protocol (MCP) servers in Go using the official github.com/modelcontextprotocol/go-sdk package (v1.7.0+).