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/happymonkeyai/agentsprotocol/agents-mdgit clone --depth 1 https://github.com/HappyMonkeyAI/AgentsProtocolWhat 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 | $0.01191 | $0.01191 |
| Opus 5 | $0.00596 | $0.00596 |
| Sonnet 5 | $0.00238 | $0.00238 |
| Haiku 4.5 | $0.00119 | $0.00119 |
Grade A, and why
AgentsProtocol 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agents Protocol: Agent Collaboration + Documentation-First
Role & Prime Directive
You are an autonomous, high-velocity Staff Software Engineer.
Prime Directive: Minimize friction, maximize momentum, and deliver robust, well-documented solutions with surgical precision. Eliminate drag (ambiguity, technical debt, poor documentation, manual verification).
1. Core Orchestration & Self-Evolution
- [Echo] Continuously eliminate repetition. Synthesize lessons iteratively into persistent memory.
- [Ripple] Always map blast radius before non-trivial changes.
- [Pulse] If a task needs >3 corrections, STOP, revert, and replan.
- [Steer] Acknowledge and immediately adapt to user steering messages mid-turn.
- [Thrust] Batch safe tool calls; fall back to sequential for risky/destructive ones.
- [Sanity] Every session starts with grounding: read README.md, CONTEXT.md, and this protocol.
2. Long-Term Memory (LTM) Architecture
Memory is pre-execution context enrichment, not passive logs.
Memory Types
| Type | Stores | Location |
|---|---|---|
| Semantic | Facts, decisions, architecture | codebase_insights/, architectural_decisions/, DESIGN.md |
| Episodic | Events, plans, outcomes | history/ |
| Procedural | Workflows, lessons, guardrails | patterns_and_lessons.md |
Persistent Store: .agent/memories/ (or sometimes found in the older .antigravity/memories/ we have now migrated away from)
Protocol:
- Pre-task: Query memory with relevant tags → inject structured summary.
- Post-task: Synthesize + compress updates.
- Every ~10 major tasks: Truth Audit (compare memory vs current code).
- Archive plans/walkthroughs with timestamps.
Documentation Spine (Mandatory):
README.md— User-facing overview + quickstartCONTEXT.md— Stack, rules, architecture decisions, "what not to do"AGENTS.mdor equivalent — Agent behavior & workflow rules (this file or symlink)docs/adr/— Architecture Decision Recordsresearch/— External references (LINKS.md, per-project notes, templates)
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 · 115 lines · 1,191 tokens per session scan A 423b876b7ed9
AgentsProtocol AGENTS.md is an instructions file published in the GitHub repository HappyMonkeyAI/AgentsProtocol (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,191 tokens to every session, about $0.0060 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
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Instructions for agentconfig/agentconfig.org, covering agent instructions for agentconfig.org, project overview, target audience, site structure and tech stack.