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/peakflo/20x/agents-mdgit clone --depth 1 https://github.com/peakflo/20xWhat 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 | $0.06580 | $0.06580 |
| Opus 5 | $0.03290 | $0.03290 |
| Sonnet 5 | $0.01316 | $0.01316 |
| Haiku 4.5 | $0.00658 | $0.00658 |
Grade A, and why
20x 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 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 — 594 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Multi-Agent Architecture (Implemented)
This document describes the production multi-agent system powering 20x. The architecture spans three agent backends, a centralized polling coordinator, skill management, auto-triage, secret management, heartbeat monitoring, and enterprise sync.
Overview
20x supports running multiple AI coding agents in parallel, each working on assigned tasks within specific codebases. Agents are managed through adapter interfaces and interact with users via streaming transcripts with human-in-the-loop (HITL) approval flows. Three backend adapters are supported: OpenCode SDK, Claude Code, and Codex (ACP).
Agent Model
Each agent is a persistent configuration stored in SQLite:
interface AgentRecord {
id: string // cuid2
name: string // e.g. "Backend Agent", "Frontend Agent"
server_url: string // Default: 'http://localhost:4096'
config: AgentConfigRecord // stored as JSON
is_default: boolean // one agent is pre-seeded on first launch
created_at: string
updated_at: string
}
interface AgentConfigRecord {
coding_agent?: 'opencode' | 'claude-code' | 'codex'
model?: string
auth_method?: 'subscription' | 'api_key'
permission_mode?: 'ask' | 'allow'
system_prompt?: string
mcp_servers?: Array<string | AgentMcpServerEntry>
skill_ids?: string[]
secret_ids?: string[]
api_keys?: {
openai?: string
anthropic?: string
}
}
interface AgentMcpServerEntry {
serverId: string
enabledTools?: string[]
}
A default agent is seeded on first launch with sensible defaults.
Database Schema
CREATE TABLE agents (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
server_url TEXT NOT NULL DEFAULT 'http://localhost:4096',
config TEXT NOT NULL DEFAULT '{}',
is_default INTEGER NOT NULL DEFAULT 0,
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
);
-- Tasks table has agent_id for assignment:
ALTER TABLE tasks ADD COLUMN agent_id TEXT REFERENCES agents(id) ON DELETE SET NULL;
-- Skills table stores reusable instructions:
CREATE TABLE skills (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
description TEXT NOT NULL,
content TEXT NOT NULL,
version INTEGER NOT NULL DEFAULT 1,
confidence REAL NOT NULL DEFAULT 0.5,
uses INTEGER NOT NULL DEFAULT 0,
last_used TEXT,
tags TEXT NOT NULL DEFAULT '[]',
is_deleted INTEGER NOT NULL DEFAULT 0,
enterprise_skill_id TEXT DEFAULT NULL,
uses_at_last_sync INTEGER NOT NULL DEFAULT 0,
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
);
-- Secrets table for encrypted env vars:
CREATE TABLE secrets (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
description TEXT NOT NULL DEFAULT '',
env_var_name TEXT NOT NULL UNIQUE,
value BLOB NOT NULL,
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
);
-- MCP servers table:
CREATE TABLE mcp_servers (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
type TEXT NOT NULL DEFAULT 'local',
command TEXT NOT NULL DEFAULT '',
args TEXT NOT NULL DEFAULT '[]',
url TEXT,
headers TEXT NOT NULL DEFAULT '{}',
environment TEXT NOT NULL DEFAULT '{}',
tools TEXT NOT NULL DEFAULT '[]',
oauth_metadata TEXT NOT NULL DEFAULT '{}',
source TEXT NOT NULL DEFAULT 'user',
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
);
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 · 594 lines · 6,580 tokens per session scan A b333d6db92b9
20x AGENTS.md is an instructions file published in the GitHub repository peakflo/20x (107 stars, last pushed 2d ago), licensed MIT. It adds 6,580 tokens to every session, about $0.0329 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
vscode chat.instructions.md
Chat feature area coding guidelines.
beads AGENTS.md
AGENTS.md instructions for gastownhall/beads, covering agent instructions, key sections, project scope, pr safety for agents and visual design anti-patterns.
mcp-devtools CLAUDE.md
Instructions for sammcj/mcp-devtools, covering claude.md, commands, build and run, testing and code quality.
AgentEval maf-upgrade-preparation.instructions.md
Instructions for analyzing a new MAF version and producing an upgrade plan BEFORE updating the NuGet package.
agentic-playwright skill-creator.instructions.md
Instructions for idavidov13/agentic-playwright, covering skill creator, communicating with the user, creating a skill, capture intent and interview and research.
AgentEval copilot-instructions.md
Instructions for AgentEvalHQ/AgentEval, covering agenteval - ai coding agent instructions, architecture overview, environment setup, optional: secondary models for comparison and build & test commands.