What is opencode-scheduler AGENTS.md?

A guide to building applications where AI agents can perform the same actions as users and combine small tools to complete larger tasks. It explains principles such as making tools small, reusable, and available wherever the user interface offers an action.

Use it when designing agent tools, user interfaces, or workflows. It helps map user actions to agent actions and decide how much responsibility each tool should have.

It helps teams avoid putting inflexible, task-specific logic into tools. Instead, an agent can decide how to combine basic actions for different requests.

opencode-scheduler AGENTS.md — Instructions file by different-ai

Install

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.

agentmods

npx agentmods add instructions/different-ai/opencode-scheduler/agents-md

Clone the repo

git clone --depth 1 https://github.com/different-ai/opencode-scheduler

Made for: Codex, OpenCode.

Per session 1,841 This file is loaded in full into every session.

When invoked 1,841 The same file — it is already loaded in full.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.01841	$0.01841
Opus 5	$0.00920	$0.00920
Sonnet 5	$0.00368	$0.00368
Haiku 4.5	$0.00184	$0.00184

Measured 2d ago against content hash 2d45e2d6971d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

opencode-scheduler 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.

AGENTS.md · 348 lines

How it starts

The opening of the file, as written. The whole thing — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent-Native Architecture: Implementation Guide

A practical reference for building applications where agents are first-class citizens.

Core Principles

1. Parity

Whatever the user can do through the UI, the agent must be able to achieve through tools.

Create a capability map:

User Action	Agent Method
Create item	`write_file` or `create_item` tool
Update item	`update_file` or `update_item` tool
Delete item	`delete_file` or `delete_item` tool
Search	`search_files` or `search` tool

Test: Pick any UI action. Can the agent accomplish it?

2. Granularity

Prefer atomic primitives. Features are outcomes achieved by an agent in a loop.

# Wrong - logic in code
Tool: classify_and_organize_files(files)

# Right - agent decides
Tools: read_file, write_file, move_file, list_directory, bash
Prompt: "Organize downloads by content and recency"

Test: To change behavior, do you edit prose or refactor code?

3. Composability

New features = new prompts (when tools are atomic and parity exists).

Prompt: "Review files modified this week. Summarize changes. Suggest three priorities."

No code written. Agent uses list_files, read_file, and judgment.

4. Emergent Capability

Agent can accomplish things you didn't explicitly design for.

Build atomic tools → Users ask unexpected things → Agent composes solutions → You observe patterns → Optimize common patterns → Repeat.

Tool Design

Domain Tools

Add when needed for:

Vocabulary anchoring - create_note teaches "note" concept better than "write file with format"
Guardrails - Validation that shouldn't be left to agent judgment
Efficiency - Common multi-step operations

Rule: One conceptual action per tool. Judgment stays in prompts.

# Wrong
analyze_and_publish(input)  # bundles judgment

# Right
publish(content)  # one action, agent decided what to publish

Read the full file on GitHub · 348 lines

Changes

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 · 348 lines · 1,841 tokens per session scan A 2d45e2d6971d

Subscribe to this mod's changes

opencode-scheduler AGENTS.md is an instructions file published in the GitHub repository different-ai/opencode-scheduler (479 stars, last pushed 6mo ago), licensed MIT. It adds 1,841 tokens to every session, about $0.0092 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.

Planning

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

opencode-scheduler AGENTS.md