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 skills/xiaoluolyg/god/plannpx skills add XiaoLuoLYG/GOD --skill plangit clone --depth 1 https://github.com/XiaoLuoLYG/GODWrote 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/skills/xiaoluolyg/god/plan)<a href="https://agentmods.dev/skills/xiaoluolyg/god/plan"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/plan.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 | $0.00008 | $0.01363 |
| Opus 5 | $0.00004 | $0.00681 |
| Sonnet 5 | $0.00002 | $0.00273 |
| Haiku 4.5 | $0.00001 | $0.00136 |
Grade A, and why
plan 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan
Execute intentions by generating environment actions via codegen.
Activation
Activate this skill when you have an intention to execute.
Dual-Process Decision Making
Human decisions arise from two systems:
System 1: Fast, Habitual
- Triggered by: routine situations, familiar contexts
- Characteristics: quick, automatic, low cognitive load
- Output: single-step action, no plan_state needed
- Use when:
- Routine activity (eating, sleeping, commuting)
- Time pressure
- Low stakes
- Strong habit exists
System 2: Deliberate, Planned
- Triggered by: novel situations, complex goals, conflicts
- Characteristics: slow, analytical, requires attention
- Output: multi-step plan_state.json
- Use when:
- New or unfamiliar goal
- Multiple steps required
- High stakes or uncertainty
- Conflicting options
System Selection
| Condition | System |
|---|---|
| Routine time + routine action | System 1 |
| Familiar location + known action | System 1 |
| New intention + complex goal | System 2 |
| Multiple options + uncertainty | System 2 |
| Urgent need + known solution | System 1 |
| Conflict detected | System 2 |
Input Files
| File | Use |
|---|---|
state/intention.json |
Current goal |
state/observation.txt |
Environment context |
state/plan_state.json |
Ongoing multi-step plan |
Output Files
state/plan_state.json
{
"goal": "Buy groceries at the supermarket",
"steps": ["walk to supermarket", "enter store", "pick items", "pay"],
"current_step": 1,
"started_tick": 42,
"status": "in_progress",
"decision_mode": "system2",
"estimated_ticks": 4
}
Single-Step Actions (System 1)
Most routine intentions execute in one codegen call:
{
"tool_name": "codegen",
"arguments": {
"instruction": "Move to the café on Main Street.",
"ctx": {}
}
}
No plan_state.json needed for single-step actions.
Multi-Step Plans (System 2)
For complex goals, maintain state/plan_state.json:
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 · 220 lines · 8 tokens per session scan A be151e8c0370
plan is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,096 stars, last pushed 7d ago), licensed Apache-2.0. It adds 8 tokens to every session and 1,363 once invoked, about $0.0000 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 skills, from other repositories
game-design-theory
Comprehensive game design theory covering MDA framework, player psychology, balance principles, and progression systems. Master why games are fun.
team
AgentWorld development team. Trigger: '组建团队', '开会', '/team', 'build team', 'review architecture'.
javascript-testing-patterns
Implement comprehensive testing strategies using Jest, Vitest, and Testing Library for unit tests, integration tests, and end-to-end testing with mocking, fixtures, and test-driven development. Use when writing JavaScript/TypeScript tests, setting up test infrastructure, or implementing TDD/BDD workflows.
monorepo-management
Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.
typescript-advanced-types
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.