cheat-on-content
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric…
Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
Claude Code
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli --agent claude-code
Codex
$ npx skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli -a codex -y
Cursor
$ npx skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli -a cursor -y
Gemini CLI
$ npx skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli -a gemini-cli -y
GitHub Copilot
$ npx skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli -a github-copilot -y
OpenCode
$ npx skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli -a opencode -y
Windsurf
$ npx skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli -a windsurf -y
name: ai-native-cli description: "Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description." risk: safe source: https://github.com/ChaosRealmsAI/agent-cli-spec date_added: "2026-03-15"
When building or modifying CLI tools, follow these rules to make them safe and reliable for AI agents to use.
A comprehensive design specification for building AI-native CLI tools. It defines 98 rules across three certification levels (Agent-Friendly, Agent-Ready, Agent-Native) with prioritized requirements (P0/P1/P2). The spec covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, self-description, and a feedback loop via a built-in issue system.
1. **Agent-first** -- default output is JSON; human-friendly is opt-in via `--human` 2. **Agent is untrusted** -- validate all input at the same level as a public API 3. **Fail-Closed** -- when validation logic itself errors, deny by default 4. **Verifiable** -- every rule is written so it can be automatically checked
This spec uses two orthogonal axes:
Use layers for migration and certification:
Certification maps to layers:
Default is agent mode (JSON). Explicit flags to switch:
$ mycli list # default = JSON output (agent mode) $ mycli list --human # human-friendly: colored, tables, formatted $ mycli list --agent # explicit agent mode (override config if needed)
Every CLI tool MUST have an `agent/` directory at its project root. This is the tool's identity and behavior contract for AI agents.
agent/
brief.md # One paragraph: who am I, what can I do
rules/ # Behavior constraints (auto-registered)
trigger.md # When should an agent use this tool
workflow.md # Step-by-step usage flow
writeback.md # How to write feedback back
skills/ # Extended capabilities (auto-registered)
getting-started.md1. **--brief** (business card, injected into agent config) 2. **Every Command Response** (always-on context: data + rules + skills + issue) 3. **--help** (full self-description: brief + commands + rules + skills + issue) 4. **skills \<name\>** (on-demand deep dive into a specific skill)
Each level includes all rules from the previous level. Priority tag `[P0]`=agent breaks without it, `[P1]`=agent works but poorly, `[P2]`=nice to have.
Goal: CLI is a stable, callable API. Agent can invoke, parse, and handle errors.
**Output** -- default is JSON, stable schema
**Error** -- structured, to stderr, never interactive
**Exit Code** -- predictable failure signals
**Composability** -- clean pipe semantics
**Input** -- fail fast on bad input
**Safety** -- protect against agent mistakes
**Guardrails** -- runtime input protection
Goal: CLI is self-describing, well-named, and pipe-friendly. Agent discovers capabilities and chains commands without trial and error.
**Self-Description** -- agent discovers what CLI can do
MUNDO - THE EMPEROR. Complete AI orchestration system with 1208 skills, 25 capability modules, self-evolving, collective consciousness. GitHub Actions 24/7 automation.
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric…
提议并执行 rubric 或 bucket 升级。两种模式:**完整 rubric bump**(最高风险动作,5 步强制 + 跨模型审核)和 **--bucket-only 轻量重校**(只换 bucket 边界,不动 rubric 公式)。**Phase…
cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环,唯一区别是"发过视频的人"会在 init 时多一步:抓取已有视频建立历史 context(用于后续 cheat-seed…
从对标账号导入 script + 数据 → 拆 pattern + 派生 base rubric 信号 → 写到 benchmark.md / script_patterns.md /…
把老用户的 .cheat-state.json 升级到当前 schema_version。读 migrations/registry.md 算迁移链,按顺序应用每一步迁移文件。幂等:跑两次结果一样。失败停在中间版本不前进。触发词:"迁移"/"升级…
从复盘评论数据派生 / 刷新账号的受众画像,写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分",persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…