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 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
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.
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill ai-native-cli --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/ai-native-cliContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
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.
Repo: LiHongwei-cn/lihongwei-cn
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 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
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 2 强制走 cheat-score-blind…
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 给更贴合的选题、更准的…
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 / rubric_notes.md。**这是工具最早期信号的来源**——cold-start…
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 state"/"migrate"/"我的 state…
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 含实绩信号,cheat-score-blind…