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.
git clone --depth 1 https://github.com/clxzl/claude-code-best-practice-cnWrote 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/commands/clxzl/claude-code-best-practice-cn/time-orchestrator)<a href="https://agentmods.dev/commands/clxzl/claude-code-best-practice-cn/time-orchestrator"><img src="https://agentmods.dev/badge/commands/clxzl/claude-code-best-practice-cn/time-orchestrator.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 5.1 | $0.00000 | $0.00584 |
| Opus 5 | $0.00000 | $0.00292 |
| Sonnet 5 | $0.00000 | $0.00117 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
time-orchestrator 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 8d 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.
What it actually says
时间编排器命令
获取迪拜的当前时间（Asia/Dubai，UTC+4）并创建一个可视化的 SVG 时间卡片。
工作流
步骤 1：获取当前迪拜时间
使用 Agent 工具调用 time agent：
- subagent_type: time-agent
- description: 获取当前迪拜时间
- prompt: 获取迪拜（Asia/Dubai，UTC+4）的当前时间。返回三个字段：
time（时间部分，例如 "14:30:45"）、timezone（"GST (UTC+4)"）和formatted（完整格式化字符串，例如 "2026-03-12 14:30:45 +04"）。该 agent 有一个预加载的 skill（time-fetcher）提供详细指令。 - model: haiku
等待 agent 完成并捕获返回的时间数据。
数据契约
time-agent 必须返回以下三个字段：
- time：时间部分（例如 "14:30:45"）
- timezone："GST (UTC+4)"
- formatted：完整格式化字符串（例如 "2026-03-12 14:30:45 +04"）
步骤 2：创建 SVG 时间卡片
使用 Skill 工具调用 time-svg-creator skill：
- skill: time-svg-creator
- args: 传递步骤 1 中的时间数据 — 包括
time、timezone和formatted值
该 skill 将使用步骤 1 中的时间数据（在当前上下文中可用）来创建 SVG 卡片并写入输出文件。
关键要求
- 使用 Agent 工具调用 time-agent：不要使用 bash 命令调用 agent。你必须使用 Agent 工具并设置
subagent_type: "time-agent"。 - 使用 Skill 工具调用 SVG 创建器：通过 Skill 工具使用
skill: "time-svg-creator"调用 SVG 创建器，而不是 Agent 工具。 - 顺序流程：Agent 必须完成并返回时间数据后才能调用 skill。不要并行运行。
- 数据传递：确保 agent 响应中的三个字段（time、timezone、formatted）在调用 skill 时在上下文中可用。
输出摘要
两个步骤都完成后，向用户提供清晰的摘要，显示：
- 获取到的当前迪拜时间
- 时区：GST (UTC+4)
- 完整格式化时间戳
- SVG 卡片已创建于
agent-teams/output/dubai-time.svg - 摘要已写入
agent-teams/output/output.md
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.
- 8d ago First seen · 51 lines · 0 tokens per session scan A fd9ce638f60e
time-orchestrator is a command published in the GitHub repository clxzl/claude-code-best-practice-cn (127 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 584 tokens. 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.