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/TestAny-io/testany-agent-skillsWrote 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/testany-io/testany-agent-skills/runbook-writer)<a href="https://agentmods.dev/commands/testany-io/testany-agent-skills/runbook-writer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/runbook-writer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/testany-io/testany-agent-skills/runbook-writer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/runbook-writer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00041 | $0.00597 |
| Opus 5 | $0.00020 | $0.00298 |
| Sonnet 5 | $0.00008 | $0.00119 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
runbook-writer 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 10d 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
Runbook Writer
启动 Runbook 撰写流程。基于 HLD/LLD 等上游文档，编写生产就绪的运维手册。
使用方式
提供上游文档路径：
$ARGUMENTS
Runbook 在研发流程中的位置
PRD → API Contract → HLD → Guardrails → LLD → Runbook → 生产部署
Runbook 承接 LLD 的设计，输出可直接用于生产部署、故障处理的运维手册。
Runbook 内容范围
- 部署流程：前置检查、部署步骤、验证命令
- 回滚流程：触发条件、回滚步骤、验证方法
- 监控与告警：关键指标、SLO 阈值、告警配置
- 故障处理：常见故障、排查步骤、解决方案
- 值班手册：职责、联系方式、升级路径
工作流程
- Phase 0: 基线收集 - 读取 PRD/HLD/LLD/API Contract/Guardrails
- Phase 1: 上下文准备 - 提取部署、回滚、监控、故障处理约束
- Phase 2: 派发 Writer Subagent - 独立写作（完整上下文传递）
- Phase 3: Spec Compliance Review - 验证是否覆盖上游要求
- Phase 4: Quality Review - 验证可执行性和完整性
- Phase 5: 输出与验证 - 按模板输出最终 Runbook
必需产出
- Runbook 文档（含部署、回滚、监控、故障处理全流程）
- 验证记录（Spec Compliance + Quality Review 通过）
核心原则
- Context 隔离：Subagent 获得新鲜上下文，避免假设污染
- 双阶段审查：Spec compliance 先行，quality 后续
- 证据驱动：所有约束必须来自上游文档
- 可执行优先：每个步骤必须有验证命令，回滚路径必须可操作
请提供 HLD 路径（必需），以及 LLD、API Contract、Guardrails 路径（可选）。
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.
- 10d ago First seen · 54 lines · 41 tokens per session scan A 851227a5df29
runbook-writer is a command published in the GitHub repository TestAny-io/testany-agent-skills (81 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 597 once invoked, about $0.0002 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 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.