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/an8079/take-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/an8079/take-skills/takes-office-hours)<a href="https://agentmods.dev/commands/an8079/take-skills/takes-office-hours"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-office-hours/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/an8079/take-skills/takes-office-hours"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-office-hours.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 | $0.00018 | $0.00465 |
| Opus 5 | $0.00009 | $0.00233 |
| Sonnet 5 | $0.00004 | $0.00093 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
office-hours 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 11d 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
/office-hours - 产品审视
gstack 风格的产品审视，评估产品价值和是否应该继续开发。
使用方式
/office-hours
或
产品审视
审视产品
该不该做
工作流程
- 产品价值审视 - 评估产品/功能的价值
- 竞品分析 - 分析市场上的竞品
- 用户价值 - 评估对目标用户的价值
- 开发成本 - 评估时间/资源投入
- 建议输出 - 给出是否该做的建议
审视维度
| 维度 | 问题 | 权重 |
|---|---|---|
| 产品价值 | 解决什么问题？为谁解决？ | 30% |
| 市场需求 | 真的有需求吗？需求强度？ | 25% |
| 竞品对比 | 比现有方案好多少？差异化？ | 20% |
| 实现难度 | 技术可行吗？成本合理吗？ | 15% |
| 商业模式 | 如何变现？变现周期？ | 10% |
输出内容
审视建议报告
| 内容 | 说明 |
|---|---|
| 产品评分 | 1-10 分 |
| 核心价值 | 产品的核心卖点 |
| 目标用户 | 目标用户画像 |
| 竞品分析 | 主要竞品对比 |
| 风险评估 | 主要风险点 |
| 建议 | 强烈推荐/推荐/观望/不推荐 |
调整建议
如果审视后发现 spec 文档有不足之处，给出调整建议：
- 目标用户不够明确？
- 核心价值不突出？
- 竞品分析不到位？
提示： /office-hours 应该在任务开始前使用，帮助你决定是否应该做这个产品。
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.
- 11d ago First seen · 65 lines · 18 tokens per session scan A db56ca1b7e45
office-hours is a command published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 465 once invoked, about $0.0001 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 2026-08-31.
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.