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-find-product-remind)<a href="https://agentmods.dev/commands/an8079/take-skills/takes-find-product-remind"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-find-product-remind/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-find-product-remind"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-find-product-remind.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 | $0.00022 | $0.00413 |
| Opus 5 | $0.00011 | $0.00206 |
| Sonnet 5 | $0.00004 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
find-product-remind 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 12d 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
/find-product-remind - 技能推荐
与用户访谈需求，在 lenny-skills 搜索适合的技能并推荐安装。
使用方式
/find-product-remind
或
推荐技能
安装技能
找找技能
工作流程
- 需求访谈 - 与用户讨论当前项目需要的技能
- 技能搜索 - 在 lenny-skills 仓库搜索适合的技能
- 推荐列表 - 给用户提供推荐列表
- 用户选择 - 用户选择要安装的技能
- 下载安装 - 从 GitHub 下载并安装到项目
技能来源
- lenny-skills: https://github.com/RefoundAI/lenny-skills
- 官方技能: Claude Code 内置技能
- 社区技能: 其他开源技能
安装位置
| 位置 | 说明 |
|---|---|
skills/ |
项目本地技能（推荐） |
~/.claude/skills/ |
全局技能（所有项目可用） |
输出内容
| 内容 | 说明 |
|---|---|
| 需求分析 | 用户需求的总结 |
| 技能推荐 | 推荐的技能列表（1-5个） |
| 技能说明 | 每个技能的功能和用途 |
| 安装确认 | 用户确认后显示安装结果 |
用户同意机制
必须用户确认后才进行下载安装：
- 显示推荐列表
- 用户选择要安装的技能
- 确认后开始下载安装
提示： 使用 /find-product-remind 可以帮助你快速找到适合项目需求的技能，提高开发效率。
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.
- 12d ago First seen · 64 lines · 22 tokens per session scan A 5005616e483c
find-product-remind is a command published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 413 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.
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.