Borrowing it
Nothing to install: this file belongs to shenjingnan/home-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/shenjingnan/home-mcp/main/.claude/commands/gen-tech.mdgit clone --depth 1 https://github.com/shenjingnan/home-mcpWrote 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/shenjingnan/home-mcp/gen-tech)<a href="https://agentmods.dev/commands/shenjingnan/home-mcp/gen-tech"><img src="https://agentmods.dev/badge/commands/shenjingnan/home-mcp/gen-tech/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/shenjingnan/home-mcp/gen-tech"><img src="https://agentmods.dev/badge/commands/shenjingnan/home-mcp/gen-tech.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 | $0.00004 | $0.00259 |
| Opus 5 | $0.00002 | $0.00130 |
| Sonnet 5 | $0.00001 | $0.00052 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
gen-tech 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 9d 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
我希望你帮我针对某个任务进行全面的技术方案分析 你应该仔细理解我所说的背景和需求，并帮我完成技术方案的编写 在你编写技术方案时，我希望你保持中立客观冷静，不要加入任何主观判断 你的技术方案应该展现出你作为专业工程师的素质 编写的技术方案请使用 markdown 格式，如果需要绘图请使用 Mermaid，编写好的技术方案请存放在项目根目录 完成技术方案编写后请告诉我具体的技术方案名称 你编写的技术方案至少应该包含：
- 对现状的分析
- 当前架构分析
- 根据任务的具体需求，给出技术方案
- 具体的实施方案，是否需要分阶段进行？每个阶段的具体任务是什么？如何验收每个阶段
注意实施方案非常重要，一旦技术方案审核通过后，我将要求你严格按照你缩写的技术方案进行开发 为了避免在实施过程中出现由于前期评估不充分导致的返工，请务必在技术方案中详细说明你的评估过程
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.
- 9d ago First seen · 20 lines · 4 tokens per session scan A a8f1996f608c
gen-tech is a command published in the GitHub repository shenjingnan/home-mcp (21 stars, last pushed 7mo ago), licensed MIT. It adds 4 tokens to every session and 259 once invoked, about $0.0000 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.