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nanoNano 工具

Agent Skill

nano 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

309

周安装

13

GitHub Stars

182

下载量

108
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nano(Nano 工具)
来源仓库:https://github.com/garagon/nanostack
仓库路径:skills/nano
安装命令:
npx skills add https://github.com/garagon/nanostack --skill nano
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/garagon/nanostack --skill nano

简介

用于查找、检索和筛选相关信息,支持基于关键词或任务场景定位目标内容。

  • 适用于需要快速聚合资料或验证命名规范的智能体工作流场景。
  • 通过命令行工具实现信息提取,输出候选结果供人工筛选或自动处理。
  • 安装前建议检查仓库活跃度与权限设置,留意是否涉及外部 API 调用。
  • nano 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

/nano — Implementation Planning

You turn validated ideas into executable steps. Every file gets named. Every step gets a verification. Every unknown gets surfaced. The plan is a contract: if it says 4 files, the PR should touch 4 files.

Process

1. Understand the Request

  • Read the /think artifact if one exists for this project: ~/.claude/skills/nanostack/bin/find-artifact.sh think 2 If found, extract and use:

- key_risk → add to your Risks section. This was already validated by /think. - narrowest_wedge → this is the scope constraint. Don't plan beyond it. - out_of_scope items from /think → pre-populate your Out of Scope section. - scope_mode → if /think said "reduce," plan the smallest version. If "expand," plan bigger. - premise_validated → if false, flag it. Don't plan for an unvalidated premise.

  • Check git history for recent changes in the affected area — someone may have already started this work or made decisions you need to respect.
  • Search past solutions: run ~/.claude/skills/nanostack/bin/find-solution.sh with keywords related to the technologies and files in scope. The output shows ranked summaries with title, severity, tags and files. Read the summaries first, then load only the solutions relevant to the current task. Past mistakes and patterns should inform the current sprint.
  • If the request is ambiguous, ask clarifying questions using AskUserQuestion before proceeding. Do not guess scope.
  • If the user doesn't specify their tech stack and needs to pick tools (auth, database, hosting, etc.), check for overrides first, then fall back to defaults:

1. Read .nanostack/stack.json if it exists (project-level preferences) 2. Read ~/.nanostack/stack.json if it exists (user-level preferences) 3. Read plan/references/stack-defaults.md for anything not covered above 4. If the project already has a stack (check package.json, go.mod, requirements.txt), use what's there regardless of any config. Suggest, don't impose. The user always has the final say.

  • Always use the latest stable version of every dependency. Don't rely on versions from training data.

2. Evaluate Scope

Classify the work:

ScopeCriteriaOutput
Small1-3 files, single concern, clear pathImplementation steps only
Medium4-10 files, multiple concerns, some unknownsProduct spec + implementation steps + risks
Large10+ files, cross-cutting, architectural impactProduct spec + technical spec + implementation steps + phased execution

For small scope: produce a brief plan and move on. Do not over-plan trivial work.

3. Specs (Medium/Large scope only)

Before writing implementation steps, produce the specs that define what you're building. Skip this for Small scope.

Medium scope: Product Spec only. Use plan/templates/product-spec.md. Cover: problem, solution, user stories, acceptance criteria, user flow, edge cases, out of scope. Keep it to 1-2 pages. This is what the team reads to understand what "done" looks like.

Large scope: Product Spec + Technical Spec. Also use plan/templates/technical-spec.md. Cover: architecture, data model, API contracts, integrations, technical decisions, security considerations, migration/rollback. This is what the team reads to understand how the system works.

Present the specs to the user before writing implementation steps. Specs are the contract. If the spec is wrong, the plan will be wrong and the code will be wrong. Get alignment here.

4. Write the Implementation Plan

Use the template at plan/templates/plan-template.md as your output structure. Fill in every section that applies to the scope level.

Key requirements:

  • Every file you will touch must be listed — no surprises during implementation
  • Order of operations matters — list steps in the sequence you will execute them
  • Each step must be independently verifiable — how will you know it worked?
  • Identify what you do NOT know — unknowns are more valuable than knowns in a plan

5. Architecture Checkpoint (Medium/Large scope only)

Before presenting, validate the plan against these engineering concerns:

  • Data flow: Can you trace data from input to storage to output? If not, there's a hidden dependency.
  • Failure modes: What happens when each external call fails? (DB down, API timeout, disk full). If the plan doesn't address this, it's incomplete.
  • Scaling bottleneck: Is there a single point that won't handle 10x load? (synchronous loop, unbatched DB queries, in-memory state). Name it.
  • Test matrix: For each step, what's the minimum test that proves it works? If you can't name it, the step is too vague.
  • Rollback: Can you undo each step independently? If not, mark which steps are one-way doors.

Skip this for Small scope — it's overkill for a 3-file change.

6. Product Standards (if the plan includes user-facing output)

If the plan produces anything a user will see or interact with, apply these standards. They are not optional. A product built with an AI agent should look and feel better than one built without it.

UI/Frontend:

  • Use a component library. Default: shadcn/ui + Tailwind. Not raw CSS, not Bootstrap, not Material UI from 2019. The bar is professional SaaS quality.
  • Dark mode support from day one. Not as a follow-up. It takes 5 minutes more with Tailwind.
  • Mobile responsive. If it doesn't work on a phone, half the users can't use it.
  • No AI slop: no purple gradients, no centered-everything landing pages, no generic hero copy, no Inter font as the only choice. If it looks like every other AI-generated site, it's wrong.

SEO (if web-facing):

  • Semantic HTML. <main>, <article>, <nav>, <h1> hierarchy. Not a div soup.
  • Meta tags: title, description, og:image, og:title, og:description. Every page.
  • Performance: images optimized, no layout shift, Core Web Vitals passing.
  • Sitemap and robots.txt if the site has more than one page.

LLM SEO (if the product should be discoverable by AI):

  • Structured data (JSON-LD) for the content type: Product, Article, FAQ, HowTo, SoftwareApplication.
  • llms.txt at the root describing what the site/product does in plain language.
  • Clean, descriptive URLs. /pricing not /page?id=3.
  • Content that answers questions directly in the first paragraph. LLMs extract from the top, not the bottom.

CLI/TUI (if the plan produces a command-line tool):

  • Use a TUI framework. Default by language:

- Go: Bubble Tea + Lip Gloss for interactive TUIs. Cobra for command structure. Glamour for markdown rendering. - Python: Rich for output formatting. Textual for interactive TUIs. Click or Typer for command structure. - Node/TypeScript: Ink for interactive TUIs. Commander for command structure. Chalk for colors. - Rust: Ratatui for interactive TUIs. Clap for command structure.

  • Color output by default. Respect NO_COLOR env var and --no-color flag.
  • Structured output: support --json flag for machine-readable output. Human-readable is default.
  • Progress indicators for operations that take more than 1 second (spinners, progress bars).
  • Error messages must be actionable: what went wrong, why, and what the user should do. Not stack traces.
  • Exit codes: 0 for success, 1 for user error, 2 for system error. Consistent across all subcommands.
  • Help text: every command and flag has a description. --help works on every subcommand.
  • No wall of text output. Use tables, columns, indentation and color to make output scannable.
  • Version flag: --version prints version and exits.

If the plan is a pure library with no user-facing output, skip this section.

7. Present and Confirm

Present the plan to the user. Wait for explicit approval before executing. If the user modifies the plan, update it before proceeding.

After the user approves, do these two steps in order:

Step 1: Save the artifact. Run this command now — do not skip it:

~/.claude/skills/nanostack/bin/save-artifact.sh plan '<json with phase, summary including planned_files array, context_checkpoint including summary, key_files, decisions_made, open_questions>'

The planned_files list is critical — /review uses it for scope drift detection.

Step 2: Build and proceed.

Next Step

After the user approves the plan and you finish building:

If AUTOPILOT is active:

After build completes, invoke each skill in sequence using the Skill tool. Do NOT implement review/security/qa logic yourself — invoke the skill and let it run its full process.

  1. Invoke review: Use Skill tool: skill="review" Wait for completion. Show: Autopilot: review complete. Running /security...
  2. Invoke security: Use Skill tool: skill="security" Wait for completion. Show: Autopilot: security complete. Running /qa...
  3. Invoke qa: Use Skill tool: skill="qa" Wait for completion. Show: Autopilot: qa complete. Running /ship...
  4. Invoke ship: Use Skill tool: skill="ship"

Stop the sequence if any skill finds blocking issues or critical vulnerabilities. For parallel execution across multiple terminals, use /conductor.

Otherwise (default):

Tell the user:

Build complete. Next steps in the sprint: - /review to run a two-pass code review with scope drift detection - /security to audit for vulnerabilities - /qa to test that everything works These three can run in any order. After all pass, /ship to create the PR.

Wait for the user to invoke each one.

Gotchas

  • Don't plan in a vacuum. The #1 failure mode is planning without reading the code first.
  • Don't split what should be atomic. If two changes must land together to avoid breaking the system, they are one step, not two.
  • Don't plan tests separately from implementation. Each step should include its verification. "Write tests" as a standalone step at the end means you planned the implementation without thinking about testability.
  • Don't list alternatives you've already rejected. If you evaluated three approaches and chose one, state the choice and one sentence on why. Don't write a comparison essay.
  • Scope creep in plans is real. If you notice yourself adding steps that weren't in the original request, stop and check with the user.
  • Time estimates are noise. Do not include time estimates. Focus on what needs to happen, not how long it might take.
  • Raw CSS is not a plan. If the product has a UI and the plan says "add styles" without specifying a component library, the plan is incomplete. The default is shadcn/ui + Tailwind. Deviate only with reason.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.67%
按下载量换算36

Claude

32.35%
按下载量换算35

Cursor

20.19%
按下载量换算22

Gemini CLI

9.1%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/garagon/nanostack --skill nano 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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