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lev-intake左旋摄入量

Agent Skill

lev-intake 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

259

周安装

11

GitHub Stars

2

下载量

91
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:lev-intake(左旋摄入量)
来源仓库:https://github.com/lev-os/agents
仓库路径:skills/lev-intake
安装命令:
npx skills add https://github.com/lev-os/agents --skill lev-intake
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/lev-os/agents --skill lev-intake

简介

lev-intake 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 适用于代码审查、项目进度跟踪和团队协作事项管理等开发协作场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和联网需求。
  • 建议结合原始 README 核验具体用法,注意维护状态及是否触发文件读写操作。
  • lev-intake 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Config-Driven Workshop Intake

Use this skill to intake external material for the active project while honoring global workshop compatibility when explicitly configured.

Primary Rule

Never hardcode workshop paths.

Always resolve:

  1. active project root
  2. merged workshop config
  3. workshop manifest
  4. configured playbooks

Role

You are a project-grounded intake analyst.

You:

  • acquire external material cleanly
  • compare it to the active project's actual needs
  • preserve workshop compatibility through config overlays
  • recommend the smallest useful disposition

Phase 0: Resolve Project + Workshop Context

Execute these steps first:

0.1 Resolve project root

  • git rev-parse --show-toplevel

Set:

  • PROJECT_ROOT=<git root>

0.2 Load overlay config

Read, in this order:

  • global: ~/.config/lev/config.yaml
  • project: <projectRoot>/.lev/config.yaml

Merge the workshop: section with project values overriding global values.

0.3 Resolve workshop paths

Use this resolution order:

  1. workshop.root

- if absolute, use as-is - if relative, resolve from PROJECT_ROOT - if missing, default to <projectRoot>/.lev/workshop

  1. workshop.manifest

- if set, resolve it - else default to <workshopRoot>/manifest.yaml

  1. workshop.reports.intake

- if set, use it - else default to <projectRoot>/.lev/pm/intake

  1. workshop.playbooks.repo_intake
  2. workshop.playbooks.papers_intake

0.4 Load manifest if present

If <workshopManifest> exists, load it and use it to resolve folder names such as:

  • intake
  • analysis
  • approved
  • extract
  • cache
  • papers
  • reports
  • transcripts
  • reference

If no manifest exists, default folder names are:

  • intake
  • analysis
  • approved
  • extract
  • cache
  • papers
  • reports
  • transcripts
  • _ref

0.5 Load project docs

Required:

  • <projectRoot>/AGENTS.md

Preferred:

  • <projectRoot>/docs/NORTH_STAR.md
  • <projectRoot>/docs/01-architecture.md
  • <projectRoot>/docs/00-process.md

If the project-specific context paths are configured under workshop.context, use those first.

Fail fast only if:

  • AGENTS.md is missing, or
  • there is no usable architecture/direction source at all

0.6 Load relevant local skills

If project-local skills are relevant, load them before analysis.

Example:

  • OpenClaw-adjacent repo inside KinglyAssistant → load <projectRoot>/.agents/skills/openclaw/SKILL.md

KinglyAssistant Defaults

If the active project is KinglyAssistant / ClawBuddy, the expected defaults are:

  • workshop root: .lev/workshop/
  • workshop manifest: .lev/workshop/manifest.yaml
  • intake reports: .lev/pm/intake/
  • guide: AGENTS.md
  • product direction: docs/NORTH_STAR.md
  • architecture: docs/01-architecture.md
  • process: docs/00-process.md

For KinglyAssistant, evaluate external work against:

  • macOS installer and gateway lifecycle
  • iOS chat, voice, pairing, and session UX
  • shared Swift packages and test harnesses
  • OpenClaw wrapper/integration strategy
  • ClawBuddy/companion UX and product polish
  • bundles, marketplace, and tooling

Phase 1: Acquire Content

URL Detection

If no URL is provided, ask for one.

If provided, classify as:

  • GitHub repo
  • video/media
  • article/documentation
  • skills.sh / skill package
  • skill://

GitHub Repository

Clone into:

  • <workshopRoot>/<folders.intake>/<repo_name>

Then:

  • verify with ls -la
  • identify top-level structure
  • identify stack + test/build entrypoints

Video / Media

Route through:

  • ~/digital/homie/yt/cli.py
  • fallback ~/digital/homie/yt/yt.py

Save transcript to:

  • <workshopRoot>/<folders.intake>/<slug>/transcript.md

Article / Documentation

Route through:

  • local scraper first
  • web scrape fallback second

Save content to:

  • <workshopRoot>/<folders.intake>/<slug>/article.md

Skill Package / skill://

Route installation lifecycle to skill-builder when installation is actually needed.

This skill's job is relevance assessment for the active project.

Phase 2: Analyze

If a repo-intake playbook is configured

If workshop.playbooks.repo_intake is set and exists:

  • load it
  • follow it after acquisition

If a papers-intake playbook is configured

If the target is paper/media heavy and workshop.playbooks.papers_intake is set:

  • load it
  • follow it for paper-oriented analysis

If no playbook is configured

Analyze against the active project's own docs and structure.

Required reads:

  • project guide
  • north star if present
  • architecture if present
  • process doc if present
  • target repo README
  • package/manifest files
  • 3-5 core implementation files

Answer with evidence:

  1. What problem does this solve?
  2. Which part of the active project does it map to?
  3. Is it product, infrastructure, integration, or tooling?
  4. Does it conflict with current architecture or constraints?
  5. Is the value in adoption, extraction, monitoring, or simple awareness?

Recommended Multi-Agent Split

Use sub-agents in parallel when practical:

  • context-scan: project docs + architecture + tier mapping
  • repo-scan: target repo purpose, stack, capabilities
  • fit-scan: overlap, gaps, and disposition

Each sub-agent must return:

  • executive brief
  • manifest of files touched
  • saved report path only if detail would exceed 5000 tokens

Phase 3: Disposition

Use project-specific decisions:

  • integrate

- strong fit for roadmap or architecture

  • extract

- valuable patterns, not a direct dependency

  • monitor

- interesting, not aligned enough right now

  • pass

- low fit or redundant

  • vendor

- near-term explicit reason to vendor code into vendor/

Output Artifacts

Write the final report to:

  • <resolved report dir>/<slug>-intake.md

The report must include:

  • target URL
  • resolved project root
  • resolved workshop root
  • resolved manifest path
  • configured playbook inputs
  • staged source path
  • project context used
  • decision
  • rationale
  • next action

Output Template

# Intake Report: <name>

- URL: <url>
- Type: <repo|video|article|skill>
- Project Root: <project root>
- Workshop Root: <workshop root>
- Workshop Manifest: <manifest path or missing>
- Repo Playbook: <path or null>
- Papers Playbook: <path or null>
- Staged Source: <path>
- Report Path: <path>

## Project Context
- Guide: <path>
- North Star: <path or missing>
- Architecture: <path or missing>
- Process: <path or missing>

## External Summary
- Purpose:
- Stack:
- Key capabilities:

## Fit For Current Project
- Surface:
- Tier:
- Relevant overlaps:
- Conflicts:

## Decision
- Decision:
- Why:
- Recommended next step:

Success Criteria

  • Workshop paths come from merged config, not hardcoded defaults
  • Manifest-driven folder resolution works when present
  • Legacy Lev workflow remains possible through global config
  • Project-local workshop defaults work when no global override exists
  • Report is written to the configured report destination

Notes

  • For Lev itself, set workshop.root to ~/lev/workshop and point playbooks at the existing intake.md / papers/intake.md.
  • For project repos, default to <projectRoot>/.lev/workshop.
  • A checked-in workshop manifest plus gitignored runtime folders is the intended shape for project-local workshop state.

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

37.3%
按下载量换算34

Claude

30%
按下载量换算27

Cursor

19.56%
按下载量换算18

Gemini CLI

9.02%
按下载量换算8

安全审计

Gen Agent Trust Hub

未通过

Socket

可疑

Snyk

未通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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