Internship Project Preparation Tool
LiuMengxuan04/shushu-internship-tool
Turns a target internship job description into a resume-ready, interview-ready project by finding and auditing GitHub projects and drafting resume bullets and interview Q&A.
Agent skill
Backend and Agent Project Selector
by lishuangqiang in lishuangqiang/backend-agent-resume-scout
Finds backend or AI agent projects on GitHub that are worth putting on a resume, checks them against local source and writes a Markdown resume package.
SKILL.md written in Chinese; this summary is our English description.
Install Backend and Agent Project Selector
$ npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lishuangqiang/backend-agent-resume-scout backend-agent-project-selector --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/lishuangqiang/backend-agent-resume-scout.git skills-src && mkdir -p .claude/skills && cp -r skills-src/backend-agent-project-selector .claude/skills/backend-agent-project-selector && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "backend-agent-project-selector" agent skill from https://github.com/lishuangqiang/backend-agent-resume-scout/tree/main/backend-agent-project-selector into .claude/skills/backend-agent-project-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "backend-agent-project-selector", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/lishuangqiang/backend-agent-resume-scout/tree/main/backend-agent-project-selectorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lishuangqiang/backend-agent-resume-scout backend-agent-project-selector --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lishuangqiang/backend-agent-resume-scout.git skills-src && mkdir -p .agents/skills && cp -r skills-src/backend-agent-project-selector .agents/skills/backend-agent-project-selector && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "backend-agent-project-selector" agent skill from https://github.com/lishuangqiang/backend-agent-resume-scout/tree/main/backend-agent-project-selector into .agents/skills/backend-agent-project-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "backend-agent-project-selector", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lishuangqiang/backend-agent-resume-scout backend-agent-project-selector --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lishuangqiang/backend-agent-resume-scout.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/backend-agent-project-selector .cursor/skills/backend-agent-project-selector && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "backend-agent-project-selector" agent skill from https://github.com/lishuangqiang/backend-agent-resume-scout/tree/main/backend-agent-project-selector into .cursor/skills/backend-agent-project-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "backend-agent-project-selector", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/lishuangqiang/backend-agent-resume-scout.git --path backend-agent-project-selector--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lishuangqiang/backend-agent-resume-scout backend-agent-project-selector --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lishuangqiang/backend-agent-resume-scout.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/backend-agent-project-selector .gemini/skills/backend-agent-project-selector && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "backend-agent-project-selector" agent skill from https://github.com/lishuangqiang/backend-agent-resume-scout/tree/main/backend-agent-project-selector into .gemini/skills/backend-agent-project-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "backend-agent-project-selector", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install lishuangqiang/backend-agent-resume-scout backend-agent-project-selectorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lishuangqiang/backend-agent-resume-scout.git skills-src && mkdir -p .github/skills && cp -r skills-src/backend-agent-project-selector .github/skills/backend-agent-project-selector && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "backend-agent-project-selector" agent skill from https://github.com/lishuangqiang/backend-agent-resume-scout/tree/main/backend-agent-project-selector into .github/skills/backend-agent-project-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "backend-agent-project-selector", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lishuangqiang/backend-agent-resume-scout backend-agent-project-selector --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lishuangqiang/backend-agent-resume-scout.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/backend-agent-project-selector .opencode/skills/backend-agent-project-selector && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "backend-agent-project-selector" agent skill from https://github.com/lishuangqiang/backend-agent-resume-scout/tree/main/backend-agent-project-selector into .opencode/skills/backend-agent-project-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "backend-agent-project-selector", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
backend-agent-project-selector- GitHub stars
- 350
- Token cost
- ~1.4k tokens
- SKILL.md length
- 266 words
- Files
- 12 (incl. references)
- Skills in repo
- 1
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Finds backend or AI agent projects on GitHub that are worth putting on a resume, checks them against local source and writes a Markdown resume package.
- Works in 11 steps: 先检查用户是否明确给出推荐模式。 → 如果没有给出推荐模式，停止执行，并使用“缺少推荐模式时的反问话术”完整展示所有可选… → 如果已给出推荐模式，再提取用户画像。 → …
- Choosing backend or distributed-system projects to study for a resume
- SKILL.md covers 能力范围, 必须遵守, 参考资料加载 and 高层流程, plus 4 more sections
- Runs Python scripts from its folder; calls python
What it does
Written in Chinese for students and early-career engineers, the skill searches GitHub and the web for complete business-grade backend or AI agent projects. A recommendation mode is mandatory: `agent-only`, `backend-only`, `mixed`, `safe-mode` or `challenge-mode`. If you give none, the agent lists the modes and asks before it searches. It favors full business systems over demos, plugins, thin wrappers and shallow CRUD, treats popularity only as a weak signal, and drops IoT and hardware platforms unless asked.
The workflow builds a candidate pool with `references/search_github_candidates.py`, shows three to four shortlisted projects with reasons and waits for your confirmation, then clones the finalists with `pull_github_repos.py` and verifies them from local source only. A project is dropped if it cannot be cloned or fewer than five pieces of evidence can be extracted. The result is `backend-agent-project-resume-pack.md`, also exported to PDF, separating existing capability, suggested changes and what can honestly be written on a resume, with interview talking points.
When your agent uses it
- Choosing backend or distributed-system projects to study for a resume
- Finding a complete multi-agent or LLM business project on GitHub
- Turning chosen projects into resume bullets and interview questions
Example prompts
- “Use mixed mode to pick two projects, one backend and one AI agent, for a Java backend resume.”
- “Find distributed-system projects in backend-only mode and write the resume pack.”
- “Give me challenge-mode agent projects and the interview questions I should prepare.”
Requirements
- Network access to search GitHub and clone repositories
- Python to run the bundled search, clone and PDF scripts
Workflow steps
11 steps, taken from the first numbered list in SKILL.md.
- 先检查用户是否明确给出推荐模式。
- 如果没有给出推荐模式，停止执行，并使用“缺少推荐模式时的反问话术”完整展示所有可选模式后让用户选择。
- 如果已给出推荐模式，再提取用户画像。
- 优先运行 references/search_github_candidates.py 搜索多个项目分桶，建立候选池；候选池必须包含 1k+ star 的中等热度项目，不能只来自全局高星榜。
- 按 筛选评分.md 过滤浅层项目，并给剩余候选打分。
- 按推荐模式选择 3-4 个短名单项目，输出短名单、选择理由、淘汰理由和待拉取 URL，先让用户确认技术栈和方向。
- 用户确认短名单后，将最终候选仓库 URL 写入临时列表或作为 --repo 参数，强制执行 python references/pull_github_repos.py 拉取源码并生成 manifest。
- 只读取 manifest 中状态为 cloned 的本地仓库目录；阅读 README / 文档只是前置步骤，不能代替本地源码验证。
- 只保留能够从本地源码证据提取功能点的项目；无法完成脚本拉取或源码验证的候选直接淘汰。
- 按 输出模板.md 和 简历写法.md 生成 backend-agent-project-resume-pack.md。
- 默认最终必须同时生成 Markdown 和 PDF 两个版本；执行 python backend-agent-project-selector/references/markdown_to_pdf.py backend-agent-project-resume-pack.md…
What it can do on your machine
Read from SKILL.md and the folder at commit 7f2e227. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Backend and Agent Project Selector loads about 1.4k tokens when it runs, and up to ~32k if it reads all its reference files. Until then it costs about 231 tokens; SKILL.md has 266 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from lishuangqiang/backend-agent-resume-scout at commit 7f2e227, republished under its Apache-2.0 licence (© lishuangqiang). 266 words, ~1,363 tokens.
Download SKILL.mdSave it as
.claude/skills/backend-agent-project-selector/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.- name
- backend-agent-project-selector
- description
- Find suitable traditional software backend or business-grade AI agent projects from GitHub for students or early-career engineers, evaluate resume value, and generate a Markdown resume-writing package. Use when a user asks to choose backend projects, Java/Spring projects, distributed-system projects, complete AI agent business projects, multi-agent systems, LLM apps, GitHub projects for resumes, or turn selected projects into resume bullets/interview talking points. Supports modes: agent-only, backend-only, mixed, safe-mode, and challenge-mode. If no mode is specified, ask the user to choose before searching or recommending. Backend recommendations exclude IoT, embedded, hardware-integration, device-management, and industrial-control platforms unless requested. Avoid simple browser extensions, thin LLM wrappers, shallow AI plugins, and browser-automation libraries unless requested.
后端 / Agent 项目选择器
能力范围
从 GitHub / Web 搜索适合写进简历的后端项目或完整业务型 Agent 项目，筛选后生成 backend-agent-project-resume-pack.md。
推荐模式是必填项。用户必须指定 agent-only、backend-only、mixed、safe-mode、challenge-mode 中的一种；如果用户初始化找项目、选择项目、推荐项目或生成简历项目但没有指定模式，先完整列出所有可选模式并让用户选择，不要开始搜索或生成推荐。
必须遵守
- 用户每次初始化找项目、选择项目、推荐项目或生成简历项目时，如果推荐模式缺失，必须先完整列出所有可选模式并反问，不能自行默认、推断或继续执行。
- 除非用户明确要求不要联网，否则要搜索当前 GitHub / Web 项目后再推荐。
- 优先完整业务系统，避免 demo、插件、薄封装、纯框架和只会调用一次大模型的项目。
backend-only不要默认只推荐 API 网关、IAM、调度、监控等底层技术项目；除非用户明确要求基础设施 / 中间件，否则应优先覆盖电商交易、内容社区、协作办公、工单客服、CRM/ERP、知识库、网盘、在线教育等完整业务项目。- 不要避开有业务闭环的业务项目；只避开浅层 CRUD、教程复刻、没有状态流转 / 异步链路 / 失败恢复 / 权限边界的业务项目。
- 默认不要把 IoT、嵌入式、硬件接入、设备管理、工业控制类平台作为后端项目推荐；除非用户明确要求硬件 / IoT / 物联网方向，否则这些项目应降权或淘汰。
- 先构建多样化候选池，再做最终选择；不要直接推荐全局高星项目，也不要把 star 数作为主要排序依据。
- star 数只作为“项目已有一定社区验证”的弱信号：达到 1k star 即可纳入正式候选池并认真评估；超过 1k 后不应因为 star 更高而显著加分。
- 搜索候选时必须主动覆盖中等 star 区间和细分领域项目，避免只用
sort=stars或只看几万 star 项目；后端和 Agent 项目都适用该规则。 - 候选池阶段要读取 README 前若干行做项目类型 probe，用于判断是否为业务系统、框架 / SDK / 桌面壳 / 工具链；README probe 只能用于筛选和项目定位，不能支撑最终“负责功能 / 技术难点”。
- 构建候选池时优先使用
references/search_github_candidates.py自动搜索、README probe、去重、分桶和初筛；如果手动搜索，最终也要输出同等字段的候选池和短名单确认内容。 - 拉取最终候选源码前，必须先向用户展示 3-4 个短名单项目、每个项目的选择理由和主要淘汰理由，并等待用户确认方向；用户确认后才能执行
pull_github_repos.py。 - 最终入选项目前必须运行
references/pull_github_repos.py真实拉取对应 GitHub 仓库到本地；未经过该脚本成功拉取并写入 manifest 的项目不得进入最终推荐。 - 源码验证只能基于
pull_github_repos.py拉取到本地的仓库目录进行；不能用 GitHub raw/API、README、网页搜索、模型记忆或经验判断替代本地源码验证。 - 如果脚本执行失败、仓库无法拉取、manifest 中该仓库状态不是
cloned、本地源码不可读，或无法从源码提取至少 5 个证据点，该项目必须淘汰。 - 必须区分
已有能力、建议改造、可写入简历。 - 简历功能点默认输出为“建议简历功能点（完成对应改造后可写）”；除非用户明确已经实现或本轮完成代码改造，否则不能把建议改造写成已完成成果。
- 项目亮点必须挖掘技术难度，不能只写“实现功能 / 接入组件 / 提供接口”；优先挖数据同步与一致性、MQ 异步链路、缓存与高并发、并发控制与幂等、任务调度、线程池与异步编排、流量治理、数据库优化、检索索引、权限安全、可观测性、接口治理、规则引擎、状态机、文件处理、实时通信、交易链路、Agent 工程等机制。
- 默认使用中文输出，除非用户要求其他语言。
参考资料加载
按任务需要加载，不要一次性加载所有资料：
references/执行流程.md：项目选择任务必载。references/用户输入模板.md：用户需要模板、示例，或推荐模式缺失需要反问时加载。references/筛选评分.md：筛选、分桶、推荐模式打分时加载。references/简历写法.md：写简历条目和面试问题前加载。references/输出模板.md：生成最终 Markdown 文件前加载。references/search_github_candidates.py：候选池搜索和短名单确认前优先执行；用于自动搜索 GitHub、读取 README probe、去重、分桶、排除前端 / 库 / IoT / 框架 / SDK / 桌面壳 / coding-agent 工具链等不合适项目，并生成候选池 JSON 与短名单预览 Markdown。references/pull_github_repos.py：最终候选源码验证前必须执行；用于真实拉取 GitHub 仓库并生成本地源码 manifest。references/markdown_to_pdf.py：最终交付必执行；将最终 Markdown 转成浅色、中文友好的 PDF。references/规则索引.md：只在需要查看文档映射关系时加载。
高层流程
- 先检查用户是否明确给出推荐模式。
- 如果没有给出推荐模式，停止执行，并使用“缺少推荐模式时的反问话术”完整展示所有可选模式后让用户选择。
- 如果已给出推荐模式，再提取用户画像。
- 优先运行
references/search_github_candidates.py搜索多个项目分桶，建立候选池；候选池必须包含 1k+ star 的中等热度项目，不能只来自全局高星榜。 - 按
筛选评分.md过滤浅层项目，并给剩余候选打分。 - 按推荐模式选择 3-4 个短名单项目，输出短名单、选择理由、淘汰理由和待拉取 URL，先让用户确认技术栈和方向。
- 用户确认短名单后，将最终候选仓库 URL 写入临时列表或作为
--repo参数，强制执行python references/pull_github_repos.py拉取源码并生成 manifest。 - 只读取 manifest 中状态为
cloned的本地仓库目录；阅读 README / 文档只是前置步骤，不能代替本地源码验证。 - 只保留能够从本地源码证据提取功能点的项目；无法完成脚本拉取或源码验证的候选直接淘汰。
- 按
输出模板.md和简历写法.md生成backend-agent-project-resume-pack.md。 - 默认最终必须同时生成 Markdown 和 PDF 两个版本；执行
python backend-agent-project-selector/references/markdown_to_pdf.py backend-agent-project-resume-pack.md --output backend-agent-project-resume-pack.pdf --title "业务型 Agent 项目推荐报告"。
推荐模式
agent-only：只推荐完整业务型 Agent 项目。backend-only：只推荐传统软件后端项目，默认排除 IoT / 硬件接入类项目。mixed：推荐一个 Agent 项目 + 一个传统软件后端项目；其中后端项目默认排除 IoT / 硬件接入类项目。safe-mode：只推荐容易落地、依赖少、部署成本低的项目。challenge-mode：推荐更难、更有差异化、更适合深度改造的项目。
缺少推荐模式时的反问话术
如果用户没有明确写出推荐模式，直接回复：
你想按哪种推荐模式执行？
- agent-only：只找完整业务型 Agent 项目
- backend-only：只找传统软件后端项目，默认排除 IoT / 硬件接入类
- mixed：一个 Agent 项目 + 一个传统软件后端项目
- safe-mode：只推荐容易落地、部署成本低的项目
- challenge-mode：推荐更难、更有差异化的项目
你回复其中一个模式后，我再开始搜索 GitHub 并生成简历写法。Agent 项目标准
Agent 项目必须是完整业务系统，而不是 LLM UI、浏览器插件或浏览器自动化库。它应该解决具体领域问题，例如 AI 投研、多智能体协作、客服自动化、数据分析、代码修复、DevOps 运维、报告生成、企业知识流转。
Agent 候选短名单必须优先满足业务型强门槛：业务数据、状态流转、持久化、工具调用、评测、用户价值至少命中 2-3 项；纯 framework / SDK / library / toolkit / desktop companion / coding-agent 工具链不得和业务型 Agent 混在同一短名单中，除非用户明确要求工具链方向。
除非用户明确要求，否则淘汰简单浏览器插件、网页侧边栏、总结器、提示词管理器、单次 LLM 调用包装，以及没有业务数据、状态、持久化、评测或可见用户价值的项目。
输出约定
始终在当前工作区创建或更新 Markdown 与 PDF 两个交付物；Markdown 内容包括：
- 结论先行的推荐
- 候选池
- 推荐模式和多样性说明
- 可替换项目
- 每个推荐项目的详细分析
- 简历写法：先给 80-120 字项目简介，再给代码验证摘要，然后给 5-6 条“负责功能 / 技术难点”，最后给“建议简历功能点（完成对应改造后可写）”；其中“负责功能 / 技术难点”必须来自源码验证。
- 下一步落地或改造计划
- PDF 必须生成：使用
references/markdown_to_pdf.py输出backend-agent-project-resume-pack.pdf，保持浅色背景块、清晰标题层级，不添加“阅读导航”，内联代码按普通正文渲染以避免中文段落异常换行。
© lishuangqiang, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 11 other files (references) in backend-agent-project-selector of lishuangqiang/backend-agent-resume-scout.
- SKILL.md
- agents/openai.yaml
- references/markdown_to_apple_pdf.py
- references/markdown_to_pdf.py
- references/pull_github_repos.py
- references/search_github_candidates.py
- references/执行流程.md
- references/用户输入模板.md
- references/筛选评分.md
- references/简历写法.md
- references/规则索引.md
- references/输出模板.md
Open the folder on GitHubat commit 7f2e227
Compare with similar skills
Backend and Agent Project Selector next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Backend and Agent Project Selector this skilllishuangqiang/backend-agent-resume-scout | 350 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Internship Project Preparation ToolLiuMengxuan04/shushu-internship-tool | 2.1k | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| Offer Negotiationreactive-resume/reactive-resume | 44k | — | ~10k | Automated safety check: Pass | MIT | |
| Career-Ops Job Search Centercareer-ops-hq/career-ops | 74k | — | ~3.6k | Automated safety check: Pass | MIT | |
| Job Application AssistantMadsLorentzen/ai-job-search | 45k | — | ~1.2k | Automated safety check: Notes | MIT | |
| LLM Intern Skillwanyichen06/LLMInternSkill | 326 | — | ~1.2k | Automated safety check: Pass | MIT |
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Offer Negotiation
reactive-resume/reactive-resume
Evaluates and negotiates job offers. An agent skill from reactive-resume/reactive-resume.
Career-Ops Job Search Center
career-ops-hq/career-ops
Routes job-search requests to modes for evaluating offers, scanning portals, generating tailored CVs, tracking applications and drafting outreach, starting from a pasted job URL or description.
Job Application Assistant
MadsLorentzen/ai-job-search
Evaluates job postings against your profile, then tailors a LaTeX CV and cover letter and prepares interview answers for the roles you pursue.
LLM Intern Skill
wanyichen06/LLMInternSkill
A skill your agent uses when polishing, diagnosing, tailoring, or exporting resumes for LLM, RAG, Agent, Agentic RL, post-training, pretraining, AIGC, search/ranking, multimodal, AI backend, or LLM…
Resume Tailor
reactive-resume/reactive-resume
Tailors one resume to one specific job posting. An agent skill from reactive-resume/reactive-resume.
Related
Works with
Categories
Questions about Backend and Agent Project Selector
What does Backend and Agent Project Selector do?
Finds backend or AI agent projects on GitHub that are worth putting on a resume, checks them against local source and writes a Markdown resume package. Written in Chinese for students and early-career engineers, the skill searches GitHub and the web for complete business-grade backend or AI agent projects. A recommendation mode is mandatory: `agent-only`, `backend-only`, `mixed`, `safe-mode` or `challenge-mode`.
When should I use Backend and Agent Project Selector?
Backend and Agent Project Selector fits situations like: choosing backend or distributed-system projects to study for a resume; finding a complete multi-agent or LLM business project on GitHub; turning chosen projects into resume bullets and interview questions.
How do I install Backend and Agent Project Selector in Claude Code?
Run `npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a claude-code`. Or copy the skill folder (backend-agent-project-selector in lishuangqiang/backend-agent-resume-scout) into .claude/skills/backend-agent-project-selector in your project. Claude Code loads it when a task matches its description.
How do I install Backend and Agent Project Selector in Codex?
Run `npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a codex`. Or copy the skill folder (backend-agent-project-selector in lishuangqiang/backend-agent-resume-scout) into .agents/skills/backend-agent-project-selector in your project. Codex loads it when a task matches its description.
Can I use Backend and Agent Project Selector in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lishuangqiang/backend-agent-resume-scout --skill backend-agent-project-selector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/backend-agent-project-selector, .gemini/skills/backend-agent-project-selector, .github/skills/backend-agent-project-selector and .opencode/skills/backend-agent-project-selector in your project.
What does Backend and Agent Project Selector need to run?
Going by SKILL.md and its folder, Backend and Agent Project Selector needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Network access to search GitHub and clone repositories; Python to run the bundled search, clone and PDF scripts.
Does Backend and Agent Project Selector access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Backend and Agent Project Selector safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Backend and Agent Project Selector use?
Backend and Agent Project Selector is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Backend and Agent Project Selector use?
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 31k tokens, read only when the agent opens those files.
What are the alternatives to Backend and Agent Project Selector?
Skills that share tags, products or a category with Backend and Agent Project Selector: Internship Project Preparation Tool (LiuMengxuan04/shushu-internship-tool, 2.1k stars), Offer Negotiation (reactive-resume/reactive-resume, 44k stars), Career-Ops Job Search Center (career-ops-hq/career-ops, 74k stars) and Job Application Assistant (MadsLorentzen/ai-job-search, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Backend and Agent Project Selector?
lishuangqiang (a GitHub user) maintains it in lishuangqiang/backend-agent-resume-scout, which has 350 GitHub stars. The repository was last updated on June 22, 2026.
Source: lishuangqiang/backend-agent-resume-scout on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.