GitHub Deep Research
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
技术选型技能：在多个候选方案中做出有依据的决策. An agent skill from 312362115/claude.
Install Tech Evaluation
$ npx skills add 312362115/claude --skill tech-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install 312362115/claude tech-evaluation --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/312362115/claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tech-evaluation .claude/skills/tech-evaluation && 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 "tech-evaluation" agent skill from https://github.com/312362115/claude/tree/main/skills/tech-evaluation into .claude/skills/tech-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-evaluation", 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/312362115/claude/tree/main/skills/tech-evaluationType 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 312362115/claude --skill tech-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install 312362115/claude tech-evaluation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/312362115/claude.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/tech-evaluation .agents/skills/tech-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tech-evaluation" agent skill from https://github.com/312362115/claude/tree/main/skills/tech-evaluation into .agents/skills/tech-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-evaluation", 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 312362115/claude --skill tech-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install 312362115/claude tech-evaluation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/312362115/claude.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/tech-evaluation .cursor/skills/tech-evaluation && 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 "tech-evaluation" agent skill from https://github.com/312362115/claude/tree/main/skills/tech-evaluation into .cursor/skills/tech-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-evaluation", 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/312362115/claude.git --path skills/tech-evaluation--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 312362115/claude --skill tech-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install 312362115/claude tech-evaluation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/312362115/claude.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/tech-evaluation .gemini/skills/tech-evaluation && 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 "tech-evaluation" agent skill from https://github.com/312362115/claude/tree/main/skills/tech-evaluation into .gemini/skills/tech-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-evaluation", 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 312362115/claude tech-evaluationInstalls 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 312362115/claude --skill tech-evaluation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/312362115/claude.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/tech-evaluation .github/skills/tech-evaluation && 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 "tech-evaluation" agent skill from https://github.com/312362115/claude/tree/main/skills/tech-evaluation into .github/skills/tech-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-evaluation", 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 312362115/claude --skill tech-evaluation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install 312362115/claude tech-evaluation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/312362115/claude.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/tech-evaluation .opencode/skills/tech-evaluation && 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 "tech-evaluation" agent skill from https://github.com/312362115/claude/tree/main/skills/tech-evaluation into .opencode/skills/tech-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-evaluation", 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
tech-evaluation- GitHub stars
- 107
- Token cost
- ~891 tokens
- SKILL.md length
- 145 words
- Files
- 1
- Skills in repo
- 20
- Repo updated
- First seen
- Licence
- MIT
At a glance
技术选型技能：在多个候选方案中做出有依据的决策. An agent skill from 312362115/claude.
- Tasks that involve Deep research
- SKILL.md covers 第一步：定义选型问题, 第二步：快速筛选, 第三步：多维度评估 and 第四步：综合评分与决策, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Tech Evaluation is an agent skill from 312362115/claude. 技术选型技能：在多个候选方案中做出有依据的决策。 和 deep-research 的区别：deep-research 是广度调研（搞清楚一件事）， tech-evaluation 是聚焦决策（A 还是 B，选哪个，给结论）。 包含权重矩阵、PoC 验证流程、决策报告模板。 触发词：选型、选哪个、A 还是 B、对比、评估方案、用什么框架、用什么库。 触发场景：task-start 方案阶段遇到选型问题、引入新依赖前、架构决策。 即使用户没有说"选型"，只要意图是"在几个方案中做选择"，都应触发此技能。
Its SKILL.md is about 890 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Deep research. It works with GitHub. The licence is MIT.
When your agent uses it
- Tasks that involve Deep research
Example prompts
- “，只要意图是”
- “/tech-evaluation”
What it can do on your machine
Read from SKILL.md and the folder at commit 2d4fa49. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
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
Tech Evaluation loads about 891 tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 145 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 312362115/claude at commit 2d4fa49, republished under its MIT licence (© 312362115). 145 words, ~891 tokens.
Download SKILL.mdSave it as
.claude/skills/tech-evaluation/SKILL.md (or your agent's skills folder).- name
- tech-evaluation
- description
- 技术选型技能：在多个候选方案中做出有依据的决策。 和 deep-research 的区别：deep-research 是广度调研（搞清楚一件事）， tech-evaluation 是聚焦决策（A 还是 B，选哪个，给结论）。 包含权重矩阵、PoC 验证流程、决策报告模板。 触发词：选型、选哪个、A 还是 B、对比、评估方案、用什么框架、用什么库。 触发场景：task-start 方案阶段遇到选型问题、引入新依赖前、架构决策。 即使用户没有说"选型"，只要意图是"在几个方案中做选择"，都应触发此技能。
- version
- 1.0.0
- last_updated
- 2026-04-08
- repository
- https://github.com/312362115/claude
技术选型（Tech Evaluation）
选型的核心不是"哪个更好"，而是"在我们的场景下哪个更合适"。 没有最好的技术，只有最合适的技术。
第一步：定义选型问题
用 AskUserQuestion 明确以下信息：
| 要素 | 问什么 | 为什么重要 |
|---|---|---|
| 要解决的问题 | 选型是为了解决什么？ | 锚定评估标准 |
| 候选方案 | 已经有哪些候选？需要我帮忙发现更多吗？ | 确定评估范围 |
| 硬性约束 | 必须满足的条件（许可证、语言、兼容性） | 先排除不合格的 |
| 优先维度 | 最看重什么？（性能 / 生态 / 学习成本 / 成本） | 决定权重 |
| 决策时间 | 需要多深入？快速判断还是深度评估？ | 控制投入 |
第二步：快速筛选
2.1 排除不合格的
用硬性约束做第一轮筛选：
## 候选方案筛选
| 候选 | 约束 1（MIT 许可） | 约束 2（支持 TS） | 约束 3（活跃维护） | 结果 |
|------|-------------------|-------------------|-------------------|------|
| 方案 A | ✅ | ✅ | ✅ | 进入评估 |
| 方案 B | ✅ | ❌ | ✅ | 排除 |
| 方案 C | ✅ | ✅ | ❌（2 年无更新） | 排除 |2.2 快速判断路径
如果筛选后只剩 1-2 个候选，且差异明显：
- 直接给出推荐 + 理由，不需要走完整评估
- 记录决策到 spec 文档中即可
如果筛选后有 2-3 个势均力敌的候选 → 进入第三步完整评估。
第三步：多维度评估
3.1 构建评估矩阵
根据用户关注的维度，构建权重矩阵：
## 评估维度与权重
| 维度 | 权重 | 说明 |
|------|------|------|
| 功能匹配度 | 30% | 是否满足核心需求 |
| 性能 | 25% | 对应场景下的实际表现 |
| 生态与社区 | 20% | 文档质量、社区活跃度、第三方集成 |
| 学习成本 | 15% | 团队上手难度 |
| 运维成本 | 10% | 部署复杂度、监控、升级成本 |权重确定方式：
- 用户明确说了优先级 → 直接用
- 用户没说 → 给出建议权重，让用户确认
3.2 逐维度评估
对每个维度，用事实和数据评估，不用"感觉"：
| 维度 | 怎么评估 | 数据来源 |
|---|---|---|
| 功能匹配度 | 列出需求清单，逐项检查每个候选是否支持 | 官方文档、GitHub issues |
| 性能 | 找 benchmark 数据，或自己跑 PoC 测试 | 官方 benchmark、第三方评测、自测 |
| 生态与社区 | GitHub stars/issues 响应速度、npm 周下载量、Stack Overflow 问题数 | GitHub、npm、Stack Overflow |
| 学习成本 | 文档质量、API 设计是否直觉、有无迁移指南 | 官方文档、教程资源 |
| 运维成本 | 部署方式、配置复杂度、升级历史（有无 breaking changes） | CHANGELOG、升级指南 |
3.3 PoC 验证（可选但推荐）
对关键维度，写代码验证比看文档更可靠：
## PoC 验证
### 验证目标
用方案 A 和方案 B 分别实现 <核心场景>，对比：
- 代码量和复杂度
- 实际性能数据
- 遇到的坑
### 验证结果
| 指标 | 方案 A | 方案 B |
|------|--------|--------|
| 代码行数 | 120 行 | 85 行 |
| 响应时间(p95) | 23ms | 18ms |
| 遇到的问题 | 文档缺失，靠看源码 | 顺利，文档完整 |PoC 不需要做完整功能，只需要验证最不确定的维度。
第四步：综合评分与决策
4.1 评分汇总
## 综合评分
| 维度 | 权重 | 方案 A | 方案 B | 方案 C |
|------|------|--------|--------|--------|
| 功能匹配度 | 30% | 9 | 8 | 7 |
| 性能 | 25% | 7 | 9 | 8 |
| 生态与社区 | 20% | 8 | 7 | 9 |
| 学习成本 | 15% | 6 | 8 | 7 |
| 运维成本 | 10% | 7 | 8 | 6 |
| **加权总分** | | **7.65** | **8.05** | **7.50** |4.2 给出决策
## 选型决策
**推荐：方案 B**
### 核心理由
- 加权总分最高（8.05）
- 在最看重的性能维度（权重 25%）上明显领先
- PoC 验证中开发体验最好
### 取舍说明
- 放弃方案 A 的原因：学习成本较高，团队没有相关经验
- 放弃方案 C 的原因：社区活跃但功能匹配度不足
### 风险提示
- 方案 B 的社区规模较小，未来可能面临维护风险
- 建议：核心功能不过度依赖其独有特性，保持可替换性第五步：输出选型报告
选型结论写入 spec 文档（调 writing skill 的技术文档模式），至少包含：
- 背景与动机：为什么需要选型
- 候选方案：有哪些选项
- 评估过程：评估维度、权重、数据来源
- PoC 结果：如果做了验证
- 决策与取舍：选了什么、为什么选它、放弃了什么
选型准则
- 场景优先：不存在"最好的"技术，只有"最合适当前场景的"技术
- 数据说话：每个评分都要有事实依据，不凭印象打分
- 验证不确定性：最不确定的维度用 PoC 验证，不靠猜
- 考虑团队：技术本身好不等于团队用得好，学习成本是真实成本
- 留退路：优先选不锁定的方案，保持可替换性
- 不过度评估：2 个候选差异明显就直接选，不需要搞 5 维 10 分的矩阵
与其他 skill 的关系
task-start（方案阶段遇到选型）→ tech-evaluation（评估决策）
deep-research（需要广度调研时）← tech-evaluation 按需调用
tech-evaluation → writing（输出选型报告到 spec）tech-evaluation vs deep-research：
- tech-evaluation：必须给结论。"选 A，因为 XYZ"
- deep-research：不一定给结论。"目前市场格局是这样，趋势是那样"
- 选型前如果对候选方案不够了解，可以先用 deep-research 调研，再用 tech-evaluation 决策
© 312362115, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/tech-evaluation of 312362115/claude.
Open the folder on GitHubat commit 2d4fa49
Compare with similar skills
Tech Evaluation 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 |
|---|---|---|---|---|---|---|
| Tech Evaluation this skill312362115/claude | 107 | — | ~891 | Automated safety check: Pass | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 83k | 5 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Rival Search MCPdamionrashford/RivalSearchMCP | 132 | 1 repos | ~796 | Automated safety check: Pass | MIT | |
| Inno Code SurveyLigphiDonk/Oh-my--paper | 738 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Researcherunderstudy-ai/understudy | 459 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Deep ResearchCitrus-bit/Anaxa | 120 | — | ~1.9k | Automated safety check: Pass | MIT |
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Questions about Tech Evaluation
What does Tech Evaluation do?
技术选型技能：在多个候选方案中做出有依据的决策. An agent skill from 312362115/claude. Tech Evaluation is an agent skill from 312362115/claude.
When should I use Tech Evaluation?
Tech Evaluation fits situations like: tasks that involve Deep research.
How do I install Tech Evaluation in Claude Code?
Run `npx skills add 312362115/claude --skill tech-evaluation -a claude-code`. Or copy the skill folder (skills/tech-evaluation in 312362115/claude) into .claude/skills/tech-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Tech Evaluation in Codex?
Run `npx skills add 312362115/claude --skill tech-evaluation -a codex`. Or copy the skill folder (skills/tech-evaluation in 312362115/claude) into .agents/skills/tech-evaluation in your project. Codex loads it when a task matches its description.
Can I use Tech Evaluation 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 312362115/claude --skill tech-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tech-evaluation, .gemini/skills/tech-evaluation, .github/skills/tech-evaluation and .opencode/skills/tech-evaluation in your project.
What does Tech Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Tech Evaluation is instructions for the agent only.
Does Tech Evaluation 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 Tech Evaluation 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 Tech Evaluation use?
Tech Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Tech Evaluation use?
About 891 tokens (SKILL.md is roughly 3.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Tech Evaluation?
Skills that share tags, products or a category with Tech Evaluation: GitHub Deep Research (bytedance/deer-flow, 83k stars), Rival Search MCP (damionrashford/RivalSearchMCP, 132 stars), Inno Code Survey (LigphiDonk/Oh-my--paper, 738 stars) and Researcher (understudy-ai/understudy, 459 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tech Evaluation?
312362115 (a GitHub user) maintains it in 312362115/claude, which has 107 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on May 14, 2026.
Source: 312362115/claude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.