9Router Web Search
decolua/9router
Runs web, news and public X searches through a 9Router gateway's search endpoint, choosing among providers such as Tavily, Exa, Brave and Perplexity.
四源同级：Brave (websearch) + Exa + Tavily + Grok。按意图自动选策略、调权重、做合成。
Install Search Layer
$ npx skills add aAAaqwq/AGI-Super-Team --skill search-layer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team search-layer --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/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/search-layer .claude/skills/search-layer && 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 "search-layer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/search-layer into .claude/skills/search-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-layer", 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/aAAaqwq/AGI-Super-Team/tree/main/skills/search-layerType 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 aAAaqwq/AGI-Super-Team --skill search-layer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team search-layer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/search-layer .agents/skills/search-layer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "search-layer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/search-layer into .agents/skills/search-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-layer", 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 aAAaqwq/AGI-Super-Team --skill search-layer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team search-layer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/search-layer .cursor/skills/search-layer && 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 "search-layer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/search-layer into .cursor/skills/search-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-layer", 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/aAAaqwq/AGI-Super-Team.git --path skills/search-layer--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 aAAaqwq/AGI-Super-Team --skill search-layer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team search-layer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/search-layer .gemini/skills/search-layer && 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 "search-layer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/search-layer into .gemini/skills/search-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-layer", 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 aAAaqwq/AGI-Super-Team search-layerInstalls 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 aAAaqwq/AGI-Super-Team --skill search-layer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/search-layer .github/skills/search-layer && 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 "search-layer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/search-layer into .github/skills/search-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-layer", 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 aAAaqwq/AGI-Super-Team --skill search-layer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team search-layer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/search-layer .opencode/skills/search-layer && 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 "search-layer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/search-layer into .opencode/skills/search-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-layer", 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
search-layer- GitHub stars
- 105
- Used in
- 1 other repo
- Token cost
- ~1.9k tokens
- SKILL.md length
- 554 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 152
- Repo updated
- First seen
- Licence
- MIT
At a glance
四源同级：Brave (websearch) + Exa + Tavily + Grok。按意图自动选策略、调权重、做合成。
- Works in 6 steps: 意图分类 → 查询分解 & 扩展 → 多源并行检索 → …
- ANY query that requires web search — factual lookups
- SKILL.md covers 执行流程, Phase 1: 意图分类, Phase 2: 查询分解 & 扩展 and Phase 3: 多源并行检索, plus 6 more sections
- Runs Python scripts from its folder; calls python3; reaches github.com; needs GROK_API_KEY
What it does
Search Layer is an agent skill from aAAaqwq/AGI-Super-Team. 四源同级：Brave (websearch) + Exa + Tavily + Grok。按意图自动选策略、调权重、做合成。 DEFAULT search tool for ALL search/lookup needs. Multi-source search and deduplication layer with intent-aware scoring. Integrates Brave Search (websearch), Exa, Tavily, and Grok to provide high-coverage, high-quality results. Automatically classifies query intent and adjusts search strategy, scoring weights, and result synthesis. Use for ANY query that requires web search — factual lookups, research, news, comparisons, resource finding, "what is X"…
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/authority-domains.json`, `references/intent-guide.md` and `scripts/chain_tracker.py`).
It sits in Productivity & Automation, covering Web search. It works with Tavily, Brave Search API, Deno and Rust. The repository describes itself as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters. The licence is MIT.
When your agent uses it
- ANY query that requires web search — factual lookups
- Resource finding
Example prompts
- “what is X”
- “/search-layer”
Requirements
- Python 3
- A credential in GROK_API_KEY
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 331ecd3. 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 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
GROK_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Search Layer loads about 1.9k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 156 tokens; SKILL.md has 554 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); the scripts in this folder are not scanned.
SKILL.md
The full file from aAAaqwq/AGI-Super-Team at commit 331ecd3, republished under its MIT licence (© aAAaqwq). 554 words, ~1,929 tokens.
Download SKILL.mdSave it as
.claude/skills/search-layer/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- search-layer
- description
- 四源同级：Brave (`web_search`) + Exa + Tavily + Grok。按意图自动选策略、调权重、做合成。 DEFAULT search tool for ALL search/lookup needs. Multi-source search and deduplication layer with intent-aware scoring. Integrates Brave Search (web_search), Exa, Tavily, and Grok to provide high-coverage, high-quality results. Automatically classifies query intent and adjusts search strategy, scoring weights, and result synthesis. Use for ANY query that requires web search — factual lookups, research, news, comparisons, resource finding, "what is X", status checks, etc. Do NOT use raw web_search directly; always route through this skill.
Search Layer v2.2 — 意图感知多源检索协议
四源同级：Brave (web_search) + Exa + Tavily + Grok。按意图自动选策略、调权重、做合成。
执行流程
用户查询
↓
[Phase 1] 意图分类 → 确定搜索策略
↓
[Phase 2] 查询分解 & 扩展 → 生成子查询
↓
[Phase 3] 多源并行检索 → Brave + search.py (Exa + Tavily + Grok)
↓
[Phase 4] 结果合并 & 排序 → 去重 + 意图加权评分
↓
[Phase 5] 知识合成 → 结构化输出Phase 1: 意图分类
收到搜索请求后，先判断意图类型，再决定搜索策略。不要问用户用哪种模式。
| 意图 | 识别信号 | Mode | Freshness | 权重偏向 |
|---|---|---|---|---|
| Factual | "什么是 X"、"X 的定义"、"What is X" | answer | — | 权威 0.5 |
| Status | "X 最新进展"、"X 现状"、"latest X" | deep | pw/pm | 新鲜度 0.5 |
| Comparison | "X vs Y"、"X 和 Y 区别" | deep | py | 关键词 0.4 + 权威 0.4 |
| Tutorial | "怎么做 X"、"X 教程"、"how to X" | answer | py | 权威 0.5 |
| Exploratory | "深入了解 X"、"X 生态"、"about X" | deep | — | 权威 0.5 |
| News | "X 新闻"、"本周 X"、"X this week" | deep | pd/pw | 新鲜度 0.6 |
| Resource | "X 官网"、"X GitHub"、"X 文档" | fast | — | 关键词 0.5 |
详细分类指南见
references/intent-guide.md
判断规则：
- 扫描查询中的信号词
- 多个类型匹配时选最具体的
- 无法判断时默认
exploratory
Phase 2: 查询分解 & 扩展
根据意图类型，将用户查询扩展为一组子查询：
通用规则
- 技术同义词自动扩展：k8s→Kubernetes, JS→JavaScript, Go→Golang, Postgres→PostgreSQL
- 中文技术查询：同时生成英文变体（如 "Rust 异步编程" → 额外搜 "Rust async programming"）
按意图扩展
| 意图 | 扩展策略 | 示例 |
|---|---|---|
| Factual | 加 "definition"、"explained" | "WebTransport" → "WebTransport", "WebTransport explained overview" |
| Status | 加年份、"latest"、"update" | "Deno 进展" → "Deno 2.0 latest 2026", "Deno update release" |
| Comparison | 拆成 3 个子查询 | "Bun vs Deno" → "Bun vs Deno", "Bun advantages", "Deno advantages" |
| Tutorial | 加 "tutorial"、"guide"、"step by step" | "Rust CLI" → "Rust CLI tutorial", "Rust CLI guide step by step" |
| Exploratory | 拆成 2-3 个角度 | "RISC-V" → "RISC-V overview", "RISC-V ecosystem", "RISC-V use cases" |
| News | 加 "news"、"announcement"、日期 | "AI 新闻" → "AI news this week 2026", "AI announcement latest" |
| Resource | 加具体资源类型 | "Anthropic MCP" → "Anthropic MCP official documentation" |
Phase 3: 多源并行检索
Step 1: Brave（所有模式）
对每个子查询调用 web_search。如果意图有 freshness 要求，传 freshness 参数：
web_search(query="Deno 2.0 latest 2026", freshness="pw")Step 2: Exa + Tavily + Grok（Deep / Answer 模式）
对子查询调用 search.py，传入意图和 freshness：
python3 /home/node/.openclaw/workspace/skills/search-layer/scripts/search.py \
--queries "子查询1" "子查询2" "子查询3" \
--mode deep \
--intent status \
--freshness pw \
--num 5各模式源参与矩阵：
| 模式 | Exa | Tavily | Grok | 说明 |
|---|---|---|---|---|
| fast | ✅ | ❌ | fallback | Exa 优先；无 Exa key 时用 Grok |
| deep | ✅ | ✅ | ✅ | 三源并行 |
| answer | ❌ | ✅ | ❌ | 仅 Tavily（含 AI answer） |
参数说明：
| 参数 | 说明 |
|---|---|
--queries | 多个子查询并行执行（也可用位置参数传单个查询） |
--mode | fast / deep / answer |
--intent | 意图类型，影响评分权重（不传则不评分，行为与 v1 一致） |
--freshness | pd(24h) / pw(周) / pm(月) / py(年) |
--domain-boost | 逗号分隔的域名，匹配的结果权威分 +0.2 |
--num | 每源每查询的结果数 |
Grok 源说明：
- 通过 completions API 调用 Grok 模型（
grok-4.1-fast），利用其实时知识返回结构化搜索结果 - 自动检测时间敏感查询并注入当前时间上下文
- 在 deep 模式下与 Exa、Tavily 并行执行
- 需要在
~/.openclaw/credentials/search.json中配置 Grok 的apiUrl、apiKey、model（或通过环境变量GROK_API_URL、GROK_API_KEY、GROK_MODEL） - 如果 Grok 配置缺失，自动降级为 Exa + Tavily 双源
Step 3: 合并
将 Brave 结果与 search.py 输出合并。按 canonical URL 去重，标记来源。
如果 search.py 返回了 score 字段，用它排序；Brave 结果没有 score 的，用同样的意图权重公式补算。
Phase 3.5: 引用追踪（Thread Pulling）
当搜索结果中包含 GitHub issue/PR 链接，且意图为 Status 或 Exploratory 时，自动触发引用追踪。
自动触发条件
- 意图为
status或exploratory - 搜索结果中包含
github.com/.../issues/或github.com/.../pull/URL
Show full SKILL.md (221 more words)Show less
方式 1: search.py --extract-refs（批量）
在搜索结果上直接提取引用图，无需额外调用：
python3 search.py "OpenClaw config validation bug" --mode deep --intent status --extract-refs输出中会多一个 refs 字段，包含每个结果 URL 的引用列表。
也可以跳过搜索，直接对已知 URL 提取引用：
python3 search.py --extract-refs-urls "https://github.com/owner/repo/issues/123" "https://github.com/owner/repo/issues/456"方式 2: fetch-thread（单 URL 深度抓取）
对单个 URL 拉取完整讨论流 + 结构化引用：
python3 fetch_thread.py "https://github.com/owner/repo/issues/123" --format json
python3 fetch_thread.py "https://github.com/owner/repo/issues/123" --format markdown
python3 fetch_thread.py "https://github.com/owner/repo/issues/123" --extract-refs-onlyGitHub 场景（issue/PR）：通过 API 拉取正文 + 全部 comments + timeline 事件（cross-references、commits），提取：
- Issue/PR 引用（#123、owner/repo#123）
- Duplicate 标记
- Commit 引用
- 关联 PR/issue（timeline cross-references）
- 外部 URL
通用 web 场景：web fetch + 正则提取引用链接。
Agent 执行流程
Step 1: search-layer 搜索 → 获取初始结果
Step 2: search.py --extract-refs 或 fetch-thread → 提取线索图
Step 3: Agent 筛选高价值线索（LLM 判断哪些值得追踪）
Step 4: fetch-thread 深度抓取每个高价值线索
Step 5: 重复 Step 2-4，直到信息闭环或达到深度限制（建议 max_depth=3）Phase 4: 结果排序
评分公式
score = w_keyword × keyword_match + w_freshness × freshness_score + w_authority × authority_score权重由意图决定（见 Phase 1 表格）。各分项：
- keyword_match (0-1)：查询词在标题+摘要中的覆盖率
- freshness_score (0-1)：基于发布日期，越新越高（无日期=0.5）
- authority_score (0-1)：基于域名权威等级
- Tier 1 (1.0): github.com, stackoverflow.com, 官方文档站
- Tier 2 (0.8): HN, dev.to, 知名技术博客
- Tier 3 (0.6): Medium, 掘金, InfoQ
- Tier 4 (0.4): 其他
完整域名评分表见
references/authority-domains.json
Domain Boost
通过 --domain-boost 参数手动指定需要加权的域名（匹配的结果权威分 +0.2）：
search.py "query" --mode deep --intent tutorial --domain-boost dev.to,freecodecamp.org推荐搭配：
- Tutorial →
dev.to, freecodecamp.org, realpython.com, baeldung.com - Resource →
github.com - News →
techcrunch.com, arstechnica.com, theverge.com
Phase 5: 知识合成
根据结果数量选择合成策略：
小结果集（≤5 条）
逐条展示，每条带源标签和评分：
1. [Title](url) — snippet... `[brave, exa]` ⭐0.85
2. [Title](url) — snippet... `[tavily]` ⭐0.72中结果集（5-15 条）
按主题聚类 + 每组摘要：
**主题 A: [描述]**
- [结果1] — 要点... `[source]`
- [结果2] — 要点... `[source]`
**主题 B: [描述]**
- [结果3] — 要点... `[source]`大结果集（15+ 条）
高层综述 + Top 5 + 深入提示：
[一段综述，概括主要发现]
**Top 5 最相关结果：**
1. ...
2. ...
共找到 N 条结果，覆盖 [源列表]。需要深入哪个方面？合成规则
- 先给答案，再列来源（不要先说"我搜了什么"）
- 按主题聚合，不按来源聚合（不要"Brave 结果：... Exa 结果：..."）
- 冲突信息显性标注：不同源说法矛盾时明确指出
- 置信度表达：
- 多源一致 + 新鲜 → 直接陈述
- 单源或较旧 → "根据 [source]，..."
- 冲突或不确定 → "存在不同说法：A 认为...，B 认为..."
降级策略
- Exa 429/5xx → 继续 Brave + Tavily + Grok
- Tavily 429/5xx → 继续 Brave + Exa + Grok
- Grok 超时/错误 → 继续 Brave + Exa + Tavily
- search.py 整体失败 → 仅用 Brave
web_search（始终可用） - 永远不要因为某个源失败而阻塞主流程
向后兼容
不带 --intent 参数时，search.py 行为与 v1 完全一致（无评分，按原始顺序输出）。
现有调用方（如 github-explorer）无需修改。
快速参考
| 场景 | 命令 |
|---|---|
| 快速事实 | web_search + search.py --mode answer --intent factual |
| 深度调研 | web_search + search.py --mode deep --intent exploratory |
| 最新动态 | web_search(freshness="pw") + search.py --mode deep --intent status --freshness pw |
| 对比分析 | web_search × 3 queries + search.py --queries "A vs B" "A pros" "B pros" --intent comparison |
| 找资源 | web_search + search.py --mode fast --intent resource |
© aAAaqwq, MIT. 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 6 other files (scripts, references) in skills/search-layer of aAAaqwq/AGI-Super-Team.
- SKILL.md
- references/authority-domains.json
- references/intent-guide.md
- scripts/chain_tracker.py
- scripts/fetch_thread.py
- scripts/relevance_gate.py
- scripts/search.py
Open the folder on GitHubat commit 331ecd3
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in aAAaqwq/AGI-Super-Team, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Search Layer 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 |
|---|---|---|---|---|---|---|
| Search Layer this skillaAAaqwq/AGI-Super-Team | 105 | 1 repos | ~1.9k | Automated safety check: Pass | MIT | |
| 9Router Web Searchdecolua/9router | 30k | — | ~1k | Automated safety check: Pass | MIT | |
| Web Searchtmustier/pi-for-excel | 434 | — | ~410 | Automated safety check: Pass | MIT | |
| Web SearchEXboys/skilllite | 170 | 3 repos | ~1k | Automated safety check: Pass | MIT | |
| Add Tavily Tool to NanoClawnanocoai/nanoclaw | 31k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Mysearchskernelx/MySearch-Proxy | 159 | — | ~1.4k | Automated safety check: Notes | MIT |
Similar skills
Web Search
tmustier/pi-for-excel
Search the public web for up-to-date facts. An agent skill from tmustier/pi-for-excel.
Web Search
EXboys/skilllite
Web search and content extraction with Tavily and Exa via inference.sh CLI.
Add Tavily Tool to NanoClaw
nanocoai/nanoclaw
Registers Tavily's keyless remote MCP server, search and extract, for selected NanoClaw agent groups by pinning an mcp-remote bridge and running it through an approval-gated config step.
Mysearch
skernelx/MySearch-Proxy
DEFAULT search skill for OpenClaw. An agent skill from skernelx/MySearch-Proxy.
Mysearch
skernelx/MySearch-Proxy
Install, verify, debug, and use MySearch MCP/Skill. An agent skill from skernelx/MySearch-Proxy.
More from aAAaqwq/AGI-Super-Team
All 152 skills in this repoContent Creator
aAAaqwq/AGI-Super-Team
Create SEO-optimized marketing content with consistent brand voice.
Financial Calculator
aAAaqwq/AGI-Super-Team
Advanced financial calculator with future value tables, present value, discount calculations, markup pricing, and compound interest.
Frontend Design Ultimate
aAAaqwq/AGI-Super-Team
Create distinctive, production-grade static sites with React, Tailwind CSS, and shadcn/ui — no mockups needed.
Sysadmin Toolbox
aAAaqwq/AGI-Super-Team
Tool discovery and shell one-liner reference for sysadmin, DevOps, and security tasks.
Zsxq Smart Publish
aAAaqwq/AGI-Super-Team
Publish and manage content on 知识星球 (zsxq.com). An agent skill from aAAaqwq/AGI-Super-Team.
Content Extract
aAAaqwq/AGI-Super-Team
Robust URL-to-Markdown extraction for OpenClaw workflows. An agent skill from aAAaqwq/AGI-Super-Team.
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Questions about Search Layer
What does Search Layer do?
四源同级：Brave (websearch) + Exa + Tavily + Grok。按意图自动选策略、调权重、做合成。. Search Layer is an agent skill from aAAaqwq/AGI-Super-Team. 四源同级：Brave (websearch) + Exa + Tavily + Grok。按意图自动选策略、调权重、做合成。 DEFAULT search tool for ALL search/lookup needs.
When should I use Search Layer?
Search Layer fits situations like: ANY query that requires web search — factual lookups; resource finding.
How do I install Search Layer in Claude Code?
Run `npx skills add aAAaqwq/AGI-Super-Team --skill search-layer -a claude-code`. Or copy the skill folder (skills/search-layer in aAAaqwq/AGI-Super-Team) into .claude/skills/search-layer in your project. Claude Code loads it when a task matches its description.
How do I install Search Layer in Codex?
Run `npx skills add aAAaqwq/AGI-Super-Team --skill search-layer -a codex`. Or copy the skill folder (skills/search-layer in aAAaqwq/AGI-Super-Team) into .agents/skills/search-layer in your project. Codex loads it when a task matches its description.
Can I use Search Layer 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 aAAaqwq/AGI-Super-Team --skill search-layer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/search-layer, .gemini/skills/search-layer, .github/skills/search-layer and .opencode/skills/search-layer in your project.
What does Search Layer need to run?
Going by SKILL.md and its folder, Search Layer needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named GROK_API_KEY. Our summary lists: Python 3; A credential in GROK_API_KEY.
Does Search Layer access the network?
SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Search Layer 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Search Layer use?
Search Layer 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 Search Layer use?
About 1.9k tokens (SKILL.md is roughly 7.7k 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 1.9k tokens, read only when the agent opens those files.
What are the alternatives to Search Layer?
Skills that share tags, products or a category with Search Layer: 9Router Web Search (decolua/9router, 30k stars), Web Search (tmustier/pi-for-excel, 434 stars), Web Search (EXboys/skilllite, 170 stars) and Add Tavily Tool to NanoClaw (nanocoai/nanoclaw, 31k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Search Layer?
aAAaqwq (a GitHub user) maintains it in aAAaqwq/AGI-Super-Team, which has 105 GitHub stars. The repository holds 152 skills in this directory. The repository was last updated on September 27, 2026.
Source: aAAaqwq/AGI-Super-Team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.