Tavily Web Search
allenpeng0705/EnvoyMesh
Searches the web through the Tavily API with LLM-friendly output: clean structured results, optional AI-written answers, domain filters, news mode, images and raw content.
Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines.
SKILL.md written in Chinese; this summary is our English description.
Install Argo Search and Verification
$ npx skills add taxueseek/argo --skill argo -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install taxueseek/argo argo --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
Claude Code skills documentation · loads skills from .claude/skills/
Install the "argo" agent skill from https://github.com/taxueseek/argo/tree/main into .claude/skills/argo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "argo", 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.
$ npx skills add taxueseek/argo --skill argo -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install taxueseek/argo argo --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "argo" agent skill from https://github.com/taxueseek/argo/tree/main into .agents/skills/argo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "argo", 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 taxueseek/argo --skill argo -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install taxueseek/argo argo --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "argo" agent skill from https://github.com/taxueseek/argo/tree/main into .cursor/skills/argo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "argo", 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.
$ npx skills add taxueseek/argo --skill argo -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install taxueseek/argo argo --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "argo" agent skill from https://github.com/taxueseek/argo/tree/main into .gemini/skills/argo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "argo", 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 taxueseek/argo argoInstalls 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 taxueseek/argo --skill argo -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "argo" agent skill from https://github.com/taxueseek/argo/tree/main into .github/skills/argo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "argo", 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 taxueseek/argo --skill argo -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install taxueseek/argo argo --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "argo" agent skill from https://github.com/taxueseek/argo/tree/main into .opencode/skills/argo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "argo", 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
argo- GitHub stars
- 188
- Token cost
- ~1.2k tokens
- SKILL.md length
- 239 words
- Files
- 710 (incl. scripts, references, assets)
- Skills in repo
- 5
- Repo updated
- First seen
- Licence
- MIT
At a glance
Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines.
- Works in 7 steps: 高后果问题（金融/医疗/法律/事实核查）：search →… → 数字：必须标注算法（全市场/主动/持仓市值 vs… → SERP 链（baidu/s、sogou/link）：禁止当正文来源 → …
- Searching the web and checking the evidence before drawing a conclusion
- SKILL.md covers 快速上手, 核心命令, Agent 执行纪律 and Gotchas, plus 1 more section
- Calls python3
What it does
Argo goes beyond finding results. For high-stakes questions it marks them fetch_required, flags results as fetch_suggested, and with --verify it fetches the body text of the top results and writes back an evidence score. It covers 264 sources, 229 of which work without API keys, and scripts/search.py routes between fast, auto, deep and budget modes, with options for domain, time window, sorting and a compact JSON profile meant for agents.
A research command produces an evidence dossier (sources, coverage, gaps and whether the bar is met) rather than a verdict, following a research-protocol reference. Fetch and screenshot commands handle web pages with a fallback chain and an optional browser, an academic mode searches several scholarly sources at once, local image search is off by default, and a social-sentiment mode can look at platforms such as Xiaohongshu, Reddit and Twitter.
Agent rules require fetching high-scoring URLs before concluding on finance, medical, legal or fact-checking questions, stating the algorithm behind any figure and never merging numbers that were counted differently. Search-result pages are not accepted as body sources, and social posts count as narrative, not fact.
When your agent uses it
- Searching the web and checking the evidence before drawing a conclusion
- Running deep research that returns a dossier with sources and gaps
- Fetching or screenshotting a web page with fallbacks
- Searching academic papers across several sources at once
- Reading public sentiment on social platforms
Example prompts
- “搜索并核实最近一次贷款市场报价利率的调整，并核验前三条结果。”
- “Run a deep research on solid-state battery makers and give me the evidence dossier with its gaps.”
- “Search for papers on retrieval-augmented generation in academic deep mode and list the sources used.”
Requirements
- Python 3, to run scripts/search.py
- Network access to the search sources
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- 高后果问题（金融/医疗/法律/事实核查）：search → evidence（或看 credibility_fast）→ fetch 高分 URL → 再下结论；fetch_required=true 时禁止跳过核验
- 数字：必须标注算法（全市场/主动/持仓市值 vs 占比）；冲突时并列，禁止算法未对齐就合并
- SERP 链（baidu/s、sogou/link）：禁止当正文来源
- 社交帖：叙事/舆情，不进事实真值
- 深度研究：先读 references/research-protocol.md；有决策含义就交工作包，不要靠扩词充问题树；quality_gate_results.passed=false 必须降级表述
- 引用：讲给用户的事实带 URL 出处，日常档也要带（URL 在 results[].url，零成本）
- 上下文纪律：Agent 搜索用 --json --fields agent、按需 -n；要来源追溯或归档才加 --envelope；查引擎状态用 --list-engines --detail --engine <名>（单引擎 ~0.9 KB）。踩坑记录见下方 Gotchas
What it can do on your machine
Read from SKILL.md and the folder at commit a6657b0. 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 1 file in scripts/, 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
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
Argo Search and Verification loads about 1.2k tokens when it runs, and up to ~22k if it reads all its reference files. Until then it costs about 27 tokens; SKILL.md has 239 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 taxueseek/argo at commit a6657b0, republished under its MIT licence (© taxueseek). 239 words, ~1,213 tokens.
Download SKILL.mdSave it as
.claude/skills/argo/SKILL.md (or your agent's skills folder). This skill also uses 709 other files; get the full folder from GitHub.- name
- argo
- description
- Argo 阿尔戈 — 统一搜索、网页抓取与证据核验。当需要搜索/查一下/核实/抓取网页/爬取/深度研究/论文检索/新闻/舆情/公众号文章/招聘聚合时使用；支持影视/体育/金融/宏观/学术等垂直域与多语言查询。
- version
- 2.9.3
- triggers
- 搜索, 查一下, 搜一下, 核实, 查证, 可信度, 抓取, 爬取, 深度研究, 论文, 舆情, 公众号, 招聘, search for, look up, fact check, fetch, crawl, research
Argo — 统一搜索与证据核验
不止「帮你搜到」，还要「帮你核到」：高后果问题标
fetch_required、结果标fetch_suggested，--verify核验正文并回填证据分。收录 264 个源、229 个免密钥开箱可用。
快速上手
python3 scripts/search.py "查询词" # 自动路由搜索
python3 scripts/search.py "查询词" --json --fields agent # Agent 消费档
python3 scripts/search.py "查询词" --verify 3 # 核验 top-3 并回填证据分
python3 scripts/research.py "复杂问题" --json # 取证包（扩词或多工作包 → dossier）默认不附归档用的 candidates/sources；--fields agent 剥遥测只留答案（Agent 消费档，~4.3 KB）；
要来源追溯或归档才加 --envelope（--archive 自动带上）。
深度研究只此一条。机器产出取证包（dossier）：来源、覆盖、缺口、是否达标，不是判断稿。Agent 先读 references/research-protocol.md（含多轨道「广泛研究」节），写出工作包再取证；判断按事实/推断/建议写。不要另装「专业深度研究」skill。
核心命令
search — 统一搜索
| 参数 | 说明 |
|---|---|
--engine <name> | 强制引擎（anysearch/byted/bocha/exa/tavily/eastmoney/zhihu/arxiv/pypi/mdn/hackernews/v2ex/redskill…，全量见 --list-engines） |
--local-first | 本地零成本聚合优先（local_search 29 引擎，27 默认启用） |
--include-local / --no-local | 本机文件命中（source=local_files，score 0.9/0.7）：fast/budget 默认开，auto/deep 显式 |
| `--mode fast | auto |
--explain | 解释路由决策（含 TF-IDF 分数） |
--no-cache / `--depth fast | balanced |
--academic-deep | 学术多源模式：自动设置 depth=deep + domain=academic，禁用 early-stop 让所有学术源参与（arxiv/openalex/local_pubmed/core 等），适合深度研究场景 |
| `--since 7d | 2026-08-01 --until --sort relevance |
--verify [N] | 对 top-N 未核验结果 fetch 正文，回填证据分（URL→证据分缓存，同 URL 二次搜索自动复用） |
--domain --sub_domain | 垂直域 / 子域限定 |
图片检索
网络图走 search（image_search 域自动命中）；本地素材走 argo local-image
（Vision 索引 + --similar-to 找相似图 + --sheet 出联络表交多模态模型判断）。
本地图默认关闭，需 ARGO_LOCAL_IMAGE=1；详见 references/usage.md。
增强三工具
# research — 取证（扩词或 --work-packages → 取证包 + 引用 + 达标检查）
# 工作包可带 file_inputs（本地一手数据入账）+ recompute（可复算脚本，默认拒绝，需显式授权）
# 社交舆情：--mode social-sentiment --platforms xiaohongshu,reddit,twitter
python3 scripts/research.py "查询" [--work-packages PATH|JSON] [--depth deep] [--json] [--verify N]
# evidence — 可信度评估（选拔×吸收两维）
echo '{"results": [...]}' | python3 scripts/evidence.py "查询词" --stdin --json [--high-stakes]
# clarify — 意图消歧
python3 scripts/clarify.py "有歧义的查询" --json抓取族（bin/argo 入口）
argo fetch "https://example.com" [--focus "关键词"] [--use-browser]
# 降级链见 references/usage.md
argo screenshot "https://example.com" [--full-page]
argo pdf "https://example.com/paper.pdf" [--pages "1-5"] [--password "secret"]
argo paper "1706.03762" # 论文深读
argo tweet "<x-url|id>" # X 帖子打包（正文+串/引用/转发+媒体）
argo answer "query" [--scope <语料>] # 直答（语料见 usage.md）
argo watch add|check|list|remove # 观察模式（--json 供 cron）Agent 执行纪律
- 高后果问题（金融/医疗/法律/事实核查）：search → evidence（或看
credibility_fast）→ fetch 高分 URL → 再下结论；fetch_required=true时禁止跳过核验 - 数字：必须标注算法（全市场/主动/持仓市值 vs 占比）；冲突时并列，禁止算法未对齐就合并
- SERP 链（baidu/s、sogou/link）：禁止当正文来源
- 社交帖：叙事/舆情，不进事实真值
- 深度研究：先读
references/research-protocol.md；有决策含义就交工作包，不要靠扩词充问题树；quality_gate_results.passed=false必须降级表述 - 引用：讲给用户的事实带 URL 出处，日常档也要带（URL 在
results[].url，零成本） - 上下文纪律：Agent 搜索用
--json --fields agent、按需-n；要来源追溯或归档才加--envelope；查引擎状态用--list-engines --detail --engine <名>（单引擎 ~0.9 KB）。踩坑记录见下方 Gotchas
Gotchas
踩到新坑加一行，口径见 references/usage.md。
--envelope与--fields agent别同给：后者会把 envelope 的增量剥成 0 字节——以为拿到了 provenance、实际没有。要 provenance 就去掉--fields agent。- 结果异常少：看
funnel六格阶段计数，哪格归零即塌陷点。 - 慢查询：看
timing.dispatch的useful_ms/wasted_ms，区分「等答案」与「白等」。 --list-engines别直接--all：全量 ~54 KB 会灌爆版面；默认摘要 ~2.5 KB 够用。-n超 10 无收益。
按需读取（低频操作细节）
以下按需打开；日常搜索/抓取/研究走上面核心命令即可。
| 场景 | 读什么 |
|---|---|
| MCP 工具全清单 / 多客户端注入 / DSH 插件接入 / 配额·TinyFish / 子技能 / 本地打通 / 工程纪律 | references/operations.md |
| 使用指南：全命令、参数、86 开关总表、输出体积陷阱、日志反馈 | references/usage.md |
| 深度研究协议：约定、工作包、取证包 vs 判断稿、达标检查 | references/research-protocol.md |
| 约定 / 工作包 / 判断稿模板 | references/research-templates.md |
| 引擎全景：垂直域/社交/学术/本地引擎表 + 路由规则 | references/engines.md |
| 学术检索：查询构造（arXiv/S2/GS 语法）、相关性五因子排序、引用网络挖掘、学术反模式与证据分级 | references/academic-query.md |
| 架构：文件结构、证据流水线、量化公式、输出 JSON Schema、内容质量信号 | references/architecture.md |
| MCP 多客户端注入详解 | docs/MCP_SETUP.md |
| 搜索源使用文档：全量清单（费用 / 密钥 / 状态 / 域组合）+ 特别能力 + 打开方式 | docs/ENGINE_CATALOG.md（生成，勿手改） |
工程纪律（每个事实只定义一处：代码看本仓库、引擎声明看 config.yaml、宿主入口用 link_source.py 建软链、新增源流程）见
references/operations.md末尾。
© taxueseek, 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 709 other files (scripts, references, assets) in the repository root of taxueseek/argo.
- SKILL.md
- .gitignore
- .npmignore
- LICENSE
- README.en.md
- README.es.md
- README.ja.md
- README.ko.md
- README.md
- assets/readme/hero.svg
- assets/readme/made-with-beautify.svg
- assets/readme/proof-routes.svg
- assets/readme/why-better.svg
- assets/readme/workflow.svg
- backends/domain_profiles.json
- backends/engine_registry.yaml
- backends/query_synonyms_cn.json
- backends/quota_profiles.json
- … and 692 more
Open the folder on GitHubat commit a6657b0
Compare with similar skills
Argo Search and Verification 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 |
|---|---|---|---|---|---|---|
| Argo Search and Verification this skilltaxueseek/argo | 188 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Tavily Web Searchallenpeng0705/EnvoyMesh | 3.1k | 3 repos | ~2.5k | Automated safety check: Notes | None | |
| Ray Trend Searchimraywang/rayskills | 159 | — | ~2.1k | Automated safety check: Pass | Custom licence | |
| Insane Searchfivetaku/gptaku-plugins-codex | 128 | — | ~5.6k | Automated safety check: Pass | MIT | |
| Agent ReachPanniantong/Agent-Reach | 95k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Qiaomu Opencli Usagejoeseesun/qiaomu-opencli-skills | 993 | — | ~3k | Automated safety check: Pass | MIT |
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Works with
Questions about Argo Search and Verification
What does Argo Search and Verification do?
Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines. Argo goes beyond finding results. For high-stakes questions it marks them fetch_required, flags results as fetch_suggested, and with --verify it fetches the body text of the top results and writes back an evidence score.
When should I use Argo Search and Verification?
Argo Search and Verification fits situations like: searching the web and checking the evidence before drawing a conclusion; running deep research that returns a dossier with sources and gaps; fetching or screenshotting a web page with fallbacks; searching academic papers across several sources at once.
How do I install Argo Search and Verification in Claude Code?
Run `npx skills add taxueseek/argo --skill argo -a claude-code`. Or copy the skill folder (the taxueseek/argo repository) into .claude/skills/argo in your project. Claude Code loads it when a task matches its description.
How do I install Argo Search and Verification in Codex?
Run `npx skills add taxueseek/argo --skill argo -a codex`. Or copy the skill folder (the taxueseek/argo repository) into .agents/skills/argo in your project. Codex loads it when a task matches its description.
Can I use Argo Search and Verification 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 taxueseek/argo --skill argo -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/argo, .gemini/skills/argo, .github/skills/argo and .opencode/skills/argo in your project.
What does Argo Search and Verification need to run?
Going by SKILL.md and its folder, Argo Search and Verification needs the command-line tools its instructions call (python3). Our summary lists: Python 3, to run scripts/search.py; Network access to the search sources.
Does Argo Search and Verification 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 Argo Search and Verification 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 Argo Search and Verification use?
Argo Search and Verification is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Argo Search and Verification use?
About 1.2k tokens (SKILL.md is roughly 4.9k 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 20k tokens, read only when the agent opens those files.
What are the alternatives to Argo Search and Verification?
Skills that share tags, products or a category with Argo Search and Verification: Tavily Web Search (allenpeng0705/EnvoyMesh, 3.1k stars), Ray Trend Search (imraywang/rayskills, 159 stars), Insane Search (fivetaku/gptaku-plugins-codex, 128 stars) and Agent Reach (Panniantong/Agent-Reach, 95k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Argo Search and Verification?
taxueseek (a GitHub user) maintains it in taxueseek/argo, which has 188 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 6, 2026.
Source: taxueseek/argo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.