Argo Search and Verification
taxueseek/argo
Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines.
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.
Install Tavily Web Search
$ npx skills add allenpeng0705/EnvoyMesh --skill tavily -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install allenpeng0705/EnvoyMesh tavily --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/allenpeng0705/EnvoyMesh.git skills-src && mkdir -p .claude/skills && cp -r skills-src/apps/node/skills/tavily .claude/skills/tavily && 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 "tavily" agent skill from https://github.com/allenpeng0705/EnvoyMesh/tree/main/apps/node/skills/tavily into .claude/skills/tavily/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tavily", 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/allenpeng0705/EnvoyMesh/tree/main/apps/node/skills/tavilyType 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 allenpeng0705/EnvoyMesh --skill tavily -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install allenpeng0705/EnvoyMesh tavily --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/allenpeng0705/EnvoyMesh.git skills-src && mkdir -p .agents/skills && cp -r skills-src/apps/node/skills/tavily .agents/skills/tavily && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tavily" agent skill from https://github.com/allenpeng0705/EnvoyMesh/tree/main/apps/node/skills/tavily into .agents/skills/tavily/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tavily", 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 allenpeng0705/EnvoyMesh --skill tavily -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install allenpeng0705/EnvoyMesh tavily --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/allenpeng0705/EnvoyMesh.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/apps/node/skills/tavily .cursor/skills/tavily && 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 "tavily" agent skill from https://github.com/allenpeng0705/EnvoyMesh/tree/main/apps/node/skills/tavily into .cursor/skills/tavily/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tavily", 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/allenpeng0705/EnvoyMesh.git --path apps/node/skills/tavily--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 allenpeng0705/EnvoyMesh --skill tavily -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install allenpeng0705/EnvoyMesh tavily --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/allenpeng0705/EnvoyMesh.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/apps/node/skills/tavily .gemini/skills/tavily && 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 "tavily" agent skill from https://github.com/allenpeng0705/EnvoyMesh/tree/main/apps/node/skills/tavily into .gemini/skills/tavily/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tavily", 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 allenpeng0705/EnvoyMesh tavilyInstalls 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 allenpeng0705/EnvoyMesh --skill tavily -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/allenpeng0705/EnvoyMesh.git skills-src && mkdir -p .github/skills && cp -r skills-src/apps/node/skills/tavily .github/skills/tavily && 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 "tavily" agent skill from https://github.com/allenpeng0705/EnvoyMesh/tree/main/apps/node/skills/tavily into .github/skills/tavily/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tavily", 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 allenpeng0705/EnvoyMesh --skill tavily -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install allenpeng0705/EnvoyMesh tavily --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/allenpeng0705/EnvoyMesh.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/apps/node/skills/tavily .opencode/skills/tavily && 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 "tavily" agent skill from https://github.com/allenpeng0705/EnvoyMesh/tree/main/apps/node/skills/tavily into .opencode/skills/tavily/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tavily", 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
tavily- GitHub stars
- 3.1k
- Used in
- 3 other repos
- Token cost
- ~2.5k tokens
- SKILL.md length
- 632 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 1
- Repo updated
- First seen
- Licence
- None found
At a glance
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.
- Works in 9 steps: Research & Fact-Finding → Current Events → Domain-Specific Research → …
- Looking up current events with a news-focused search
- SKILL.md covers Overview, Architecture, Quick Start and Search Modes, plus 4 more sections
- Runs Python scripts from its folder; calls pip, jq and python3; needs TAVILY_API_KEY
What it does
The bundled `scripts/tavily_search.py` takes a query and returns structured results, with an AI-generated answer summary if requested. Two depths are available: `basic`, a fast mode of roughly one to two seconds suited to quick facts and definitions, and `advanced`, which takes five to ten seconds and suits research, complex topics and cases where several perspectives help. A decision tree in the skill picks between them and favors basic for time-sensitive queries.
Topics can be general, covering all time and broad web content, or news, which looks at the last seven days of news sites and is the choice when a query says latest, recent, current or today. Further options include including or excluding specific domains, adding relevant images and extracting raw page content for deeper analysis. A reference file documents the API, and the API key setup section starts with adding the key to the Clawdbot config.
When your agent uses it
- Looking up current events with a news-focused search
- Researching a topic with answer summaries and source links
- Restricting a search to trusted domains
- Fact-checking a claim against several sources
Example prompts
- “Search the news for the latest developments in battery recycling.”
- “Do an advanced search on climate change solutions and summarize it with sources.”
- “Search only python.org and docs.python.org for asyncio tutorials.”
- “Find images and facts about the architecture of the Eiffel Tower.”
Requirements
- A Tavily API key
- Python, to run `scripts/tavily_search.py`
Workflow steps
9 steps, taken from the step headings in SKILL.md.
- Research & Fact-Finding
- Current Events
- Domain-Specific Research
- Visual Research
- Content Extraction
- Choose the Right Search Depth
- Use Domain Filtering Strategically
- Optimize for Cost
- Handle Errors Gracefully
What it can do on your machine
Read from SKILL.md and the folder at commit 92484b2. 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/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pipjqpython3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
tavily.comgithub.comdocs.tavily.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
TAVILY_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Tavily Web Search loads about 2.5k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 632 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: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NoteMentions a .env fileSKILL.md:160
Add to `~/.clawdbot/.env` or your shell profile.
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
Without a licence we can't republish the file, so here is its outline and opening line. It has 632 words (~2,486 tokens).
“Tavily is a search engine specifically optimized for Large Language Models and AI applications. Unlike traditional search APIs, Tavily provides AI-ready results with optional answer generation, clean content extraction, and domain filtering capabilities.”
- name
- tavily
Files
SKILL.md and 5 other files (scripts, references) in apps/node/skills/tavily of allenpeng0705/EnvoyMesh.
- SKILL.md
- .clawhub/origin.json
- _meta.json
- references/api-reference.md
- scripts/tavily_search.py
- skill-card.md
Open the folder on GitHubat commit 92484b2
Used in 3 other repositories
We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in allenpeng0705/EnvoyMesh, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Tavily Web Search 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 |
|---|---|---|---|---|---|---|
| Tavily Web Search this skillallenpeng0705/EnvoyMesh | 3.1k | 3 repos | ~2.5k | Automated safety check: Notes | None | |
| Argo Search and Verificationtaxueseek/argo | 188 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Ray Trend Searchimraywang/rayskills | 159 | — | ~2.1k | Automated safety check: Pass | Custom licence | |
| Multi Source Searchsandbaseai/sandbase-skills | 203 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| AI RAG PipelineNeverSight/learn-skills.dev | 217 | 1 repos | ~2k | Automated safety check: Pass | None | |
| Perplexity SearchescapeWu/perplexity-ai | 171 | — | ~1.8k | Automated safety check: Pass | MIT |
Similar skills
Ray Trend Search
imraywang/rayskills
Researches what people are saying about a topic over a recent window across X, Reddit, YouTube and the public web, reporting each source's status with links.
Multi Source Search
sandbaseai/sandbase-skills
Portable multi-source research with cross-source validation and an offline evidence ledger.
AI RAG Pipeline
NeverSight/learn-skills.dev
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs.
Perplexity Search
escapeWu/perplexity-ai
Searches the live web with citations through perplexity-mcp v2 tools or a bundled Python REST client, with focused ask, deep research and detached tasks.
Tavily Deep Research
sandbaseai/sandbase-skills
Advanced web search, content extraction, and site mapping through Tavily via SandBase.
Related
Questions about Tavily Web Search
What does Tavily Web Search do?
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. py` takes a query and returns structured results, with an AI-generated answer summary if requested. Two depths are available: `basic`, a fast mode of roughly one to two seconds suited to quick facts and definitions, and `advanced`, which takes five to ten seconds and suits research, complex topics and cases where several perspectives help.
When should I use Tavily Web Search?
Tavily Web Search fits situations like: looking up current events with a news-focused search; researching a topic with answer summaries and source links; restricting a search to trusted domains; fact-checking a claim against several sources.
How do I install Tavily Web Search in Claude Code?
Run `npx skills add allenpeng0705/EnvoyMesh --skill tavily -a claude-code`. Or copy the skill folder (apps/node/skills/tavily in allenpeng0705/EnvoyMesh) into .claude/skills/tavily in your project. Claude Code loads it when a task matches its description.
How do I install Tavily Web Search in Codex?
Run `npx skills add allenpeng0705/EnvoyMesh --skill tavily -a codex`. Or copy the skill folder (apps/node/skills/tavily in allenpeng0705/EnvoyMesh) into .agents/skills/tavily in your project. Codex loads it when a task matches its description.
Can I use Tavily Web Search 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 allenpeng0705/EnvoyMesh --skill tavily -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tavily, .gemini/skills/tavily, .github/skills/tavily and .opencode/skills/tavily in your project.
What does Tavily Web Search need to run?
Going by SKILL.md and its folder, Tavily Web Search needs Python for the scripts in its folder, the command-line tools its instructions call (pip, jq and python3) and credentials named TAVILY_API_KEY. Our summary lists: A Tavily API key; Python, to run `scripts/tavily_search.py`.
Does Tavily Web Search access the network?
SKILL.md names 3 domains. As links in the text: tavily.com, github.com and docs.tavily.com. This is read from the text; nothing was executed.
Is Tavily Web Search safe to install?
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. 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 Tavily Web Search use?
No licence was found for Tavily Web Search or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Tavily Web Search use?
About 2.5k tokens (SKILL.md is roughly 9.9k 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 1.3k tokens, read only when the agent opens those files.
What are the alternatives to Tavily Web Search?
Skills that share tags, products or a category with Tavily Web Search: Argo Search and Verification (taxueseek/argo, 188 stars), Ray Trend Search (imraywang/rayskills, 159 stars), Multi Source Search (sandbaseai/sandbase-skills, 203 stars) and AI RAG Pipeline (NeverSight/learn-skills.dev, 217 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tavily Web Search?
allenpeng0705 (a GitHub user) maintains it in allenpeng0705/EnvoyMesh, which has 3,100 GitHub stars. The repository was last updated on October 10, 2026.
Source: allenpeng0705/EnvoyMesh on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.