Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tavily-ai/skills --skill tavily-researchgit clone --depth 1 https://github.com/tavily-ai/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tavily-ai/skills/tavily-research)<a href="https://agentmods.dev/skills/tavily-ai/skills/tavily-research"><img src="https://agentmods.dev/badge/skills/tavily-ai/skills/tavily-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tavily-ai/skills/tavily-research"><img src="https://agentmods.dev/badge/skills/tavily-ai/skills/tavily-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.01003 |
| Opus 5 | $0.00055 | $0.00502 |
| Sonnet 5 | $0.00022 | $0.00201 |
| Haiku 4.5 | $0.00011 | $0.00100 |
Grade A, and why
tavily-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tavily research
AI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.
Before running
Research requires authentication. Run the requested command directly when
tvly is already authenticated; do not add a status check to every invocation.
If tvly is missing, follow the tavily-cli setup.
If an installed CLI reports an authentication error, use tvly login for
authentication only, or tvly init --skip-skills when guided verification is
also useful. Browser-based OAuth is preferred when an interactive user can
complete it. --no-browser prints the sign-in link instead of opening it, but
still waits for a localhost callback. In an unattended agent or CI environment,
leave authentication to the user or use a securely provided TAVILY_API_KEY.
Do not start a second login immediately after guided setup has completed.
When to use
- You need comprehensive, multi-source analysis
- The user wants a comparison, market report, or literature review
- Quick searches aren't enough — you need synthesis with citations
- Step 5 in the workflow: search → extract → map → crawl → research
Quick start
# Basic research (waits for completion)
tvly research "competitive landscape of AI code assistants"
# Pro model for comprehensive analysis
tvly research "electric vehicle market analysis" --model pro
# Stream results in real-time
tvly research "AI agent frameworks comparison" --stream
# Save report to file
tvly research "fintech trends 2025" --model pro -o fintech-report.json
# JSON output for agents
tvly research "quantum computing breakthroughs" --json
Options
| Option | Description |
|---|---|
--model |
mini, pro, or auto (default) |
--stream |
Stream results in real-time |
--no-wait |
Return request_id immediately (async) |
--output-schema |
Path to JSON schema for structured output |
--citation-format |
numbered, mla, apa, chicago |
--poll-interval |
Seconds between checks (default: 10) |
--timeout |
Max wait seconds (default: 600) |
-o, --output |
Save the JSON response to a file |
--json |
Structured JSON output |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago Changed · +2 lines scan C → A d62b2b1ba980
- 12d ago First seen · 101 lines · 109 tokens per session scan C e24618857161
tavily-research is a skill published in the GitHub repository tavily-ai/skills (479 stars, last pushed 7d ago), licensed MIT. It adds 109 tokens to every session and 1,003 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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