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 BenedictKing/benedictking-skills --skill tavily-webgit clone --depth 1 https://github.com/BenedictKing/benedictking-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/benedictking/benedictking-skills/tavily-web)<a href="https://agentmods.dev/skills/benedictking/benedictking-skills/tavily-web"><img src="https://agentmods.dev/badge/skills/benedictking/benedictking-skills/tavily-web/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/benedictking/benedictking-skills/tavily-web"><img src="https://agentmods.dev/badge/skills/benedictking/benedictking-skills/tavily-web.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00034 | $0.01224 |
| Opus 5 | $0.00017 | $0.00612 |
| Sonnet 5 | $0.00007 | $0.00245 |
| Haiku 4.5 | $0.00003 | $0.00122 |
Grade A, and why
tavily-web scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
## Payload Examples (Based on Provided curl) How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tavily Web Skill
Trigger Conditions & Endpoint Selection
Choose Tavily endpoint based on user intent:
- search: Need to "search web / latest info / find sources / find links"
- extract: Given URL(s), need to extract/summarize content
- crawl: Need to traverse site following instructions and scrape page content
- map: Need to discover site page list/structure (without full content or metadata only)
- research: Need structured research output following given
output_schema
Recommended Architecture (Main Skill + Sub-skill)
This skill uses a two-phase architecture:
- Main skill (current context): Understand user question → Choose endpoint → Assemble JSON payload
- Sub-skill (fork context): Only responsible for HTTP call execution, avoiding conversation history token waste
Execution Method
Use Task tool to invoke tavily-fetcher sub-skill, passing command and JSON (stdin):
Task parameters:
- subagent_type: Bash
- description: "Call Tavily API"
- prompt: cat <<'JSON' | node scripts/tavily-api.cjs <search|extract|crawl|map|research>
{ ...payload... }
JSON
Payload Examples (Based on Provided curl)
1) Search the web
cat <<'JSON' | node scripts/tavily-api.cjs search
{
"query": "who is Leo Messi?",
"auto_parameters": false,
"topic": "general",
"search_depth": "basic",
"chunks_per_source": 3,
"max_results": 1,
"time_range": null,
"start_date": "2025-02-09",
"end_date": "2025-12-29",
"include_answer": false,
"include_raw_content": false,
"include_images": false,
"include_image_descriptions": false,
"include_favicon": false,
"include_domains": [],
"exclude_domains": [],
"country": null,
"include_usage": false
}
JSON
2) Extract webpages
cat <<'JSON' | node scripts/tavily-api.cjs extract
{
"urls": "https://en.wikipedia.org/wiki/Artificial_intelligence",
"query": "<string>",
"chunks_per_source": 3,
"extract_depth": "basic",
"include_images": false,
"include_favicon": false,
"format": "markdown",
"timeout": "None",
"include_usage": false
}
JSON
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 186 lines · 34 tokens per session scan A 030d3e002220
tavily-web is a skill published in the GitHub repository BenedictKing/benedictking-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 1,224 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
My Skill
Content here.
task-generation
Reference material with the canonical task-format grammar and decomposition rules for plan-to-tasks expansion. Loaded on demand by generate-tasks; not directly invokable.
implementation-standards
Reference material with coding standards (defensive coding, error handling, testing patterns). Loaded on demand by the Developer sub-agent (.github/agents/developer.md); not directly invokable.
spec-authoring
Reference material for writing product, technical, and operational specifications (work-item priorities, requirement families, success criteria). Loaded on demand by specify-feature; not directly invokable.
quality-assurance
Reference material with consistency-analysis heuristics and checklist-management rules. Loaded on demand by analyze-compliance and quality-control; not directly invokable.
writing-quality
Removes common AI writing patterns while preserving meaning, evidence, structure, and SDDP traceability. Ambient through AGENTS.md; not directly invokable.