Agent Skills

tavily-dynamic-search

researchtavily-ai11K installs

Programmatic Tavily search with context isolation. Use when web search or extraction will return large results that need filtering, deduplication, or multi-step triage before they enter the model context. Use tavily-search for ordinary lookups and tavily-research for end-to-end cited synthesis.

Install

npx skills add https://github.com/tavily-ai/skills --skill tavily-dynamic-search
SKILL.md

Tavily Dynamic Search

Keep large raw web payloads on disk and return only the evidence needed for the task. This is useful when using --include-raw-content, combining several queries, or extracting multiple long pages. Do not use this workflow for a simple lookup that a normal tvly search --json can answer directly.

Before running

Search and extract support capped keyless access. Run them directly when tvly is available. If tvly is missing, follow the tavily-cli setup. Do not look for an API key or authenticate before the first request. If the keyless cap is reached in an interactive session, run tvly login to open browser OAuth, then retry the blocked request once. In an unattended environment, report the cap and authentication options instead of starting an interactive flow.

Workflow

  1. Search broadly without raw content and inspect titles, URLs, scores, and snippets.
  2. Fetch full content only for the best sources.
  3. When raw output could be large, save it with -o and filter the file before printing anything to the model context.
  4. Preserve source URLs beside every extracted fact.

When the user restricts evidence to official or named domains, validate the hostname of every selected URL during local filtering. --include-domains narrows the search but is not proof that every returned result belongs to an allowed host. If full-page extraction is unavailable, label conclusions as search-snippet evidence instead of implying that the page body was verified.

Keep the process in one turn when the relevant sources and filters are already known. Use another turn only when the first search changes what should be extracted.

Create a unique temporary task directory before saving evidence so concurrent agents do not overwrite one another. Python's tempfile.mkdtemp() is available when mktemp is not permitted. Reuse that directory for all raw and filtered artifacts from the task.

Small result: filter a direct JSON response

For a small search response, a pipe is enough:

tvly search "query" --max-results 5 --json | python3 -c '
import json, sys
data = json.load(sys.stdin)
for result in data.get("results", []):
    score = result.get("score") or 0
    title = result.get("title") or ""
    print(f"[{score:.2f}] {title}")
    print(result.get("url", ""))
    print(result.get("content", "")[:300])
'

Do not discard stderr. Authentication failures, keyless-cap messages, and API errors are actionable and must remain visible.

Large result: save first, then filter

Use the CLI's file output so raw page content does not pass through the tool response:

tvly search "query" \
  --include-raw-content markdown \
  --max-results 8 \
  --json \
  -o /tmp/tavily-search-results.json

Then print only bounded evidence:

python3 -c '
import json
from pathlib import Path

data = json.loads(Path("/tmp/tavily-search-results.json").read_text())
for result in data.get("results", []):
    title = result.get("title") or ""
    url = result.get("url") or ""
    print(f"## {title}")
    print(f"URL: {url}")
    print((result.get("raw_content") or result.get("content") or "")[:1200])
    print()
'

Adjust the filtering logic to the question. Prefer relevant paragraphs or fields over fixed character slices when the target information is known. Aim for roughly 150-600 tokens per source unless a table or code block genuinely requires more.

Targeted extraction

When search identifies the right URLs, extract only those pages:

tvly extract "https://example.com/article" \
  --json \
  -o /tmp/tavily-extract-results.json

For topic-focused pages, let Tavily reduce the response before local filtering:

tvly extract "https://example.com/docs" \
  --query "authentication API" \
  --chunks-per-source 3 \
  --json \
  -o /tmp/tavily-extract-results.json

Multiple queries

For multi-angle research, run a small set of focused searches, deduplicate by URL, and rank before extracting. Use subprocess.run(..., capture_output=True, text=True) when orchestrating commands in Python. Check returncode; if a command fails, surface its stderr and stop or retry deliberately. Never use a blanket except Exception: continue that hides missing evidence.

Response shapes

tvly search --json returns query, optional answer, results, and response_time. Each result commonly contains url, title, content, score, and optional raw_content.

tvly extract --json returns results, failed_results, and response_time. Each successful result commonly contains url, raw_content, and optional images.

Treat fields as optional and use .get() while filtering. Inspect failed_results instead of assuming every requested URL succeeded.

Useful options

Option Purpose
--max-results Bound the search result count; default 5, maximum 20
--depth Choose ultra-fast, fast, basic, or advanced
--time-range Restrict results to day, week, month, or year
--include-domains Restrict results to a comma-separated list of trusted domains
--exclude-domains Exclude a comma-separated list of domains
--include-raw-content Include full content as markdown or text
-o, --output Save the complete response to a file

Use jq only for short filters when Python is unavailable:

tvly search "query" --json | jq '[.results[] | {title, url, score, content}]'

Related skills

researchmattpocock575KInvestigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.paper-context-resolverlllllllama451KRigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing Renv-and-assets-bootstraplllllllama450KRigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.ai-research-explorelllllllama311KRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow c

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers