wkylin/pro-react-admin/tavily-dynamic-search

Claude Code and agent skill by wkylin/pro-react-admin

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Programmatic web search with context isolation. Use this skill for any research task where you need to search the web, filter results, and extract specific information — without polluting your context window with raw HTML and boilerplate. This is the default skill for web research. Triggered by \"search for\", \"look up\", \"find\", \"research\", \"what's the latest on\", or any query that requires current web information. Also use when asked to \"search and filter\", \"find the important parts\", or \"extract the key details\" — any case where the user wants curated, noise-free content.\n

Install

npx vskill@latest install wkylin/pro-react-admin/tavily-dynamic-search

SKILL.md

Tavily Dynamic Search

Search the web, filter results, and extract content so that raw search data never enters your context window. Only your curated print() output comes back.

Why this matters

A typical tvly search --include-raw-content returns 8 results × 30-50K chars each = ~300K characters of raw page content. If this enters your context window, you burn tokens reading navigation bars, cookie banners, and boilerplate — and your reasoning quality degrades under the noise. By processing results inside a Python script, only your print() output enters context — typically 1-3K characters of pure signal. That's a 100-200x reduction.

Background: Programmatic Tool Calling (PTC)

This skill replicates the architecture of Anthropic's Programmatic Tool Calling (PTC) for web search. PTC lets the model write code that orchestrates tool calls inside a sandbox — intermediate results stay in the sandbox, and only the final print() output reaches the model's context window.

This skill applies the same principle using local Python execution. The Python process is the sandbox. Variables in memory hold the raw data. Only what you print() crosses into your context window. You write the filtering logic — you decide what matters for each query.

Before running any command

If tvly is not found on PATH, install it first:

bash
curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login

Core Rule

NEVER run tvly as a bare command. Always process output through Python so you control what enters your context.

bash
# WRONG — raw results flood your context
tvly search "quantum computing 2025" --json
 
# RIGHT — only your print() output enters context
tvly search "quantum computing 2025" --json 2>/dev/null | python3 -c "
import json, sys
data = json.load(sys.stdin)
for r in data['results']:
    print(f'[{r[\"score\"]:.2f}] {r[\"title\"]}')
    print(f'  {r[\"url\"]}')
"

JSON Schemas

You need these to write correct filtering code.

tvly search --json
json
{
  "query": "string",
  "answer": "string | null",
  "results": [
    {
      "url": "string",
      "title": "string",
      "content": "string (snippet, ~500-1500 chars)",
      "score": 0.0-1.0,
      "raw_content": "string | null (full page, only with --include-raw-content)"
    }
  ],
  "response_time": 0.0
}
tvly extract --json
json
{
  "results": [
    {
      "url": "string",
      "title": "string",
      "raw_content": "string (full page markdown)",
      "images": []
    }
  ],
  "failed_results": [],
  "response_time": 0.0
}

How to search

You have two building blocks and two ways to run them. Compose these however the query demands — there are no fixed patterns. You decide the approach based on what you need.

Building blocks

`tvly search` — returns titles, URLs, snippets, scores. Optionally includes full page content with --include-raw-content markdown.

`tvly extract` — fetches full page content for specific URLs. Use when you found a URL from search and need more detail.

Execution modes

Pipe mode — for simple filters (3-5 lines). Pipe tvly output into python3 -c:

bash
tvly search "query" --json 2>/dev/null | python3 -c "
import json, sys
data = json.load(sys.stdin)
# your filtering code here
"

Heredoc mode — for anything more complex. Single Bash call, clean multi-line Python, no escaping, no temp files:

bash
python3 << 'PYEOF'
import json, subprocess
raw = subprocess.check_output(
    ['tvly', 'search', 'query', '--json'],
    stderr=subprocess.DEVNULL
)
data = json.loads(raw)
for r in data['results']:
    print(f"[{r['score']:.2f}] {r['title']}")
    print(f"  {r['url']}")
PYEOF

Single-quoted heredocs (<< 'PYEOF') don't interpret anything — no escaping needed. This is the default for most tasks.

Script mode — only when you will reuse the same script across multiple turns. Do NOT write one-shot scripts to /tmp/. If you run it once, use a heredoc.

Important: save DATA to `/tmp/`, not CODE. Writing /tmp/tavily_results.json (data for later turns) = good. Writing /tmp/my_filter.py (one-shot code) = wasteful — use a heredoc instead.

Multi-turn iteration

For complex queries, you often need to explore before you extract — just like PTC, where the model searches, sees titles, decides which results to drill into, then extracts.

The key: save raw results to a file, then process them in separate steps. The file is your persistent state between turns.

Turn 1: Search and explore

Search and print only titles + scores. Save raw results to disk for later turns:

bash
python3 << 'PYEOF'
import json, subprocess
 
raw = subprocess.check_output(
    ['tvly', 'search', 'solid-state battery commercialization 2025',
     '--include-raw-content', 'markdown', '--max-results', '8', '--json'],
    stderr=subprocess.DEVNULL
)
data = json.loads(raw)
 
# Save raw results — this stays on disk, never enters context
with open('/tmp/tavily_results.json', 'w') as f:
    json.dump(data, f)
 
# Print only what you need to decide next steps
print(f'{len(data["results"])} results saved to /tmp/tavily_results.json\n')
for i, r in enumerate(data['results']):
    print(f'[{i}] [{r["score"]:.2f}] {r["title"][:90]}')
    print(f'    {r["url"]}')
    print(f'    {r["content"][:150]}')
    print()
PYEOF

Context receives: ~800 tokens of titles + snippets. The 300K of raw page content is in /tmp/tavily_results.json, untouched.

Turn 2: Extract based on what you saw

Now you know what's in the results. Write targeted extraction — you decide which results to drill into and what to filter for:

bash
python3 << 'PYEOF'
import json
 
data = json.load(open('/tmp/tavily_results.json'))
 
# You chose these indices based on the titles you saw in turn 1
for i in [0, 2, 5]:
    r = data['results'][i]
    raw = r.get('raw_content', '') or ''
    if not raw:
        continue
 
    print(f'## {r["title"]}')
    print(f'URL: {r["url"]}\n')
 
    # You write the filtering logic based on the query
    # This example extracts paragraphs about specific companies
    for para in raw.split('\n\n'):
        para = para.strip()
        if len(para) > 80 and any(kw in para.lower() for kw in
                ['toyota', 'quantumscape', 'samsung', 'commercializ', 'production']):
            print(para)
            print()
 
    print('---\n')
PYEOF

Context receives: ~600 tokens of targeted content. You made the decision about what to keep.

Turn 3 (optional): Fetch more detail

If you need more from a specific source:

bash
python3 << 'PYEOF'
import json, subprocess
 
# Fetch a specific URL you identified
raw = subprocess.check_output(
    ['tvly', 'extract', 'https://example.com/article', '--json'],
    stderr=subprocess.DEVNULL
)
data = json.loads(raw)
page = data['results'][0]
content = page.get('raw_content', '')
 
# Save for potential further processing
with open('/tmp/page_detail.txt', 'w') as f:
    f.write(content)
 
# Print only the section you care about
for line in content.split('\n'):
    if any(kw in line.lower() for kw in ['timeline', '2025', '2026', 'mass production']):
        print(line.strip())
PYEOF
When to use multi-turn vs single-turn

Single turn (pipe mode or one script): when you know upfront what you're looking for. Specific factual queries, known keywords.

Multi-turn (save + explore + extract): when you need to see what's available before deciding what to extract. Open-ended research, complex topics, queries where you don't know the right keywords yet.

Examples

Simple factual lookup (single turn, pipe mode)
bash
tvly search "Python 3.13 release date" --max-results 5 --json 2>/dev/null | python3 -c "
import json, sys
data = json.load(sys.stdin)
for r in data['results'][:3]:
    print(f'{r[\"title\"]}')
    print(f'{r[\"content\"][:300]}')
    print()
"
Financial data extraction (single turn, heredoc)
bash
python3 << 'PYEOF'
import json, subprocess
 
raw = subprocess.check_output(
    ['tvly', 'search', 'NVIDIA Q4 2025 earnings revenue',
     '--include-raw-content', 'markdown', '--max-results', '5',
     '--json'],
    stderr=subprocess.DEVNULL
)
data = json.loads(raw)
 
for r in data['results']:
    raw_content = r.get('raw_content', '') or ''
    # For financial queries, look for lines with numbers
    financial_lines = [
        line.strip() for line in raw_content.split('\n')
        if any(kw in line.lower() for kw in
               ['revenue', 'eps', 'earnings', 'margin', 'guidance', 'billion'])
        and any(c.isdigit() for c in line)
        and len(line.strip()) > 30
    ]
    if financial_lines:
        print(f'## {r["title"]}')
        print(f'URL: {r["url"]}')
        for line in financial_lines[:15]:
            print(f'  {line}')
        print()
PYEOF
Multi-source research (multi-turn)

Turn 1 — broad search + triage:

bash
python3 << 'PYEOF'
import json, subprocess
 
# Search from multiple angles
queries = [
    ('broad', 'EU AI Act implementation timeline 2025'),
    ('specific', 'EU AI Act high-risk AI systems obligations'),
]
 
all_results = []
for label, query in queries:
    raw = subprocess.check_output(
        ['tvly', 'search', query, '--max-results', '8', '--json'],
        stderr=subprocess.DEVNULL
    )
    data = json.loads(raw)
    for r in data['results']:
        r['_query'] = label
    all_results.extend(data['results'])
 
# Deduplicate by URL
seen = set()
unique = []
for r in all_results:
    if r['url'] not in seen:
        seen.add(r['url'])
        unique.append(r)
 
# Save all results
with open('/tmp/eu_ai_results.json', 'w') as f:
    json.dump(unique, f)
 
# Print triage
unique.sort(key=lambda r: r['score'], reverse=True)
print(f'{len(unique)} unique results from {len(queries)} queries\n')
for i, r in enumerate(unique[:10]):
    print(f'[{i}] [{r["score"]:.2f}] ({r["_query"]}) {r["title"][:80]}')
    print(f'    {r["url"]}')
    print(f'    {r["content"][:120]}')
    print()
PYEOF

Turn 2 — you see the triage, pick the best sources, and extract:

bash
python3 << 'PYEOF'
import json, subprocess
 
results = json.load(open('/tmp/eu_ai_results.json'))
 
# Fetch full content for the top 3 (you chose these based on turn 1)
for r in [results[0], results[2], results[4]]:
    try:
        raw = subprocess.check_output(
            ['tvly', 'extract', r['url'], '--json'],
            stderr=subprocess.DEVNULL, timeout=30
        )
        page = json.loads(raw)
        if not page.get('results'):
            continue
        content = page['results'][0].get('raw_content', '')
 
        # Your filtering logic — tailored to this query
        print(f'## {r["title"]}')
        print(f'URL: {r["url"]}\n')
 
        for para in content.split('\n\n'):
            para = para.strip()
            if len(para) > 100 and any(kw in para.lower() for kw in
                    ['high-risk', 'prohibited', 'deadline', 'obligation',
                     'compliance', 'penalty', 'fine', 'article']):
                print(para)
                print()
 
        print('---\n')
    except Exception:
        continue
PYEOF
Following leads across turns

Sometimes turn 2 reveals new URLs or topics to chase. You can keep iterating:

bash
python3 << 'PYEOF'
import json, subprocess
 
# Read the page you saved earlier
with open('/tmp/page_detail.txt') as f:
    content = f.read()
 
# You noticed a reference to a specific regulation document
# Search for it specifically
raw = subprocess.check_output(
    ['tvly', 'search', 'EU AI Act Annex III high-risk list',
     '--include-domains', 'eur-lex.europa.eu',
     '--max-results', '3', '--json'],
    stderr=subprocess.DEVNULL
)
data = json.loads(raw)

This is the start of the file. Read the full SKILL.md on GitHub.

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