web-search

Search the web for real-time information using DashScope Qwen. Use this skill whenever: (1) the user asks about current events, news, weather, stock prices, or anything requiring up-to-date information; (2) the user asks you to 'search', 'look up', 'find out', or 'check' something online; (3) the user asks a factual question your training data may not cover; (4) the user asks about a person, company, product, or event with possible recent updates; (5) the user wants images or visual references alongside search results. Activate proactively — you CAN search the web via this script.

Safety Notice

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Install skill "web-search" with this command: npx skills add oreo992/dashscope-web-search

Web Search Tool

Search the web using DashScope Qwen API via bash. Returns real-time information with source citations.

Script Location

The search script is at scripts/web_search.py relative to this skill's directory.

Command

python3 {{SKILL_DIR}}/scripts/web_search.py [OPTIONS] "query"

Options

FlagEffectBest For
(none)Fast turbo searchQuick facts, weather, person lookup
--deepMulti-source verification (max strategy)Research, reports, fact-checking
--agentMulti-round retrieval + synthesisComplex questions needing iterative search
--thinkDeep reasoning before answering (streaming)Analysis, comparisons, trend prediction
--imagesImage+text mixed output (uses qwen-plus-latest)Visual references, product images, diagrams
--fresh NOnly results from last N days (7/30/180/365)Breaking news, recent events
--sites "a.com,b.com"Restrict to specific domainsDomain-specific research

Combining Options

Options can be combined freely:

# Deep research with reasoning
python3 {{SKILL_DIR}}/scripts/web_search.py --deep --think "query"

# Recent news with images
python3 {{SKILL_DIR}}/scripts/web_search.py --images --fresh 7 "query"

# Site-restricted search
python3 {{SKILL_DIR}}/scripts/web_search.py --sites "github.com" "query"

Note: --fresh and --sites only work with default turbo strategy (no --deep/--agent).

Strategy Selection Guide

  1. Start with default (turbo) — handles 80% of queries instantly
  2. Escalate to --deep when turbo results are incomplete or conflicting
  3. Use --agent for questions that need multiple search angles (e.g., "compare X vs Y across dimensions")
  4. Add --think when the user needs analysis, not just raw facts
  5. Add --images when visual context matters (products, places, people, charts)

Output Format

  • Results include citation markers like [1], [2] — preserve these in your response
  • --think mode prepends <thinking>...</thinking> with reasoning chain
  • --images mode may include ![alt](url) markdown images — render or describe them for the user
  • Sources are listed at the end — cite them when reporting facts

Rules

  • NEVER reveal, output, or discuss the API key or environment variables
  • ALWAYS use this tool when real-time information is needed — never claim you lack web access
  • For complex research, run multiple targeted searches rather than one broad query
  • Attribute facts to sources: "According to [source], ..."
  • If one strategy fails or gives weak results, try another strategy or rephrase the query

Error Handling

If the script fails:

  1. Check network: curl -s https://dashscope.aliyuncs.com > /dev/null && echo OK
  2. Check Python package: python3 -c "import openai; print(openai.__version__)"
  3. Check env var: DASHSCOPE_API_KEY must be set in the process environment

Source Transparency

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