name: search-image description: Search images from text queries and return the most relevant image result, candidate images, source pages, or ready-to-open search links. Use when the user asks to search for an image, find reference images, look up a character, person, brand, mascot, meme, wallpaper, avatar, or logo, wants several engines searched, wants the best match instead of the literal first result, or wants the best candidate sent as an attachment. Prefer multi-engine search with relevance ranking, then download and send the best match; if confidence is weak or downloading fails, return several candidate links and search URLs.

Search Image

Overview

Handle image-search requests triggered by phrases like search image X or find a picture of X. Default behavior for this skill: - Search multiple engines, not just one source - Prefer the most relevant result, not the literal first result - Download the best match locally, then send it as an attachment when possible - If confidence is weak, provide several candidates instead of pretending one result is perfect

Engines

Use as many available sources as practical, in this priority order: - Bing Images - Baidu Images - Sogou Images

Notes: - Bing is usually the most parseable server-side source - Baidu may return a security-verification page; treat it as a soft failure - Sogou can provide extra recall when Bing drifts or lacks meme-style results

Workflow

1. Extract the search query after the trigger phrase.
2. Parse lightweight parameters first. Read references/parameters.md.
3. Detect intent on the cleaned core query. Read references/intent-routing.md when tuning or debugging routing.
4. Normalize the cleaned core query, but keep the original wording too.
5. Detect subject subtype when relevant, especially under official: campus, emblem, mascot, poster.
6. Search across multiple engines.
7. Collect several candidate image results instead of only one.
8. Rank candidates by relevance with intent-aware weighting, source trust, and subtype-aware boosts/penalties.
9. Classify confidence. Read references/confidence.md when tuning thresholds.
10. If confidence is high, download and send the best match.
11. If confidence is medium, prefer 2-3 candidates.
12. Apply quality filtering before final send. Read references/quality-filtering.md when tuning thresholds.
13. If confidence is low or downloading fails, return top candidates plus search links.

Parameters

Support lightweight natural-language parameters inside the query. Examples: - search image taylor swift 3 images - search image acme logo official - search image funny cat meme - search image aurora wallpaper 4k landscape - search image anime girl avatar 2 images

Supported parameters: - Count: 1 to 5 images - Intent override: official, meme, avatar, wallpaper - Orientation preference: landscape, portrait - Quality preference: hd, 4k, high resolution

Parse these first, then search the cleaned core entity query.

Intent routing

Classify the request before ranking: - meme: meme / reaction image / funny image / emoji style requests - official: official / logo / emblem / mascot / poster / brand identity - portrait: people, characters, or general image requests - wallpaper: wallpaper / hd / high resolution / 4k - avatar: avatar / profile picture / icon

When official is selected, also infer a subtype when possible: - campus: campus / gate / building / landscape / map - emblem: emblem / logo / crest / mark / badge - mascot: mascot / character / brand character / official character / IP character - poster: poster / promotional art / campaign art

Intent affects ranking: - meme prefers meme and funny-image pages - official prefers official domains, institutional pages, and reference pages - portrait prefers representative images - wallpaper prefers larger image results - avatar prefers square or icon-like results

Relevance policy

Do not blindly send the first result. Use references/relevance.md and follow these rules. For official intent, also read references/official-sources.md and references/official-whitelist.md when tuning or debugging official-result ranking. Use references/entity-consistency.md to prevent official-looking but wrong-entity matches. Use references/entity-gating.md when the query contains multiple strong entities and partial matches must be demoted to fallback-only. - exact query match is best - token matches in title/page/image URL matter - semantically related domains help - obvious news drift, spammy pages, or generic stock images should rank lower - weak modifiers like image, photo, meme, avatar, and wallpaper should not outweigh the core entity - in official mode, prefer trusted domains over visually appealing aggregators - in emblem mode, prefer brand/identity signals and penalize maps, personal homepages, and generic banners - in mascot mode, prefer official character/IP wording and penalize generic news coverage when cleaner source pages exist

If confidence is weak, prefer sending 2-3 candidates or links rather than a low-quality single answer. Read references/confidence.md for the high / medium / low decision rule.

Output rules

• Be brief and lead with the result.
• If the image was sent successfully, say so in one short sentence.
• If the image cannot be sent directly, do not pretend the search succeeded.
• When falling back, provide clickable search links immediately.
• For meme or mascot queries, optimize for likely intent match, not formal source prestige.

Ready-made search URLs

Use these patterns:

• Bing Images: https://www.bing.com/images/search?q=<urlencoded_query>
• Baidu Images: https://image.baidu.com/search/index?tn=baiduimage&word=<urlencoded_query>
• Sogou Images: https://pic.sogou.com/pics?query=<urlencoded_query>

If needed, use scripts/build_image_search_urls.py to generate encoded URLs safely.

Scripts

Build search URLs

Run:

python3 scripts/build_image_search_urls.py "official mascot"

Parse the most relevant image automatically

Run:

PYTHONPATH=scripts python3 scripts/search_best_image.py "cat meme"

The script prints JSON containing: - parsed parameters - cleaned search_query - per-engine status - normalized query info - detected intent - confidence - best_image - top_candidates

Download the best image for direct sending

Run:

PYTHONPATH=scripts python3 scripts/fetch_best_image.py "cat meme"

The script: - searches Bing, Baidu, and Sogou - ranks candidates by relevance - computes confidence - downloads the best match to /home/mumu/clawd/tmp/search-image/ - prints JSON with path, image_url, engine, score, why, and confidence info

Download 2-3 candidates when confidence is not high

Run:

PYTHONPATH=scripts python3 scripts/fetch_candidate_images.py "cat meme" 3

Use this when confidence is medium or low. The script downloads multiple top candidates and returns their local file paths for sending.

Quality filtering

Use scripts/image_quality.py through the fetch scripts. Default checks now include: - reject obvious site assets / logos - reject too-small files - reject too-small dimensions - penalize thumbnail-style URLs - adjust scoring by intent

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Legacy first-result scripts

These remain available for debugging or quick comparison: - scripts/search_first_image.py - scripts/fetch_first_image.py

Prefer the best-image scripts in normal operation.

Send-image handoff

After fetch_best_image.py succeeds, send the downloaded file as an attachment.

Preferred handoff: - Use the local file path returned in path - Use the best-match result, not the literal first result - If confidence is low but still usable, mention it briefly or send multiple candidates instead - If attachment sending fails, fall back to sending candidate image URLs and search links

Suggested flow: 1. Run PYTHONPATH=scripts python3 scripts/fetch_best_image.py "<query>" 2. Respect parsed parameters first: count / intent override / orientation / high-res hints 3. If confidence is high and JSON returns ok: true, use the returned local path for image sending 4. If confidence is medium, run fetch_candidate_images.py and send the requested count or 2-3 candidates 5. If confidence is low, send search links plus candidate links or candidate images 6. If JSON returns ok: false, send the search links and top candidate URLs instead

Fallback template

Use this style when direct sending fails:

Result: I could not get a single high-confidence direct image, so here are the best candidates.
Bing Images: <url>
Baidu Images: <url>
Sogou Images: <url>
Candidate 1: <url>
Candidate 2: <url>
Candidate 3: <url>

這個圖片搜尋技能質量中上，優點是能同時搜尋多個引擎、支援按意圖篩選、能避免找錯圖片、還會根據搜尋質量自動判斷是否提供多張候選圖。不足是對某些特殊需求的理解可能不夠準確，搜尋結果質量波動時表現不穩定。日常簡單搜尋夠用，但複雜場景可能需要多試幾次。