---

name: lov-media-discovery description: > 识别电影或剧集并从多个独立渠道收集、去重、归一化可下载候选；适用于“帮我找这部片的不同版本”、"search releases for this title"，输出可审计的候选清单。 license: MIT metadata: author: contributors version: "0.4.1" tags: - media-search - dht - candidate-normalization compatibility: "Agent runtime with web research; optional qBittorrent search API, Rats Search, and Torrent inputs." dependencies: - python

影视找片 · Media Discovery

Identify the requested work before collecting releases, then return normalized, deduplicated candidates with evidence kept separate from search-result claims. Keep Magnet, local Torrent, and remote .torrent inputs usable for later transport choice.

Triggers

Activate when

• 用户说“帮我找这部片的不同版本”“找导演剪辑版的下载候选”。
• 用户希望比较同一影片的多个 Magnet、Torrent 或发布版本。
• The user asks to search releases for a title or find several downloadable candidates.

Do not activate when

• 用户已经给出唯一链接且只要求立即下载。
• 用户只询问剧情、影评、演员或上映日期。
• The user asks only to verify an existing local media file.

Workflow (MANDATORY)

Step 0: Load the shared contract

• Resolve KIT_DIR and read $KIT_DIR/references/candidate-schema.md plus $KIT_DIR/references/user-config.md.
• Establish the output JSON path before searching.

Step 1: Disambiguate the work

Resolve canonical title, original title, year, media type, season/episode, and likely alternate spellings. Ask only if two different works remain plausible after research.

Step 2: Establish edition truth

Collect reliable evidence for known cuts and their runtimes: theatrical, original, director's cut, extended, uncut, complete, restoration, or regional variants. Record the source and confidence; do not treat a release filename as authoritative.

Step 3: Search independent paths

Use at least two available paths:

1. direct web research or catalog/index queries;
2. a local DHT index such as Rats Search;
3. user-supplied Magnet, Torrent, hash, or URL inputs;
4. optional scripts/qbittorrent_search.py across enabled, reviewed search plugins.

Run independent searches in parallel when the environment supports it. Use title, original title, year, edition terms, resolution, and subtitle markers as separate queries; avoid one over-constrained query that hides viable candidates.

Step 4: Normalize and deduplicate

• Emit UTF-8 JSON matching $KIT_DIR/references/candidate-schema.md.
• Deduplicate Magnet entries by normalized info hash, then by release identity. A Torrent URL may have a null hash until metadata is resolved; preserve the input.
• Preserve source, source_url, observed time, and edition evidence.
• Preserve advertised seeders separately from live observation fields; discovery data never counts as sustained download health.
• Infer filename features conservatively; mark inferred values with metadata_confidence: filename.
• Retain at least three healthy candidates when available so acquisition can race independent swarms.

Step 5: Validate handoff

Check that each candidate has id, name, uri, source, size when known, and current health fields when available. Report discovery gaps instead of inventing metadata.

Dependencies

No mandatory external search service. qBittorrent search plugins and Rats Search are optional adapters; direct web research remains available when configured adapters are absent.

Runtime context (shared)

运行前读取本 Skill 包的 skill.yaml，由宿主提供 skill-runtime/v1 上下文。字段解析顺序为：当前请求、项目上下文、个人 Preferences、品牌 Profile、通用默认值。

• 只使用 Manifest 声明的字段；Profile 保存公开品牌事实，Preferences 保存个人工作偏好。
• required: true 字段缺失时，按 Manifest 的问题配置向用户提出一个聚焦问题；用户明确同意后再保存回答。
• 报错提供可复制的 context_id、字段路径与来源，诊断内容避开秘密、完整私人路径和原始配置。