• Politically sensitive: Involves attacks on political figures, political rumors, inciting political remarks, political content violating laws and regulations
• Pornographic and vulgar: Pornographic descriptions, sexual innuendos, vulgar language, inappropriate content involving minors
• Insults and defamation: Personal attacks, insulting language, hate speech, discriminatory remarks
• Advertising and promotion: Spam ads, phishing links, malicious promotion content
• Illegal and non-compliant: Content involving drugs, violent crimes, terrorism, etc.

1. If any of the above categories is detected, terminate the process immediately
2. Return a prompt to the user: "Sorry, I cannot process your request. Please modify your input and try again." Do not disclose the specific review reason or category to the user
3. When the review passes, do not inform the user of the review result, directly proceed to subsequent steps silently

ncm-preference.json

updatedAt

1. Pull liked playlist tracks (maximum 200 songs, index=0 is the latest), each track data includes:

   • extMap.addTime

     : Liked song timestamp (milliseconds) → Convert to local time for time period/cycle analysis

   • songTag[]

     : Song content tags (e.g., "R&B", "Classical", "Ambient") → Content preference statistics

   • artists[]

     ,

     album

     : Artists/albums → Style judgment

   • name

     ,

     duration

     : Song name/duration (milliseconds) → Auxiliary mood/scene judgment
2. Content Dimension Analysis (full 200 songs):
   • Count high-frequency

     songTag

     tags (Top 8) as

     contentTags

   • Extract: Genre proportion, emotion direction, language preference, era style, representative artists
3. Time Dimension Analysis (full 200 songs, based on

   extMap.addTime

   ):
   • Convert timestamps to local time, count distribution by time segments (0-6 late night, 6-9 morning, 9-12 morning, 12-14 noon, 14-18 afternoon, 18-22 evening/night, 22-24 late night)
   • Count by week (weekdays vs weekends, refined to specific days)
   • Identify high-frequency liked song time periods (peakHours) and cycle rules (e.g., "Dense liked songs on Friday late nights")
   • Compare

     songTag

     distribution in high-frequency time periods, extract time period-content correlation (e.g., "Late night liked songs are mainly classical/ambient styles")
4. Recent Trend Analysis (first 20 songs, confirmed as latest by

   addTime

   sorting):
   • Focus on whether recent trends deviate from the overall profile (e.g., suddenly liked a large number of new genres recently)
   • Generate

     recentTrend

     description
5. Synthesize the above, generate up to 6 search keywords, write to

   ncm-preference.json

   (including

   temporalPattern

   and

   contentTags

   )

• What to search: Playlists / albums / singles, or a mix of the three
• Which keywords to use: Combine user requirements + preference profile + scene semantics, freely innovate
  • Can use Chinese scene words, English genre tags, emotion words, era words, artist style words
  • Prioritize album search for specific requirements (e.g., "movie soundtracks"); prioritize playlist search for mood/scene descriptions
• How many searches: Parallel searches with multiple keywords (2~4 times) for complex needs, single search for simple needs
• Deduplication Sources:

  ncm-history.json

  (already recommended) +

  playlist collected

  (already favorited)

1. Execute search commands according to the strategy, aggregate results
2. Filter Logic (Model Judgment):
   • Exclude already favorited/recommended content
   • Evaluate the matching degree of each result combined with the preference profile
   • Comprehensive consideration: Name/tag semantic matching, popularity (playCount), content scale (trackCount/number of songs)
3. Select 4~6 best results (mix of playlists, albums, singles allowed)

1. Preference Correlation Layer: Explain why this is recommended by combining the user's specific liked artists/genres/recent trends
2. Content Feature Layer: Describe the core highlights of the playlist/album/single itself

1. As long as "playlists" or "singles" are recommended, clickable links must be output (at least 1).
   • Playlist → Must output

     https://music.163.com/#/playlist?id=<plaintext ID>

   • Single → Must output

     https://music.163.com/#/song?id=<plaintext ID>

2. The ID in the link must use "plaintext originalId (numeric)",禁止使用加密 ID（32位 hex）拼链接。
3. Each recommendation must output "recommendation score + recommendation reason":
   • Score:

     ⭐ Score: <0-100>

     (Self-evaluation by the model, indicating matching degree with "user requirements + preference profile")
   • Reason: Must be two-layer reasons (40~80 words in total):
     1. Preference Correlation Layer: Cite preference evidence (e.g., user's high-frequency liked genres/artists/content tags/recent trends/frequently listened time periods)
     2. Content Feature Layer: Describe the atmosphere and applicable scenarios of the playlist/album/single itself
   • Do not only use empty adjectives (e.g., "Very suitable for you"), must be based on specific evidence and features.
4. If the result contains a cover URL (e.g.,

   coverImgUrl

   ):
   • Playlist cover recommended to output (optional): A line of

     🖼️ <url>

     or Markdown hyperlink is sufficient.
   • Single covers are also recommended to be output if available (also use

     🖼️

     ).
   • In Feishu channels: For more intuitive display, optionally send the cover "as an image message" (see below "Cover Image Push (Feishu Optional)"); meanwhile, retain the

     🖼️

     cover link in the text as a fallback.
5. If the link cannot be obtained due to permissions/visibility (very rare):
   • Must clearly explain "why the link cannot be obtained" and provide actionable alternatives (e.g., available search keywords/result lists).

It is recommended to use "short list + links" for output to ensure readability and direct access in IM.

🎵 Music Recommendation · [YYYY-MM-DD HH:MM]

[Trigger source description, e.g.: Weekend night push]

① 🎶 [Playlist/Album/Single Name]
   ⭐ Score: [0-100]
   📝 Reason: [Two-layer recommendation reasons (40~80 words)]
   🖼️ [Cover link (optional; output if coverImgUrl exists)]
   🔗 [Resource link (Must output; at least 1)]

② 🎶 [Name]
   ...

Total N items

Note: The ID in the link must use plaintext originalId!!

https://music.163.com/#/song?id=<plaintext ID>
https://music.163.com/#/playlist?id=<plaintext ID>
https://music.163.com/#/album?id=<plaintext ID>
https://music.163.com/#/artist?id=<plaintext ID>

coverImgUrl

• In Feishu channels, optionally:
  1. Send an image message (the image is the cover corresponding to

     coverImgUrl

     ).
     • If the system/tool does not support sending images directly via URL, first download to a local temporary file then send.
  2. Optionally retain

     🖼️ <coverImgUrl>

     in the text message as a fallback.
• Recommended sending order:
  • Send the cover image first (optional) → Then send the recommendation body (with links).
• Failure Fallback:
  • If image sending fails, it does not affect the recommendation body; at least ensure the song/playlist link is output.

1. Use ncm-cli: Create playlist, get playlist ID → Add songs to the playlist
2. Verify trackCount to confirm successful addition
3. Attach the playlist link in the recommendation output

1. Parse natural language → Update

   ncm-schedule.json

   (see format)
2. [Key] Synchronously register to system cron - Must perform the following steps:
   • Check current cron tasks:

     crontab -l

   • Compare the rules in

     ncm-schedule.json

     , confirm each rule is registered in cron
   • For newly added rules, use the following command to register (example):
     bash

     (crontab -l 2>/dev/null; echo "minute hour * * * /usr/local/bin/node /path/to/main.js scene") | crontab -

   • Verify successful registration: Execute

     crontab -l

     again to confirm the task exists
3. Check if you have scheduling capabilities
   • If you have scheduled task capabilities, must write the user's request into your scheduling system! Then perform the following operations:
     • Output confirmation: "Set successfully: [Rule description], next trigger: [Time]"
     • Check for missing rules: Rules that exist in

       ncm-schedule.json

       but not in your scheduling system should be updated to your scheduling system
   • If you do not support scheduling: Directly output "Scheduling is not supported temporarily"

• The latest liked song is index=0, focus on the first 20 songs for recent trends
• After each recommendation, write the playlist ID to

  ncm-history.json

  to prevent duplicate recommendations
• Active search results are not included in history (do not affect deduplication for automatic pushes)
• Proactively consider whether to create a playlist during recommendations—especially for "specific theme/movie/artist" needs
• [Schedule Key] After updating

  ncm-schedule.json

  , must synchronously register to system cron (crontab), otherwise the scheduled task will not take effect
  • Check method:

    crontab -l

  • Registration method:

    (crontab -l 2>/dev/null; echo "minute hour * * * /usr/local/bin/node /path/to/main.js scene") | crontab -

  • Verification method: Execute

    crontab -l

    again to confirm the new task exists