duclm1x1
duclm1x1/Dive-Ai
相关职业SOC
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/duclm1x1/Dive-Ai --skill tmdb命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
正在显示 SKILL.md
基于 SOC 职业分类
| name | tmdb |
| description | Search movies/TV, get cast, ratings, streaming info, and personalized recommendations via TMDb API. |
| homepage | https://www.themoviedb.org/ |
| metadata | {"clawdis":{"emoji":"🎬","requires":{"bins":["uv"],"env":["TMDB_API_KEY"]},"primaryEnv":"TMDB_API_KEY"}} |
Comprehensive movie and TV information with streaming availability, recommendations, and personalization.
Set environment variable:
TMDB_API_KEY: Your TMDb API key (free at themoviedb.org)# Search movies
uv run {baseDir}/scripts/tmdb.py search "Inception"
# Search TV shows
uv run {baseDir}/scripts/tmdb.py search "Breaking Bad" --tv
# Search people (actors, directors)
uv run {baseDir}/scripts/tmdb.py person "Christopher Nolan"
# Full movie info
uv run {baseDir}/scripts/tmdb.py movie 27205
# With cast
uv run {baseDir}/scripts/tmdb.py movie 27205 --cast
# TV show details
uv run {baseDir}/scripts/tmdb.py tv 1396
# By name (searches first, then shows details)
uv run {baseDir}/scripts/tmdb.py info "The Dark Knight"
# Find streaming availability
uv run {baseDir}/scripts/tmdb.py where "Inception"
uv run {baseDir}/scripts/tmdb.py where 27205
# Specify region
uv run {baseDir}/scripts/tmdb.py where "Inception" --region GB
# Trending this week
uv run {baseDir}/scripts/tmdb.py trending
uv run {baseDir}/scripts/tmdb.py trending --tv
# Recommendations based on a movie
uv run {baseDir}/scripts/tmdb.py recommend "Inception"
# Advanced discover
uv run {baseDir}/scripts/tmdb.py discover --genre action --year 2024
uv run {baseDir}/scripts/tmdb.py discover --genre sci-fi --rating 7.5
# Get personalized suggestions (uses Plex history + preferences)
uv run {baseDir}/scripts/tmdb.py suggest <user_id>
# Set preferences
uv run {baseDir}/scripts/tmdb.py pref <user_id> --genres "sci-fi,thriller,drama"
uv run {baseDir}/scripts/tmdb.py pref <user_id> --directors "Christopher Nolan,Denis Villeneuve"
uv run {baseDir}/scripts/tmdb.py pref <user_id> --avoid "horror,romance"
# View preferences
uv run {baseDir}/scripts/tmdb.py pref <user_id> --show
# Add to watchlist
uv run {baseDir}/scripts/tmdb.py watchlist <user_id> add 27205
uv run {baseDir}/scripts/tmdb.py watchlist <user_id> add "Dune: Part Two"
# View watchlist
uv run {baseDir}/scripts/tmdb.py watchlist <user_id>
# Remove from watchlist
uv run {baseDir}/scripts/tmdb.py watchlist <user_id> rm 27205
If the Plex skill is available, suggest command pulls recent watch history to inform recommendations.
If ppl skill is available, preferences are stored as notes on the user's contact for persistence across sessions.
Common genres for --genre filter: