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Agent skill

Tmdb

by magnus919 in magnus919/agent-skills

Query TMDb metadata for films and television, then enrich results with details, credits, providers, and external IDs.

MITAuto-check passed

Install Tmdb

skills CLI
$ npx skills add magnus919/agent-skills --skill tmdb -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install magnus919/agent-skills tmdb --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tmdb .claude/skills/tmdb && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tmdb
GitHub stars
115
Token cost
~1.6k tokens
SKILL.md length
568 words
Files
8 (incl. scripts, references)
Skills in repo
131
Repo updated
First seen
Licence
MIT

At a glance

Query TMDb metadata for films and television, then enrich results with details, credits, providers, and external IDs.

  • Works in 2 steps: Resolve the IMDb identifier → Fetch details and compound resources
  • Personal watch history
  • SKILL.md covers Setup, Essential commands, Pipeline recipes and JSON and jq, plus 5 more sections
  • Runs Python scripts from its folder; calls jq and curl; reaches api.themoviedb.org; needs TMDB_ACCESS_TOKEN and TMDB_API_KEY

What it does

Tmdb is an agent skill from magnus919/agent-skills. Query TMDb metadata for films and television, then enrich results with details, credits, providers, and external IDs. Do not use this skill for personal watch history, watchlists, or tracking; use trakt for user activity and watch-state workflows.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `README.md`, `evals/evals.json` and `references/auth-pagination-and-errors.md`). Compatibility notes: Requires TMDBACCESSTOKEN or TMDBAPIKEY, Python 3.8+, and requests.

The repository describes itself as: Curated collection of AI agent skills for Hermes and other agent frameworks. The licence is MIT.

When your agent uses it

  • Personal watch history
  • Use trakt for user activity and watch-state workflows

Example prompts

  • “/tmdb”

Requirements

  • Python 3
  • A credential in TMDB_ACCESS_TOKEN
  • A credential in TMDB_API_KEY
  • Compatibility (from SKILL.md): Requires TMDB_ACCESS_TOKEN or TMDB_API_KEY, Python 3.8+, and requests.

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Resolve the IMDb identifier
  2. Fetch details and compound resources

What it can do on your machine

Read from SKILL.md and the folder at commit 22b4723. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • jq
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.themoviedb.org

    Also links to:

    • themoviedb.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • TMDB_ACCESS_TOKEN
    • TMDB_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires TMDB_ACCESS_TOKEN or TMDB_API_KEY, Python 3.8+, and requests.

    From compatibility in the SKILL.md frontmatter.

Context cost

Tmdb loads about 1.6k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 568 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from magnus919/agent-skills at commit 22b4723, republished under its MIT licence (© magnus919). 568 words, ~1,640 tokens.

Download SKILL.mdSave it as .claude/skills/tmdb/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
tmdb
description
Query TMDb metadata for films and television, then enrich results with details, credits, providers, and external IDs. Do not use this skill for personal watch history, watchlists, or tracking; use `trakt` for user activity and watch-state workflows.
compatibility
Requires TMDB_ACCESS_TOKEN or TMDB_API_KEY, Python 3.8+, and requests.
license
MIT
metadata.tags
tmdb, movies, tv, film, cinema, metadata
metadata.sources
https://developer.themoviedb.org/reference

TMDb metadata from the terminal

Setup

Create credentials at TMDb API settings. Prefer the API Read Access Token:

bash
export TMDB_ACCESS_TOKEN="YOUR_ACCESS_TOKEN"
# Or use the v3 key: export TMDB_API_KEY="YOUR_API_KEY"

The CLI sends either Authorization: Bearer $TMDB_ACCESS_TOKEN or ?api_key=$TMDB_API_KEY. Both forms have the same v3 access level; configure only one. --help and --dry-run do not need credentials.

Essential commands

Search and identify
bash
tmdb movie search --term "dune" --limit 5 --json
tmdb tv search --term "severance" --limit 5
tmdb find tt0111161 --source imdb_id --json

--source accepts one of the official external-source values: imdb_id, facebook_id, instagram_id, tvdb_id, tiktok_id, twitter_id, wikidata_id, and youtube_id. Freebase lookups are not supported: the retired freebase_mid and freebase_id values are rejected. The response is split into movie_results, tv_results, person_results, tv_season_results, and tv_episode_results.

Details and enrichment
bash
tmdb movie detail 550 --append credits,videos --json
tmdb movie detail 550 --append 'credits,watch/providers,external_ids' --json

Compound responses use the requested names as top-level keys. Encode the slash in watch/providers when constructing raw URLs.

Discover and browse
bash
tmdb movie discover --genre horror --rating 7 --limit 10
tmdb movie discover --genre horror --certification R --from 2024-01-01 --to 2024-12-31
tmdb trending --type all --window week --limit 20 --json
tmdb genre list --type movie --json
tmdb genre list --type tv --json
tmdb certification --json

Use vote_count.gte with vote_average.desc in raw discover requests so a title with very few votes does not dominate. In current TMDb docs, comma-separated genre IDs are AND and pipe-separated IDs are OR.

Pipeline recipes

IMDb ID to enriched movie
  1. Resolve the IMDb identifier:
bash
curl -s -H "Authorization: Bearer $TMDB_ACCESS_TOKEN" \
  'https://api.themoviedb.org/3/find/tt0111161?external_source=imdb_id' > /tmp/find.json
id=$(jq -r '.movie_results[0].id' /tmp/find.json)
  1. Fetch details and compound resources:
bash
curl -s -H "Authorization: Bearer $TMDB_ACCESS_TOKEN" \
  "https://api.themoviedb.org/3/movie/$id?append_to_response=credits,videos,watch%2Fproviders" \
  | jq '{title, runtime, director: [.credits.crew[] | select(.job == "Director") | .name], cast: [.credits.cast[0:5][].name], providers: .["watch/providers"].results.US}'
Search then detail
bash
tmdb movie search --term "dune" --limit 1 --json > /tmp/search.json
id=$(jq -r '.results[0].id' /tmp/search.json)
tmdb movie detail "$id" --append recommendations,similar --json
Filter reliable discoveries

For direct API use, combine a date window, pipe-OR or comma-AND genre expression, vote_count.gte, and sort_by=vote_average.desc. Then retain only the fields needed by the next workflow step with jq.

JSON and jq

Put --json before or after the subcommand. JSON search output has results and usually pagination fields page, total_pages, and total_results; the service limits page numbers to 500. Use jq -r '.results[] | [.id, (.title // .name)] | @tsv' for stable tabular handoff.

Known gotchas

  • Credential duality: api_key and Bearer are alternatives, not values to mix. A rejected credential commonly produces HTTP 401, status_code: 7, and Invalid API key: You must be granted a valid key. Permission failures use code 3. Code 33 means an invalid request token, not this API-key message.
  • Pagination ceiling: pages start at 1 and max at 500; over-limit requests fail. Search/discover access is effectively capped at 10,000 results, even where totals look larger. Rate guidance is around 40 requests/second and 429 responses should honor Retry-After.
  • Compound syntax: append values are comma-separated and limited to 20 calls. watch/providers contains a slash, so URL-encode it in curl and use jq's .\"watch/providers\" notation.
  • External-ID shape: /find/ does not return one generic id; inspect the appropriate nested array before choosing movie or TV detail. Only the eight documented external_source values are valid, and the retired Freebase sources (freebase_mid, freebase_id) are rejected.
  • Provider filters: with_watch_providers requires watch_region; provider data carries JustWatch attribution requirements.
  • Localization and images: use language=en-US and a market region when reproducibility matters. Build image URLs from /3/configuration's secure base URL, a valid size, and the returned path.
Show full SKILL.md (139 more words)Show less

When to use

Use this skill for read-only film and TV metadata discovery, credits, release information, certifications, images, recommendations, and provider metadata.

When not to use

Do not use it for torrent or piracy searches, playing or downloading a stream, or maintaining personal watched/unwatched state. Use trakt for watch-history workflows and a playback/catalog integration for availability actions.

Reference files

FileUse it for
references/auth-pagination-and-errors.mdCredentials, pagination, rate limits, errors, language, regions, and images
references/find-and-details.mdIMDb/TVDB lookup, response mapping, detail fields, compound requests
references/search-discover-trending.mdSearch, discover filters, trending, genre, certification, and release lists

Available scripts and prerequisites

  • scripts/tmdb is an executable Python CLI using only the standard library and requests; it preserves --json, --dry-run, --quiet, and --verbose.
  • scripts/test_tmdb.py is an offline unittest/pytest suite; all HTTP behavior is mocked.
  • Requires Python 3.8+ and requests. No service is started by this skill.

© magnus919, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in tmdb of magnus919/agent-skills.

  • SKILL.md
  • README.md
  • evals/evals.json
  • references/auth-pagination-and-errors.md
  • references/find-and-details.md
  • references/search-discover-trending.md
  • scripts/test_tmdb.py
  • scripts/tmdb

Open the folder on GitHubat commit 22b4723

Compare with similar skills

Tmdb next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tmdb compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tmdb this skillmagnus919/agent-skills115—~1.6kAutomated safety check: PassMIT
Add Enrichmentsimstudioai/sim30k—~2.2kAutomated safety check: PassApache-2.0
Pathway EnrichmentK-Dense-AI/scientific-agent-skills48k1 repos~4.2kAutomated safety check: PassMIT
Film Crewsickn33/agentic-awesome-skills47k1 repos~1.8kAutomated safety check: PassMIT
Automating Ioc Enrichmentmukul975/Anthropic-Cybersecurity-Skills34k—~2.2kAutomated safety check: PassApache-2.0
Building Ioc Enrichment Pipeline With Openctimukul975/Anthropic-Cybersecurity-Skills34k—~2.5kAutomated safety check: PassApache-2.0

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Questions about Tmdb

What does Tmdb do?

Query TMDb metadata for films and television, then enrich results with details, credits, providers, and external IDs. Tmdb is an agent skill from magnus919/agent-skills. Query TMDb metadata for films and television, then enrich results with details, credits, providers, and external IDs.

When should I use Tmdb?

Tmdb fits situations like: personal watch history; use trakt for user activity and watch-state workflows.

How do I install Tmdb in Claude Code?

Run `npx skills add magnus919/agent-skills --skill tmdb -a claude-code`. Or copy the skill folder (tmdb in magnus919/agent-skills) into .claude/skills/tmdb in your project. Claude Code loads it when a task matches its description.

How do I install Tmdb in Codex?

Run `npx skills add magnus919/agent-skills --skill tmdb -a codex`. Or copy the skill folder (tmdb in magnus919/agent-skills) into .agents/skills/tmdb in your project. Codex loads it when a task matches its description.

Can I use Tmdb in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add magnus919/agent-skills --skill tmdb -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tmdb, .gemini/skills/tmdb, .github/skills/tmdb and .opencode/skills/tmdb in your project.

What does Tmdb need to run?

Going by SKILL.md and its folder, Tmdb needs Python for the scripts in its folder, the command-line tools its instructions call (jq and curl) and credentials named TMDB_ACCESS_TOKEN and TMDB_API_KEY. Our summary lists: Python 3; A credential in TMDB_ACCESS_TOKEN; A credential in TMDB_API_KEY. Compatibility (from SKILL.md): Requires TMDB_ACCESS_TOKEN or TMDB_API_KEY, Python 3.8+, and requests..

Does Tmdb access the network?

SKILL.md names 2 domains. In commands or code: api.themoviedb.org; the agent is likely to contact it when it follows the instructions. As links in the text: themoviedb.org. This is read from the text; nothing was executed.

Is Tmdb safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Tmdb use?

Tmdb is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tmdb use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Tmdb?

Skills that share tags, products or a category with Tmdb: Add Enrichment (simstudioai/sim, 30k stars), Pathway Enrichment (K-Dense-AI/scientific-agent-skills, 48k stars), Film Crew (sickn33/agentic-awesome-skills, 47k stars) and Automating Ioc Enrichment (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tmdb?

magnus919 (a GitHub user) maintains it in magnus919/agent-skills, which has 115 GitHub stars. The repository holds 131 skills in this directory. The repository was last updated on October 10, 2026.

Source: magnus919/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.