Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add magnus919/agent-skills --skill jellyfingit clone --depth 1 https://github.com/magnus919/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/magnus919/agent-skills/jellyfin)<a href="https://agentmods.dev/skills/magnus919/agent-skills/jellyfin"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/jellyfin/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/magnus919/agent-skills/jellyfin"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/jellyfin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 10 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 50 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Data Exfiltration · line 16 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.02815 |
| Opus 5 | $0.00044 | $0.01407 |
| Sonnet 5 | $0.00017 | $0.00563 |
| Haiku 4.5 | $0.00009 | $0.00281 |
Grade A, and why
jellyfin scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| [references/worked-recipes.md](references/worked-recipes.md) | Multi-step curl/jq and CLI recipes: login → latest, libraries → browse, search → seasons → episodes | How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
jellyfin — Jellyfin Media Server from the Terminal
Query recently added movies and TV episodes, search and inspect media, walk series →
seasons → episodes, browse libraries, see next-up episodes, log in as a user, and check
server stats — all from your Jellyfin server's REST API. Every command is read-only
except login.
Setup
- Make sure your Jellyfin server is running and accessible (default
http://localhost:8096). - Pick an authentication route:
- API key — Dashboard → API Keys →
+. Administrator-level, no user identity: every user-scoped command then needs an explicit user id. - User token — run
scripts/jellyfin login --username NAME --promptonce; it prints the values to export.
- API key — Dashboard → API Keys →
- Set these environment variables:
export JELLYFIN_URL="http://your-server:8096" # include protocol and port
export JELLYFIN_API_KEY="your-api-key-here" # or JELLYFIN_TOKEN after `login`
export JELLYFIN_USER_ID="your-jellyfin-user-id" # required by recent, next-up, item, seasons, episodes
Run the bundled CLI as scripts/jellyfin. --help and --dry-run work without
credentials.
How authentication works
Jellyfin wants a MediaBrowser-scheme Authorization header on every call. The login
endpoint requires its Client=..., Device=..., DeviceId=..., Version=... quartet before
any token exists — the server rejects POST /Users/AuthenticateByName with
400 Error processing request. otherwise. Afterwards the access token (or API key) rides
the same header as Token="..."; the legacy X-Emby-Token header means the same thing
and is scheduled for removal from Jellyfin 12.0. The bundled CLI sends the modern form and
puts the token in exactly that one channel per request (never co-sends X-Emby-Token).
See references/auth-and-sessions.md.
Essential Commands
Authentication — get a session
scripts/jellyfin login --username alice --prompt # prints JELLYFIN_* exports
echo "pw" | scripts/jellyfin login --username alice --password-stdin
scripts/jellyfin login --username alice --dry-run --json # preview the pre-token header
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/evals.json 4.8 KB
- README.md 3.1 KB
- references/auth-and-sessions.md 11 KB
- references/endpoint-catalog.md 11 KB
- references/gotchas-field-guide.md 8.4 KB
- references/quick-connect.md 3.4 KB
- references/user-scoping-and-errors.md 5.4 KB
- references/worked-recipes.md 8.9 KB
- scripts/jellyfin 34 KB
- scripts/test_jellyfin_cli.py 37 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 227 lines · 87 tokens per session scan A 302cac2af935
jellyfin is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed today), licensed MIT. It adds 87 tokens to every session and 2,815 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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