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 TheStreamCode/chutes-media-mcp --skill chutes-mediagit clone --depth 1 https://github.com/TheStreamCode/chutes-media-mcpWrote 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/thestreamcode/chutes-media-mcp/chutes-media)<a href="https://agentmods.dev/skills/thestreamcode/chutes-media-mcp/chutes-media"><img src="https://agentmods.dev/badge/skills/thestreamcode/chutes-media-mcp/chutes-media/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/thestreamcode/chutes-media-mcp/chutes-media"><img src="https://agentmods.dev/badge/skills/thestreamcode/chutes-media-mcp/chutes-media.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00098 | $0.01310 |
| Opus 5.5 | $0.00039 | $0.00524 |
| Sonnet 5.5 | $0.00020 | $0.00262 |
| Haiku 4.5 | $0.00010 | $0.00131 |
Grade A, and why
chutes-media scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chutes Media
Generate image, video, music, and speech through Chutes and save the asset directly into the
project being worked on, then reference it from the code. Use the MCP tools when they are available
(list_media_models, describe_media_model, generate_media); otherwise use the chutes-media
CLI, which exposes the same three operations and prints a JSON result to stdout.
Golden rule
Model payloads are never uniform across Chutes — FLUX, Qwen-Image, Wan, LTX, ACE-Step and TTS
models each expose different fields. Always call describe_media_model before generate_media
for any model not already described in this session, and compose the payload from the returned
example. Never guess field names.
Core workflow
Follow these steps for every generation request:
-
Discover — Call
list_media_modelswith the targetkind(image|video|music|speech) and/or aquery. If the user named a model, skip to step 2. Models are not hardcoded and the catalog changes, so discover rather than assume. -
Describe — Call
describe_media_model { model }. Read the returned cords. Each cord lists itsrequiredfields,fields(types/defaults/ranges), and a ready-to-fillexample; the result also reports a top-levelsupportsEditing. Pick the cord for the operation (generation cord by default). -
Compose — Start from the cord's
example, set the realprompt/inputs, and keep the payload flat. The server transparently unwraps function-signature schemas (e.g. aninput_argswrapper) and validates the payload before spending a GPU call, returning a precise error if a field is wrong. -
Generate — Call
generate_media { model, kind, params, cord?, output_dir?, filename?, overwrite? }. The server warms the model, invokes it (blocking, with progress updates), retries transient cold-start503s automatically, downloads the asset, and saves it under./assets/chutes/<kind>/by default. Outputs must remain inside the workspace; setoverwrite: trueonly when replacement is intentional. It returns{ path, kind, model, cord, bytes, contentType, cost?, durationMs, schemaHash?, provenancePath? }—schemaHashpins the cord schema the payload was validated against, andprovenancePathis the sidecar written next to the asset (omitted when provenance is disabled).
What ships with it
1 file 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.
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.
- 1mo ago Changed b1f7d7e098cb
- 1mo ago First seen · 98 lines · 98 tokens per session scan A 20139cfc882a
chutes-media is a skill published in the GitHub repository TheStreamCode/chutes-media-mcp (1 stars, last pushed today), licensed MIT. It adds 98 tokens to every session and 1,310 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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