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 tallesborges/zdx --skill ask-mediagit clone --depth 1 https://github.com/tallesborges/zdxWrote 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/tallesborges/zdx/ask-media)<a href="https://agentmods.dev/skills/tallesborges/zdx/ask-media"><img src="https://agentmods.dev/badge/skills/tallesborges/zdx/ask-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/tallesborges/zdx/ask-media"><img src="https://agentmods.dev/badge/skills/tallesborges/zdx/ask-media.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Excessive Agency · line 49 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00118 | $0.01014 |
| Opus 5 | $0.00059 | $0.00507 |
| Sonnet 5 | $0.00024 | $0.00203 |
| Haiku 4.5 | $0.00012 | $0.00101 |
Grade A, and why
ask-media scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ask-Media – File understanding via zdx ask-media
Send a local image, PDF, audio, or video file plus a question to a Gemini model and get a text answer back on stdout. One-shot: the file is re-sent on each call.
You write the question, so ask for exactly what you need — not a generic description. A targeted prompt ("read the stack trace and the failing file path") beats "describe this image" every time.
CLI reference
zdx ask-media <FILE> -p "<question>" [OPTIONS]
Options:
-p, --prompt <PROMPT> The question/instruction to run against the file (required)
-m, --model <MODEL> Gemini provider:model id (default: gemini:gemini-3.5-flash-lite)
--json Emit {file, model, answer} JSON instead of plain text
Output: prints the model's text answer to stdout (nothing else). Read it and use it directly in your reply — do not wrap it in a <media> tag (that tag is only for sending files back).
zdx ask-media "$ZDX_ARTIFACT_DIR/report.pdf" -p "Summarize the key findings in 3 bullets."
When to use
- The user gives you a specific local file path and asks about its contents.
- The active model cannot read that media type itself. Models without image input (for example
deepseek:deepseek-v4-flash) never receive attached images: the image is replaced by a note telling you to come here. The file path is in the surrounding text or tool output — callask-mediaon it with a question shaped by what the user actually asked. - Supported inputs: images (png/jpg/webp/gif/heic), PDF, audio (mp3/wav/ogg/m4a/aac/flac), and video (mp4/mov/mpeg/webm/flv/wmv/3gp).
When NOT to use
- The media is already visible to the active model — a vision-capable model receives attached images directly, so just answer. Only reach for
ask-mediawhen the content did not reach you. - Plain verbatim transcription of an audio file → prefer the
transcriptionskill (zdx transcribe), which is purpose-built for speech-to-text and supports diarization. Useask-mediawhen the user wants understanding/Q&A/summary over audio or video. - Non-Gemini models:
ask-mediais Gemini-only and errors clearly on any other provider. If no Gemini key is configured, say the file can't be inspected instead of guessing at its contents.
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.
- 9d ago First seen · 60 lines · 118 tokens per session scan A fa609d6635c3
ask-media is a skill published in the GitHub repository tallesborges/zdx (20 stars, last pushed 4d ago), licensed MIT. It adds 118 tokens to every session and 1,014 once invoked, about $0.0006 per session on Opus 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 2026-08-30.
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