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 stevepridemore/graph-memory --skill ingest-audiogit clone --depth 1 https://github.com/stevepridemore/graph-memoryWrote 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/stevepridemore/graph-memory/ingest-audio)<a href="https://agentmods.dev/skills/stevepridemore/graph-memory/ingest-audio"><img src="https://agentmods.dev/badge/skills/stevepridemore/graph-memory/ingest-audio/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/stevepridemore/graph-memory/ingest-audio"><img src="https://agentmods.dev/badge/skills/stevepridemore/graph-memory/ingest-audio.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 | $0.00056 | $0.01237 |
| Opus 5.5 | $0.00022 | $0.00495 |
| Sonnet 5.5 | $0.00011 | $0.00247 |
| Haiku 4.5 | $0.00006 | $0.00124 |
Grade A, and why
ingest-audio 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The user wants to transcribe a local audio or video file and ingest it into the graph memory system.
Arguments: $ARGUMENTS
Step 1: Parse arguments
- First positional argument: local file path (required)
--model: Whisper model size. Default:base. Options:tiny,base,small,medium,largetiny: fastest, least accurate (~39MB)base: good balance, recommended default (~74MB)small: noticeably better accuracy (~244MB)medium: high accuracy, slow on CPU (~769MB)large: best accuracy, very slow on CPU (~1.5GB)
--topic: topic hints for metadata (comma-separated)--author: speaker or creator name--now: process immediately inline after transcription instead of queuing
If no file path is provided, ask the user for one.
Step 2: Verify Whisper is installed
Run: whisper --help
If this fails, report:
Whisper is not installed. Install it with:
pip install openai-whisper
Note: This downloads model weights (~74MB for 'base') on first run.
ffmpeg is also required:
- Windows: winget install ffmpeg
- macOS: brew install ffmpeg
- Linux: apt install ffmpeg (or your distro's equivalent)
Then stop.
Step 3: Verify the file exists and is a supported format
Supported: .mp3, .wav, .m4a, .mp4, .ogg, .flac, .webm, .mkv, .avi, .mov
If the file doesn't exist or the format isn't supported, report the issue and stop.
Step 4: Transcribe with Whisper
Output directory: ~/graph-memory/.tmp/graph-audio-ingest/
Run:
whisper "<file-path>" --model <model> --output_format txt --output_dir "~/graph-memory/.tmp/graph-audio-ingest/"
Whisper outputs <original-filename>.txt in the output directory.
Note: This may take several minutes for longer files on CPU. Inform the user that transcription is running.
If transcription fails, report the error and stop.
Step 5: Read and validate the transcript
Read the output .txt file. If it's empty or very short (under 20 words), warn the user:
- "Transcript appears empty or very short. The audio may be silent, too quiet, or in a different language."
- Suggest trying
--model smallor--model mediumfor better accuracy.
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 First seen · 128 lines · 56 tokens per session scan A 2e6867d3aafb
ingest-audio is a skill published in the GitHub repository stevepridemore/graph-memory (2 stars, last pushed 2d ago), licensed MIT. It adds 56 tokens to every session and 1,237 once invoked, about $0.0002 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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