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 agentmods add commands/erhangundogan/videre/classifygit clone --depth 1 https://github.com/erhangundogan/videreWhat 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 | $0.00013 | $0.01598 |
| Opus 5 | $0.00006 | $0.00799 |
| Sonnet 5 | $0.00003 | $0.00320 |
| Haiku 4.5 | $0.00001 | $0.00160 |
Grade A, and why
classify 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 2d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tags each image as photo, screenshot, document, or meme, so you can
separate real photographs from the receipts, memes and screenshots that
accumulate in a camera roll.
videre classify # classify everything not done yet
videre classify --reprocess # redo everything, including already-tagged
videre classify --margin 0.05 # how confident it must be (default 0.05)
videre classify --silent # no per-image progress
videre classify --db ~/photos.db # use a specific database
videre classify --model <id> # classify a specific model's data
videre classify --type image # only images
videre classify --person Ada # only photos of a labelled person
Classify takes every filter, so a run can be narrowed by anything already known about a file:
videre classify --type image --after 2025-01-01 # this year's photos only
videre classify --path ~/Photos/Inbox # one folder
videre classify --location "Berlin, Germany" # photos taken near a place
videre classify --person "Alice" --reprocess # re-label one person's photos
videre classify --ext heic --type image --date 2024 # composed: format, kind, year
:::tip These filters work the same way across commands, and combine. See scoping a run. :::
The workflow
It reuses the vectors videre embed already computed, so
there is no new model to download and no image is read from disk again. On a
library that is already embedded it finishes in seconds to minutes rather than
hours.
videre embed # prerequisite, the slow part
videre classify # fast, reuses that work
videre search --category screenshot
Then use it to clear out the clutter:
videre search --category screenshot -k 5000 > /tmp/shots.txt
videre search --category meme | xargs -I{} mv {} ~/memes/
videre search --category document # receipts, tickets, forms
Resumable: rerunning only classifies what is not yet done, so adding photos
means scan, embed, classify again and only the new ones are considered.
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.
- 2d ago First seen · 157 lines · 13 tokens per session scan A 567ee4061429
classify is a command published in the GitHub repository erhangundogan/videre (2 stars, last pushed 3d ago), licensed Apache-2.0. It adds 13 tokens to every session and 1,598 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
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contrib
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memory-init
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.