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 skills/getsquish/squish/video-navigationnpx skills add getsquish/squish --skill video-navigationgit clone --depth 1 https://github.com/getsquish/squishWrote 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/getsquish/squish/video-navigation)<a href="https://agentmods.dev/skills/getsquish/squish/video-navigation"><img src="https://agentmods.dev/badge/skills/getsquish/squish/video-navigation.svg" alt="Measured on agentmods" 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 | $0.00112 | $0.02124 |
| Opus 5 | $0.00056 | $0.01062 |
| Sonnet 5 | $0.00022 | $0.00425 |
| Haiku 4.5 | $0.00011 | $0.00212 |
Grade C, and why
video-navigation scanned grade C 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 3d 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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
and do not refuse — compress the clip into **timestamped contact sheets** (one image per How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video navigation: treat video as an address space
You have vision but cannot ingest video. When a task involves a video's content, do not guess and do not refuse — compress the clip into timestamped contact sheets (one image per window of the clip, frames sampled evenly, each cell stamped with its timecode) and, when the tool provides it, an audio-activity band on the same absolute timeline. The map is your index into the video; zooming is how you navigate it.
The reasoning primitive: video → visual + audio activity map → inspect → zoom where it matters → answer with timecodes.
Non-goals. This is a video navigation and retrieval skill, not a video editing workflow. Navigation over editing; retrieval over transformation. Audio activity is an energy envelope, not transcription, sound classification, or emotion inference; a sheet is a sequence map, not motion replacement. Your deliverable is answers with timestamps — absolute seconds that hand off cleanly to any editing or clipping tool, which is where this skill stops.
When to use
- "What happens in this video / screen recording?"
- The question spans time: before/after, a scene change, progress, "find the moment when…".
- The answer needs precise citations ("at 0:07 the press comes down").
- The clip is too long or too large to ingest any other way.
When not to use: the user needs one specific frame only (extract that frame instead); the question cannot be answered from either frames or activity timing. Pairing note: if the question is about what was said, pair with an ASR/transcript tool. Audio activity can locate when something happened, but cannot recover words or identify the source of a sound.
What you need: a squisher
Any tool implementing this contract (see Wiring below for today's implementations):
(video, density?, start?, end?) → sheet image(s) + per-cell timecodes + optional audio activity
density— grid size per sheet (3x3…6x6): more frames per call.start/end— window the run to a time range: more precision per frame.- Output: sheet images in time order, plus the timecode of every cell.
- Local Squish 0.3+ also returns
audio.samples[](time= absolute seconds,level=0..1) and burns the same activity envelope above the grid.normalization: clip_peakmeans zoom calls stay comparable to the whole clip.
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.
- 3d ago First seen · 147 lines · 112 tokens per session scan C 6a6499041b40
video-navigation is a skill published in the GitHub repository getsquish/squish (1 stars, last pushed 10d ago), licensed Apache-2.0. It adds 112 tokens to every session and 2,124 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
frame-data-chart-nyt
NYT-newsroom typography, staggered reveal animation, and editorial-grade charts (line, bar, or range band).
frame-light-leak-cinema
Film light leaks, grain, 16:9 letterbox, and large serif type for cinematic openings or chapter cards.
frame-creative-voltage
Electric split with hand-drawn script — offset panels slide in, display title rises with an outlined word, script strokes itself in.
frame-electric-studio
Two-panel split with quote as hero — white/blue panels open from center, accent bar grows, quote reveals line by line.
frame-glitch-title
Digital glitch, chromatic offset, and data-corruption title frame for video transitions or cyberpunk heroes.
vfx-text-cursor
Cursor light trail, chromatic rays, and directional flares for word-by-word quote reveals in video intros.