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 xuansenpa1/skillrevise --skill sampling-and-indexinggit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote 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/xuansenpa1/skillrevise/sampling-and-indexing)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/sampling-and-indexing"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/sampling-and-indexing/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/xuansenpa1/skillrevise/sampling-and-indexing"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/sampling-and-indexing.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 | $0.00027 | $0.00363 |
| Opus 5 | $0.00014 | $0.00181 |
| Sonnet 5 | $0.00005 | $0.00073 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
sampling-and-indexing 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 12d 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.
This is a copy
100% identical to sampling-and-indexing — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
When to use
- You need to decide a sampling stride/FPS and ensure all downstream outputs (interval instructions, per-frame artifacts, etc.) cover the same frame range with consistent indices.
Core steps
- Read video metadata: frame count, fps, resolution.
- Choose a sampling strategy (e.g., every 10 frames or target ~10–15 fps) to produce
sample_ids. - Only produce instructions and masks for
sample_ids; the max index must be< total_frames. - Use a strict interval key format such as
"{start}->{end}"(integers only). Decide (and document) whetherendis inclusive or exclusive, and be consistent.
Pseudocode
import cv2
VIDEO_PATH = "<path/to/video>"
cap=cv2.VideoCapture(VIDEO_PATH)
n=int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps=cap.get(cv2.CAP_PROP_FPS)
step=10 # example
sample_ids=list(range(0, n, step))
if sample_ids[-1] != n-1:
sample_ids.append(n-1)
# Generate all downstream outputs only for sample_ids
Self-check list
-
sample_idsstrictly increasing, all < total frame count. - Output coverage max index matches
sample_ids[-1](or matches your documented sampling policy). - JSON keys are plain
start->end, no extra text. - Any per-frame artifact store (e.g., NPZ) contains exactly the sampled frames and no extras.
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.
- 12d ago First seen · 34 lines · 27 tokens per session scan A 317046cac52d
sampling-and-indexing is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 6d ago), licensed MIT. It adds 27 tokens to every session and 363 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to sampling-and-indexing, differing in 0 lines, and is treated as a copy.
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