SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill sampling-and-indexinggit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/sampling-and-indexing)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/sampling-and-indexing"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/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/benchflow-ai/skillsbench/sampling-and-indexing"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/sampling-and-indexing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- sampling-and-indexing — 100% identical, 0 lines differ
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.
- 9d ago First seen · 34 lines · 27 tokens per session scan A 317046cac52d
sampling-and-indexing is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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