skilld
All skills
nvidia avatar

/jetson-video-benchmark

@9bb5a39
by NVIDIA Corporationnvidia/skills3.5k stars
424

Use when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and user media, or producing a clearly labeled documentation-derived planning estimate when representative media is absent. Also use for a video request limited to PSNR or SSIM, to apply the terminal scope response.

Ask your Agent

Use this Skill: https://skilld.dev/gh/nvidia/skills/jetson-video-benchmark

This session only. Nothing lands on disk.

BENCHMARK.md

≈2k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Skill Benchmark: jetson-video-benchmark

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: jetson-video-benchmark
  • Evaluation date: 2026-09-16
  • Evaluator version: 1.5.6
  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)
  • Tasks: 7 evaluation tasks (7 positive)
  • Dataset digest: sha256:d1c525d9b65879a764e8676b2177c71573dee5cbfbceb816d55ca67074b7086b (skill-evaluator-dataset-snapshot/1)
  • Attempts per task: 3
  • Environment: k8s-sandbox
  • Tier 2 evidence: required for publication
  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

  • is safe to use;
  • produces correct answers;
  • is discovered and activated when needed;
  • helps the agent complete the user's goal and expected workflow; and
  • avoids wasted skill and tool usage.

Results at a Glance

Measure Claude Code (Baseline → Skill Uplift) Codex (Baseline → Skill Uplift)
Overall Not available 94.5% — baseline ran, but no comparable score was available; uplift unavailable
Security Not available 82.1% → 100.0% (+17.9 points)
Correctness Not available 52.9% → 100.0% (+47.1 points)
Discoverability Not available 94.3% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness Not available 34.8% → 93.0% (+58.2 points)
Efficiency Not available 85.2% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

Agent Dataset case With skill Without skill Delta Change Coverage
claude-code All cases 811,096 1,310,555 N/A N/A skill 7/7; base 10/21
claude-code benchmark-4k-planning-estimate 141,466 72,101 N/A N/A skill 1/1; base 2/2
claude-code benchmark-camera-direction-medium-quality 150,509 65,461 +85,048 +129.92% skill 1/1; base 1/1
claude-code benchmark-documented-estimate 186,708 359,806 N/A N/A skill 1/1; base 3/3
claude-code benchmark-natural-camera-capacity-planning 109,049 123,731 -14,682 -11.87% skill 1/1; base 1/1
claude-code benchmark-no-content 63,049 599,533 N/A N/A skill 1/1; base 1/3
claude-code benchmark-quality-boundary 60,960 59,664 +1,296 +2.17% skill 1/1; base 1/1
claude-code benchmark-undocumented-p4 99,355 30,259 +69,096 +228.35% skill 1/1; base 1/1
codex All cases 543,961 1,572,817 N/A N/A skill 7/7; base 14/14
codex benchmark-4k-planning-estimate 65,929 45,385 +20,544 +45.27% skill 1/1; base 1/1
codex benchmark-camera-direction-medium-quality 127,179 134,210 N/A N/A skill 1/1; base 3/3
codex benchmark-documented-estimate 65,755 159,060 N/A N/A skill 1/1; base 2/2
codex benchmark-natural-camera-capacity-planning 172,004 138,724 N/A N/A skill 1/1; base 3/3
codex benchmark-no-content 28,517 1,032,822 N/A N/A skill 1/1; base 3/3
codex benchmark-quality-boundary 28,338 33,628 -5,290 -15.73% skill 1/1; base 1/1
codex benchmark-undocumented-p4 56,239 28,988 +27,251 +94.01% skill 1/1; base 1/1
ALL AGENTS Dataset aggregate 1,355,057 2,883,372 N/A N/A skill 14/14; base 24/35

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

Tier Purpose Status Evidence
Tier 1 Static validation PASSED WITH OBSERVATIONS 11 validator(s); 9 finding(s)
Tier 2 Semantic deduplication PASSED 2 validator(s); 0 finding(s)
Tier 3 Live agent evaluation PASS 2 agent(s); 7 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>
  • MEDIUM QUALITY/quality_efficiency: Deeply nested references in benchmark-output-contract.md (skills/jetson-video-benchmark/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/jetson-video-benchmark/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/jetson-video-benchmark/SKILL.md)
  • LOW QUALITY/quality_correctness: No examples provided (skills/jetson-video-benchmark/SKILL.md)
  • LOW QUALITY/quality_discoverability: Description very long (389 chars, recommend 50-150) (skills/jetson-video-benchmark/SKILL.md)
  • 4 additional finding(s) are available in the full evaluation artifacts.
</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
Dimension Question Scored signals
Security Is it safe to use? security (100%)
Correctness Is the answer correct? accuracy (100%)
Discoverability Was the right skill loaded when needed? skill_execution (100%)
Effectiveness Did the skill help complete the task? goal_accuracy (50%) + behavior_check (50%)
Efficiency Did it avoid wasted tool calls and token usage? skill_efficiency (50%) + token_efficiency (50%)
  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).
  • Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.
  • skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.
  • skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).
  • accuracy (Accuracy): final-answer correctness against the reference answer.
  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.
  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.
  • token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).
</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

Source: SKILL.md on GitHub

Third-party checks

No alerts6d3 checks · Risk SAFE
  • Gen Agent Trust Hub6d

    The jetson-video-benchmark skill is a well-secured utility for measuring video codec performance on NVIDIA Jetson hardware. It follows security best practices, including using private workspaces with restricted permissions, robustly validating external URLs, and implementing a provenance system to verify the integrity of the performance samples it executes.

  • Socket6d

    No alerts

  • Snyk6d

    Risk: LOW · No issues

Provenance

Signed by skilld at 9bb5a39. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated last week

Capability

Other metadata
metadata
{
  "author": "Vinit Bansal <vinitkumarb@nvidia.com>",
  "tags": [
    "jetson",
    "video-codec-sdk",
    "pynvvideocodec",
    "benchmark",
    "nvenc",
    "nvdec"
  ],
  "languages": [
    "markdown"
  ],
  "data-classification": "public"
}

README badge

README badge for nvidia/skills/jetson-video-benchmark