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/tilegym-converting-cutile-triton-to-cutile-rs

@48a26cf
by NVIDIA Corporationnvidia/tilegym821 stars
89

Use this skill to convert, port, or translate Triton-TileIR or cuTile-Python GPU kernels to cutile-rs (Rust). The orchestrator runs scripts/preflight.sh, then drives a bounded Agent A -> B -> D -> E pipeline (Agent C is diagnostic, Agent F optional), delegating all kernel/host/correctness/perf work to sub-agents and routing by each stage's single-line VERDICT.

  • 44 files
  • 452.9 KB
  • CC-BY-4
  • Updated 2 months ago
  • GitHub

Use this Skill: https://skilld.dev/gh/nvidia/tilegym/tilegym-converting-cutile-triton-to-cutile-rs

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BENCHMARK.md

≈1.2k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Skill Benchmark: tilegym-converting-cutile-triton-to-cutile-rs

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

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

Evaluation Metadata

  • Skill: tilegym-converting-cutile-triton-to-cutile-rs
  • Evaluation date: 2026-08-12
  • Evaluator version: 1.2.4
  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)
  • Tasks: 4 evaluation tasks (1 positive, 3 negative)
  • Dataset digest: sha256:923db300232e27c2fb9a24813ef0bd0fba8ec752ad96373782447e050583cc9e (skill-evaluator-dataset-snapshot/1)
  • Attempts per task: 1
  • Environment: k8s-sandbox
  • 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 83% → 99% (+16 points) 79% → 98% (+18 points)
Security 100% → 100% (±0 points) 75% → 100% (+25 points)
Correctness 75% → 100% (+25 points) 75% → 100% (+25 points)
Discoverability 88% → 100% (+12 points) 86% → 92% (+6 points)
Effectiveness 79% → 100% (+21 points) 78% → 98% (+20 points)
Efficiency 75% → 96% (+21 points) 84% → 100% (+16 points)

How to read this table: baseline is the same task attempted without the target skill. Uplift is skill score - baseline score, shown in percentage points.

Example: 47% → 92% (+45 points) means the skill-assisted run scored 92%, 45 percentage points above its 47% no-skill baseline.

Tier Status

Tier Purpose Status Evidence
Tier 1 Static validation PASSED WITH OBSERVATIONS 1 validator(s); 2 finding(s)
Tier 2 Semantic deduplication NOT RUN No result was recorded
Tier 3 Live agent evaluation PASS 2 agent(s); 4 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>
  • LOW SCHEMA/unexpected_file: Unexpected 'concepts' in skill root (skills/tilegym-converting-cutile-triton-to-cutile-rs/concepts)
  • LOW SCHEMA/unexpected_file: Unexpected 'examples' in skill root (skills/tilegym-converting-cutile-triton-to-cutile-rs/examples)
</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 or skill usage? skill_efficiency (100%)
  • 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).
  • Token efficiency is a separate report-only signal. It does not change a dimension score or the overall verdict.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.
  • skill_execution (Skill Execution): whether the expected skill was found and executed.
  • skill_efficiency (Efficiency): routing quality, workspace-aware skill reads, and productive tool use.
  • 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.
</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

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Signed by skilld at 48a26cf. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 4 days ago.

Activeupdated 2 months ago
metadata
{
  "author": "TileGym Team <TileGym@nvidia.com>"
}

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