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BD ray-train

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Orchestra-Research/AI-Research-SKILLs Agent Skills author: Orchestra-Research MIT 2 files body ≈ 2 524 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Distributed training orchestration across clusters.

As a process D 35/100 · Unfinished process — References files that are not bundled: references/hyperparameter-tuning.md, references/custom-loops.md

ProcedureGoogle CloudAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
D
35/100
Unfinished process
References files that are not bundled: references/hyperparameter-tuning.md, references/custom-loops.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: ray-train (davila7/claude-code-templates)

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/hyperparameter-tuning.md
  • warning missing-ref reference to a missing file: references/custom-loops.md
  • note frontmatter-key unknown frontmatter key "dependencies"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: references/hyperparameter-tuning.md, references/custom-loops.md
  • 0Tools and files. 2 referenced file(s) missing: references/hyperparameter-tuning.md, references/custom-loops.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Steps. 33 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2524 tokens
  • 100Running it twice. Mutating operations check current state

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 304: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.