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huggingface-accelerate

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

64

Quality

77%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./optional-skills/mlops/accelerate/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, well-structured skill with executable code and clean progressive disclosure, weakened by body length/redundancy and by the absence of explicit validation feedback loops in its distributed launch and checkpointing workflows.

Suggestions

Collapse the repeated full-script reprint in Workflow 1 (it duplicates Quick start) to a focused diff, and move the 'Version: 1.11.0+' line out of the main Resources list into a clearly labeled version/compatibility note.

Add an explicit validation checkpoint to the multi-node launch workflow (e.g. verify process count / run a quick sanity check before full training) and present checkpoint save/load as a validate-then-retry step rather than only under 'Common issues'.

Keep the reference split as-is; optionally add a one-line summary of what each references/*.md covers so navigation is even faster.

DimensionReasoningScore

Conciseness

The body is mostly efficient and assumes Claude's competence (no explaining what distributed training is), but at ~300 lines it has redundancy — Workflow 1 reprints the full original script already shown in Quick start — and the time-sensitive inline note 'Version: 1.11.0+' should live in a versioned/deprecated section rather than the main body. It is above a 1 because it is not padded with concepts Claude already knows.

2 / 3

Actionability

Every workflow provides fully executable code and exact commands — 'accelerate launch --multi_gpu --num_processes 8', 'accelerator = Accelerator(mixed_precision=\'fp16\')', and copy-paste ready deepspeed_config.json — matching the score-3 anchor of copy-paste-ready concrete examples.

3 / 3

Workflow Clarity

Workflows are clearly sequenced (convert → prepare → launch; config → launch), but the multi-step launch paths lack embedded validation checkpoints: there is no validate→fix→retry feedback loop for multi-node/multi-GPU launches or for checkpoint save/load. The checkpointing example is reactive ("Common issues") rather than an explicit checkpoint in the workflow. It is above 1 because the sequence is present and unambiguous.

2 / 3

Progressive Disclosure

SKILL.md is an overview with three well-signaled, one-level-deep references — 'See references/megatron-integration.md', 'references/custom-plugins.md', 'references/performance.md' — all of which exist as real files, and advanced detail (Megatron, custom plugins, perf tuning) is appropriately split out rather than inlined.

3 / 3

Total

10

/

12

Passed

Description

82%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A high-signal, third-person description with concrete capabilities and strong natural trigger terms, but it lacks an explicit 'Use when...' clause telling Claude when to activate it, which caps completeness.

Suggestions

Append an explicit trigger clause, e.g. 'Use when adding distributed/multi-GPU/multi-node training to a PyTorch script, or when the user mentions DeepSpeed, FSDP, DDP, mixed precision, or accelerate launch.'

Keep the current concrete action list but ensure the 'when to use' guidance names the user-side phrasings (multi-GPU, multi-node, scale training) that would prompt selection.

DimensionReasoningScore

Specificity

Lists multiple concrete actions: "4 lines to add distributed support to any PyTorch script", "Automatic device placement, mixed precision (FP16/BF16/FP8)", "Interactive config, single launch command" — matching the score-3 anchor that lists several specific concrete actions.

3 / 3

Completeness

It strongly answers "what" but contains no explicit "Use when..." or equivalent trigger clause stating when Claude should invoke it; per the rubric guideline a missing explicit trigger clause caps completeness at 2. It is not a 1 because the what-side is thorough.

2 / 3

Trigger Term Quality

Natural terms a user would say are well covered — "distributed training", "PyTorch script", "DeepSpeed/FSDP/Megatron/DDP", "mixed precision", "HuggingFace" — giving good coverage of the vocabulary users invoke for this need.

3 / 3

Distinctiveness Conflict Risk

It carves a clear niche (HuggingFace unified distributed training with named backends DeepSpeed/FSDP/Megatron/DDP) with distinct triggers unlikely to fire for unrelated skills.

3 / 3

Total

11

/

12

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository
NousResearch/hermes-agent
Reviewed

Table of Contents

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