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miles-rl-training

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

58

Quality

73%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./backend/cli/skills/ml-inference/miles/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%Weight 40%Scale 1-5

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

A well-structured, command-heavy skill body with strong executable examples and checklisted workflows, but it undermines its own progressive disclosure: the two local reference files are orphaned (body links point outside the bundle to ../slime/) and their content is duplicated inline, which also inflates length. Consolidating the duplicate 'Key Features' sections and fixing the reference paths would lift both conciseness and organization.

Suggestions

Fix the two reference links to point at the local bundle files — replace '../slime/references/api-reference.md' with 'references/api-reference.md' — so the shipped references/api-reference.md is actually reachable.

Collapse the inline 'Common Issues and Solutions' and 'Configuration Reference' duplication by summarizing one or two top issues in SKILL.md and pointing to references/troubleshooting.md and references/api-reference.md for the rest.

Merge the 'Key Features' and 'Key Features (Conceptual)' sections into one, keeping a single explanation of R3, FP8, and INT4, and turn the bare MTP flag fragments into a complete train.py invocation.

DimensionReasoningScore

Conciseness

The body is largely command-driven and free of tutorial padding, but it carries real redundancy: the 'Key Features' section and the later 'Key Features (Conceptual)' section re-explain the same items (Unified FP8, R3 appears twice within a few lines), the intro paragraph is marketing-flavored ('high-performance, enterprise-ready... addresses critical challenges'), and 'Common Issues and Solutions' duplicates content that already lives in references/troubleshooting.md. This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' (3) rather than 4, where only minor trimming would be needed.

3 / 5

Actionability

Installation, Quick Start, and both workflows give complete, copy-paste-ready bash invocations with concrete flags, and troubleshooting prescribes exact commands and environment variables. It stops short of 5 because the 'Online MTP Training' blocks are bare flag fragments rather than complete commands, and the 'Key Features (Conceptual)' section hedges that 'specific CLI flags may vary' without giving the executable form.

4 / 5

Workflow Clarity

Both workflows are clearly sequenced with numbered steps, a prerequisites checklist for Workflow 1, and an explicit verification checklist ('Model loads without errors', 'Routing decisions are consistent', 'No NaN/Inf in loss values'), plus symptom-to-fix recovery guidance in the issues section. Not 5 because Workflow 2 (Speculative RL) has no verification step, and the checklist items state what to check but not how to check it (e.g., how to inspect loss values or confirm routing consistency).

4 / 5

Progressive Disclosure

The bundle ships references/api-reference.md and references/troubleshooting.md, but the body never links to them: its only two outbound references point to '../slime/references/api-reference.md', a path outside the skill bundle, while the Configuration Reference and 'Common Issues and Solutions' sections inline material that duplicates the local reference files. This matches 'some structure but could be better organized; references present but not clearly signaled; content that should be separate is inline' (3) — it is not 2 because section structure is clear and one-level-deep reference use is at least attempted.

3 / 5

Total

14

/

20

Passed

Description

73%Weight 40%Scale 1-5

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 solid description with an explicit 'Use when...' trigger clause and good domain keyword coverage. Its main weakness is a generic 'provides guidance for' action statement instead of enumerating the concrete capabilities (FP8 training, INT4 QAT, routing replay, speculative rollout) the skill actually delivers.

Suggestions

Replace 'Provides guidance for' with the concrete capabilities, e.g. 'Configures and runs enterprise-grade RL training of large MoE models with miles, including unified FP8, INT4 QAT, Rollout Routing Replay (R3), and speculative RL rollouts.'

Add one or two natural trigger synonyms users would type (e.g. 'reinforcement learning', 'post-training', 'GRPO') to broaden keyword matching.

DimensionReasoningScore

Specificity

The description names the domain ("enterprise-grade RL training using miles, a production-ready fork of slime") and touches concrete technical areas ("large MoE models with FP8/INT4", "train-inference alignment", "speculative RL"), but the action verb is generic ("Provides guidance for") and no specific actions like configuring training, enabling FP8, or running a training job are listed. It sits between the 'names domain and 1-2 concrete actions' (3) and 'lists several specific actions' (4) anchors, closer to 3 because the 'what' is a single generic action rather than a list of capabilities.

3 / 5

Completeness

Both parts are present: what ("Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime") and an explicit when ("Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput"). It falls short of 5 because the 'what' half is vague ("provides guidance" rather than concrete capabilities), leaving the when-clause carrying the specificity.

4 / 5

Trigger Term Quality

Strong domain keywords users would naturally say: "RL training", "MoE models", "FP8", "INT4", "train-inference alignment", "speculative RL", "throughput". A few natural variants are missing (e.g., "reinforcement learning" spelled out, "GRPO", "post-training", "quantization-aware training"), so it fits the 'good keyword coverage; a few natural terms missing' anchor rather than the comprehensive-synonyms anchor at 5.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche by naming the specific tool (miles, "a production-ready fork of slime") and distinctive triggers (FP8/INT4 MoE training, speculative RL, train-inference alignment) that no generic skill would claim, matching the 'clear niche with distinct triggers; minimal conflict risk' anchor. It is not a 4 because the named-tool plus fork-relationship framing makes confusion with adjacent RL frameworks unlikely.

5 / 5

Total

16

/

20

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.

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 2 suspicious

Warning

Total

13

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/ml-inference/miles/SKILL.md

Reviewed

Table of Contents

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