BD 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.
Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime.
As a process D 43/100 · Unfinished process — References files that are not bundled: ../slime/references/api-reference.md
The same skill appears in 1 more place: AI-Research-SKILLs
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: ../slime/references/api-reference.md - note
frontmatter-keyunknown frontmatter key "dependencies"
Process rating: all ten parameters 43/100
- 0Tools and files. 1 referenced file(s) missing: ../slime/references/api-reference.md
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (miles-rl-training) differs from the folder (post-training-miles)
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 49 steps
- 100Execution cost. Instruction body is 2101 tokens
- 100Progress reporting. Reports progress
- low 12 top-level sections: this looks like several domains in one skill
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 234: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (1 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.