BC sglang-amd-bench
Benchmark sglang serving performance on AMD Instinct GPUs (MI355X, MI300X, MI308X) with various parallel configurations (TP, DP, EP). Covers throughput/latency sweeps (ISL, OSL, concurrency), TTFT/TPOT measurement, and config comparison. Mix mode only.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Dangerous commands
cmd-background-processscripts/serve.sh:155Starts a background / autostarted processnohup $CMD > "$LOG_FILE" 2>&1 &
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 6393 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, git, python, node) that frontmatter does not declare
- 70Execution cost. Instruction body is 6393 tokens
- 100Steps. 64 steps
- 100Failures and branches. 22 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (12 tags): a typed call is more reliable
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
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 252: enough signal without eating the budget
- +4Structure: 34 headings
- +3Step-by-step instructions: 64 items
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 7 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.
External checks
ClawHub: suspicious
This is a coherent GPU benchmarking skill, but it automatically enables model-supplied code execution and should be reviewed before use.
LLM: suspicious (high) · VirusTotal: · 29 May 2026