Repository Analysis

llm-d/llm-d

Achieve state of the art inference performance with modern accelerators on Kubernetes

9.0 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of llm-d/llm-d, a Shell project with 3,798 GitHub stars. SynthScan v2.0 examined 62,050 lines of code across 648 source files, recording 249 pattern matches distributed across 11 syntactic categories. The overall adjusted score of 9.0 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

9.0
Adjusted Score
9.0
Raw Score
100%
Time Factor
2026-07-14
Last Push
3.8K
Stars
Shell
Language
62.0K
Lines of Code
648
Files
249
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 114LOW 135

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 249 distinct pattern matches across 11 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators80 hits · 268 pts
SeverityFileLineSnippetContext
MEDIUMdocker/scripts/cuda/common/install-efa.sh6# -------------------------------COMMENT
MEDIUMdocker/scripts/cuda/builder/build-ucx.sh5# --------------------------------------------COMMENT
MEDIUMdocker/scripts/cuda/builder/build-ucx.sh9# --------------------------------------------COMMENT
MEDIUMguides/flow-control/scripts/tuning_wizard.py18# ==========================================COMMENT
MEDIUMguides/flow-control/scripts/tuning_wizard.py20# ==========================================COMMENT
MEDIUMguides/flow-control/scripts/tuning_wizard.py45# ==========================================COMMENT
MEDIUMguides/flow-control/scripts/tuning_wizard.py47# ==========================================COMMENT
MEDIUMguides/flow-control/scripts/tuning_wizard.py104# ==========================================COMMENT
MEDIUMguides/flow-control/scripts/tuning_wizard.py106# ==========================================COMMENT
MEDIUM…s/recipes/observability/alerts/epp-alerting-rules.yaml9 # ---------------------------------------------------------------------------COMMENT
MEDIUM…s/recipes/observability/alerts/epp-alerting-rules.yaml11 # ---------------------------------------------------------------------------COMMENT
MEDIUM…s/recipes/observability/alerts/epp-alerting-rules.yaml63 # ---------------------------------------------------------------------------COMMENT
MEDIUM…s/recipes/observability/alerts/epp-alerting-rules.yaml65 # ---------------------------------------------------------------------------COMMENT
MEDIUM…chmark-templates/agentic-serving-nemotron-3-ultra.yaml1# ---------------------------------------------------------------------------COMMENT
MEDIUM…chmark-templates/agentic-serving-nemotron-3-ultra.yaml18# ---------------------------------------------------------------------------COMMENT
MEDIUMguides/agentic-serving/benchmark-templates/guide.yaml1# ---------------------------------------------------------------------------COMMENT
MEDIUMguides/agentic-serving/benchmark-templates/guide.yaml22# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py14# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py16# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py22# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py24# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py29# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py31# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py66# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py69# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py175# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py177# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py201# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/sync-nightly-matrix.py203# ---------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh4# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh6# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh24# ── Defaults ────────────────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh30# ── Flag parsing ────────────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh46# ── Persistent curl pod ─────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh83# ── Run curl via kubectl exec on the persistent pod ─────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh93# ── Discover Gateway address ────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh103# ── Create persistent curl pod ──────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh106# ── Determine MODEL_ID ──────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh162# ── Main test loop (10 iterations) ──────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate.sh211# ── Optional: hand off to a guide-specific validator ────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-predicted-latency.sh4# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-predicted-latency.sh6# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-predicted-latency.sh21# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-predicted-latency.sh90# ── Discover EPP service ────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-predicted-latency.sh122# ── Discover model ──────────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-predicted-latency.sh144# ── Warmup loop: feed the predictor's training window ───────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-predicted-latency.sh172# ── Scrape EPP /metrics ─────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh4# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh6# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh21# -----------------------------------------------------------------------------COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh92# ── Discover EPP service ────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh118# ── Discover model ──────────────────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh140# ── Stage the request payload on the curl pod ───────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh147# ── Metric extraction helpers ───────────────────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh213# ── Mixed-contention burst: all three bands at once ─────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh272# ── (1) Each band landed in its own queue ───────────────────────────────────COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh287# ── (2) QoS: best-effort waits longer than premium under contention ─────────COMMENT
MEDIUMhelpers/smoke-test/healthcheck.sh4# -----------------------------------------------------------------------------COMMENT
MEDIUMhelpers/smoke-test/healthcheck.sh19# -----------------------------------------------------------------------------COMMENT
MEDIUMhelpers/smoke-test/healthcheck.sh21# ── Defaults ─────────────────────────────────────────────────────────────────COMMENT
20 more matches not shown…
Structural Annotation Overuse70 hits · 110 pts
SeverityFileLineSnippetContext
LOWdocs/operations/observability/setup.md11## Step 1: Install Prometheus and GrafanaCOMMENT
LOWdocs/operations/observability/setup.md144## Step 2: Load Grafana DashboardsCOMMENT
LOWdocs/operations/observability/setup.md174## Step 3: Install Distributed Tracing (Optional)COMMENT
LOWdocs/operations/observability/alerting.md19## Step 1: Apply the Alerting RulesCOMMENT
LOWdocs/operations/observability/alerting.md30## Step 2: VerifyCOMMENT
LOWdocs/operations/observability/tracing.md24## Step 1: Deploy OTel Collector and JaegerCOMMENT
LOWdocs/operations/observability/tracing.md68## Step 2: Enable Tracing on the Model Server and Routing ProxyCOMMENT
LOWdocs/operations/observability/tracing.md89## Step 3: Enable Tracing on EPPCOMMENT
LOWdocs/operations/observability/tracing.md104## Step 4: View TracesCOMMENT
LOWdocs/operations/observability/metrics.md16## Step 1: Enable Model Server MetricsCOMMENT
LOWdocs/operations/observability/metrics.md75## Step 3: Enable EPP MetricsCOMMENT
LOWdocs/operations/observability/metrics.md125## Step 4: View DashboardsCOMMENT
LOWdocs/operations/observability/metrics.md177## Step 5: Query MetricsCOMMENT
LOWdocs/operations/rollouts/adapter-rollout.md58## Step 1: Establishing A Baseline (Alias v1)COMMENT
LOWdocs/operations/rollouts/adapter-rollout.md140## Step 2: Gradual RolloutCOMMENT
LOWdocs/infrastructure/providers/openshift-aws/README.md16## Step 1: Create a Red Hat OpenShift Service on AWS (ROSA) ClusterCOMMENT
LOWdocs/infrastructure/providers/openshift-aws/README.md25## Step 2: Add a Machineset with NVIDIA GPU InstancesCOMMENT
LOWdocs/infrastructure/providers/openshift-aws/README.md34## Step 3: Enable GPU support on OpenShift with the NFD and GPU OperatorsCOMMENT
LOWdocs/infrastructure/gateway/gke.md21## Step 1: Install Gateway API and Gateway API Inference Extension CRDsCOMMENT
LOWdocs/infrastructure/gateway/gke.md41## Step 2: Deploy the GatewayCOMMENT
LOWdocs/infrastructure/gateway/gke.md65## Step 3: Verify the GatewayCOMMENT
LOWdocs/infrastructure/gateway/gke.md82## Step 4: Send a RequestCOMMENT
LOWdocs/infrastructure/gateway/agentgateway.md18## Step 1: Install Gateway API and Gateway API Inference Extension CRDsCOMMENT
LOWdocs/infrastructure/gateway/agentgateway.md22## Step 2: Install AgentgatewayCOMMENT
LOWdocs/infrastructure/gateway/agentgateway.md58## Step 3: Deploy the GatewayCOMMENT
LOWdocs/infrastructure/gateway/agentgateway.md99## Step 4: Send a RequestCOMMENT
LOWdocs/infrastructure/gateway/envoy-ai-gateway.md18## Step 1: Install Gateway API and Gateway API Inference Extension CRDsCOMMENT
LOWdocs/infrastructure/gateway/envoy-ai-gateway.md22## Step 2: Install Envoy AI GatewayCOMMENT
LOWdocs/infrastructure/gateway/envoy-ai-gateway.md128## Step 3: Deploy the GatewayCOMMENT
LOWdocs/infrastructure/gateway/envoy-ai-gateway.md157## Step 4: Send a RequestCOMMENT
LOWdocs/infrastructure/gateway/istio.md15## Step 1: Install Gateway API and Gateway API Inference Extension CRDsCOMMENT
LOWdocs/infrastructure/gateway/istio.md19## Step 2: Install IstioCOMMENT
LOWdocs/infrastructure/gateway/istio.md44## Step 3: Deploy the GatewayCOMMENT
LOWdocs/infrastructure/gateway/istio.md73## Step 4: Send a RequestCOMMENT
LOWguides/asynchronous-processing/README.md32#### Step 1: Deploy llm-d RouterCOMMENT
LOWguides/asynchronous-processing/README.md45#### Step 2: Configure ValuesCOMMENT
LOWguides/asynchronous-processing/README.md52#### Step 3: Deploy Async ProcessorCOMMENT
LOW…prefix-cache/modelserver/tpu/base/vllm/patch-vllm.yaml18 # WARNING: This changes the HOST memory settings, not just the container.COMMENT
LOWguides/batch-gateway/README.md45### Step 1: Create the NamespaceCOMMENT
LOWguides/batch-gateway/README.md52### Step 2: Create the SecretsCOMMENT
LOWguides/batch-gateway/README.md63### Step 3: Configure the llm-d Router URLCOMMENT
LOWguides/batch-gateway/README.md75### Step 4: DeployCOMMENT
LOWguides/flow-control/tuning.md43### Step 1: Gather System Telemetry (For Compute Bound)COMMENT
LOWguides/flow-control/tuning.md55### Step 2: Gather Workload Statistics (For Memory Bound)COMMENT
LOWguides/flow-control/tuning.md65### Step 3: Run the Tuning WizardCOMMENT
LOWguides/flow-control/tuning.md103### Step 4: Apply ConfigurationCOMMENT
LOWguides/multi-model-routing/README.md56## Step 1: Deploy IPPCOMMENT
LOWguides/multi-model-routing/README.md82## Step 2: Create Model Mapping ConfigMapsCOMMENT
LOWguides/multi-model-routing/README.md95## Step 3: Configure HTTPRoutesCOMMENT
LOWguides/multi-model-routing/README.md107## Step 4: Test the DeploymentCOMMENT
LOW…s/agentic-serving/modelserver/tpu/vllm/patch-vllm.yaml15 # WARNING: This changes the HOST memory settings, not just the container.COMMENT
LOW…des/workload-autoscaling/README.replica-rebalancing.md36## Step 1: Define a GPU ResourceQuotaCOMMENT
LOW…des/workload-autoscaling/README.replica-rebalancing.md59## Step 2: Enable the Replica RebalancerCOMMENT
LOW…des/workload-autoscaling/README.replica-rebalancing.md81## Step 3: Opt HPAs Into Replica Rebalancer ManagementCOMMENT
LOW…des/workload-autoscaling/README.replica-rebalancing.md146## Step 4: Verify Replica Rebalancer BehaviorCOMMENT
LOW…es/workload-autoscaling/README.multi-inference-pool.md12## Step 1: Deploy an Additional Helm ReleaseCOMMENT
LOW…es/workload-autoscaling/README.multi-inference-pool.md42## Step 2: Deploy the Model ServerCOMMENT
LOW.github/workflows/link-checker.md46#### Step 1: Identify Changed Markdown FilesCOMMENT
LOW.github/workflows/link-checker.md56#### Step 2: Extract and Check LinksCOMMENT
LOW.github/workflows/link-checker.md74#### Step 3: Classify ResultsCOMMENT
10 more matches not shown…
Modern AI Meta-Vocabulary20 hits · 70 pts
SeverityFileLineSnippetContext
MEDIUMguides/recipes/router/calibration/calibrate.sh12# GUIDE_NAME=agentic-serving NAMESPACE=llm-d-agentic-serving \COMMENT
MEDIUMguides/predicted-latency-routing/README.md300Uses the agentic-serving guide's `inference-perf` workload, tuned for the 480B model and very long (up to 256K-token) coCODE
MEDIUMguides/predicted-latency-routing/README.md303# Fetch the existing-stack benchmark runner from llm-d-benchmark (the script the agentic-serving guide uses).COMMENT
MEDIUM…atency-routing/modelserver/tpu/vllm/kustomization.yaml4# Reuse the agentic-serving guide's TPU model server: Qwen3-Coder-480B-A35B-Instruct-FP8COMMENT
MEDIUM…d-latency-routing/router/predicted-latency.values.yaml26 # Sized for the 256K-context TPU agentic case (Qwen3-Coder-480B): matching longerCOMMENT
MEDIUM…d-latency-routing/router/predicted-latency.values.yaml27 # prefixes raises the cache-hit rate on long, shared agentic prompts. Tradeoff: itCOMMENT
MEDIUM…tency-routing/router/predicted-latency-slo.values.yaml27 # Sized for the 256K-context TPU agentic case (Qwen3-Coder-480B): matching longerCOMMENT
MEDIUM…tency-routing/router/predicted-latency-slo.values.yaml28 # prefixes raises the cache-hit rate on long, shared agentic prompts. Tradeoff: itCOMMENT
MEDIUMguides/agentic-serving/nemotron-3-ultra-550b-h200.md153# from the guide directory: guides/agentic-servingCOMMENT
MEDIUMguides/agentic-serving/nemotron-3-ultra-550b-h200.md161# from the guide directory: guides/agentic-servingCOMMENT
MEDIUMguides/agentic-serving/nemotron-3-ultra-550b-h200.md194curl -LJO "https://raw.githubusercontent.com/llm-d/llm-d/main/guides/${GUIDE_NAME}/benchmark-templates/agentic-serving-nCODE
MEDIUM…chmark-templates/agentic-serving-nemotron-3-ultra.yaml2# inference-perf workload template for the agentic-serving guideCOMMENT
MEDIUM…chmark-templates/agentic-serving-nemotron-3-ultra.yaml12# Render with `envsubst < agentic-serving-nemotron-3-ultra.yaml > config.yaml`COMMENT
MEDIUMguides/agentic-serving/benchmark-templates/guide.yaml2# inference-perf workload template for the agentic-serving guide.COMMENT
MEDIUM…-serving/router/agentic-serving-tpu-disagg.values.yaml1## agentic-serving (TPU / P/D-disaggregated) guide overrides for the llm-d router.COMMENT
MEDIUM…-serving/router/agentic-serving-tpu-disagg.values.yaml4## This deployment serves the agentic code-generation workload on Google TPUs withCOMMENT
MEDIUM…-serving/router/agentic-serving-tpu-disagg.values.yaml7## by the non-disaggregated TPU deployment in agentic-serving.values.yaml.COMMENT
MEDIUM…agentic-serving/router/agentic-serving-gpu.values.yaml1## agentic-serving (GPU / P/D-disaggregated) guide overrides for the llm-d router.COMMENT
MEDIUM…agentic-serving/router/agentic-serving-gpu.values.yaml4## This deployment serves the agentic code-generation workload on NVIDIA GPUs withCOMMENT
MEDIUM…agentic-serving/router/agentic-serving-gpu.values.yaml7## by the non-disaggregated TPU deployment in agentic-serving.values.yaml.COMMENT
Over-Commented Block39 hits · 39 pts
SeverityFileLineSnippetContext
LOW…r/scripts/cuda/runtime/install-offloading-connector.sh1#!/bin/bashCOMMENT
LOWdocker/scripts/cuda/runtime/install-vllm.sh1#!/bin/bashCOMMENT
LOWdocker/scripts/cuda/builder/build-nixl.sh1#!/bin/bashCOMMENT
LOWdocker/scripts/cuda/builder/build-nvshmem.sh1#!/bin/bashCOMMENT
LOWdocker/scripts/cuda/builder/build-nvshmem.sh81 CMAKE_EXTRA_FLAGS+=(COMMENT
LOWdocker/scripts/cuda/builder/build-lmcache.sh1#!/bin/bashCOMMENT
LOWdocker/scripts/cuda/builder/build-lmcache.sh21# so libcuda.so is not available from the host.COMMENT
LOWdocker/scripts/cuda/builder/build-gdrcopy.sh1#!/bin/bashCOMMENT
LOWdocker/scripts/cuda/builder/build-compiled-wheels.sh1#!/bin/bashCOMMENT
LOWdocker/scripts/cuda/builder/build-ucx.sh1#!/bin/bashCOMMENT
LOW…des/no-kubernetes-deployment/router/epp/endpoints.yaml1# Endpoints file consumed by the file-discovery plugin.COMMENT
LOWguides/no-kubernetes-deployment/router/envoy/envoy.yaml1# Envoy config for the no-Kubernetes file-discovery deployment.COMMENT
LOW…des/wide-ep-lws/modelserver/gpu/vllm/base/prefill.yaml81 --all2all-backend deepep_high_throughputCOMMENT
LOWguides/wide-ep-lws/monitoring/kustomization.yaml1apiVersion: kustomize.config.k8s.io/v1alpha1COMMENT
LOW…outing/router/precise-prefix-cache-routing.values.yaml1## precise-prefix-cache-routing guide overrides for the router.COMMENT
LOWguides/recipes/observability/generate-traffic-pd.sh1#!/bin/bashCOMMENT
LOWguides/recipes/router/calibration/calibrate.sh1#!/bin/bashCOMMENT
LOWguides/recipes/router/calibration/calibrate.sh21# MODEL_NAME — model name vLLM is serving (default: Qwen/Qwen3-32B)COMMENT
LOW…-baseline/modelserver/gpu/vllm/gpt-oss/patch-vllm.yaml21 - "--reasoning-parser=openai_gptoss"COMMENT
LOW…zed-baseline/modelserver/gpu/vllm/base/patch-vllm.yaml21 - name: HF_TOKENCOMMENT
LOW…baseline/modelserver/gpu/sglang/base/patch-sglang.yaml21 # - "--otlp-traces-endpoint=http://otel-collector:4317"COMMENT
LOW…ptimized-baseline/modelserver/cpu/vllm/patch-vllm.yaml21 - name: HF_TOKENCOMMENT
LOW…ptimized-baseline/modelserver/xpu/vllm/patch-vllm.yaml21 - "--disable-access-log-for-endpoints=/health,/metrics,/v1/models"COMMENT
LOW…ized-baseline/modelserver/amd/sglang/patch-sglang.yaml21 # - "--enable-trace"COMMENT
LOW…mized-baseline/modelserver/tpu/v6/vllm/patch-vllm.yaml21 # - "--otlp-traces-endpoint=http://otel-collector:4317"COMMENT
LOW…mized-baseline/modelserver/tpu/v7/vllm/patch-vllm.yaml21 # - "--otlp-traces-endpoint=http://otel-collector:4317"COMMENT
LOW…chmark-templates/agentic-serving-nemotron-3-ultra.yaml1# ---------------------------------------------------------------------------COMMENT
LOWguides/agentic-serving/benchmark-templates/guide.yaml1# ---------------------------------------------------------------------------COMMENT
LOW…ving/aggregation/modelserver/base/vllm/patch-vllm.yaml21 env:COMMENT
LOW…d-baseline-autoscaling/keda/base/wva-scaledobject.yaml1# KEDA ScaledObject driven by WVA's wva_desired_replicas metric.COMMENT
LOW…des/workload-autoscaling/scripts/nightly-deploy-cks.sh1#!/usr/bin/env bashCOMMENT
LOW.github/workflows/slash-test-nightly.yaml1name: /test-nightly Slash CommandCOMMENT
LOW.github/workflows/slash-test-nightly.yaml21# Examples:COMMENT
LOW.github/scripts/e2e/e2e-validate-predicted-latency.sh1#!/usr/bin/env bashCOMMENT
LOW.github/scripts/e2e/e2e-validate-predicted-latency.sh81 kubectl delete pod -n "$NAMESPACE" "$CURL_POD_NAME" --ignore-not-found >/dev/null 2>&1 || trueCOMMENT
LOW.github/scripts/e2e/e2e-validate-flow-control.sh1#!/usr/bin/env bashCOMMENT
LOWhelpers/smoke-test/healthcheck.sh1#!/usr/bin/env bashCOMMENT
LOWhelpers/client-setup/install-deps.sh1#!/usr/bin/env bashCOMMENT
LOW…ers/local-llm-d-cuda-builder/build-local-llm-d-cuda.sh1#!/usr/bin/env bashCOMMENT
AI Slop Vocabulary11 hits · 26 pts
SeverityFileLineSnippetContext
MEDIUM…re/providers/digitalocean/gpu-configs/l40s-values.yaml38# Enable comprehensive metrics collectionCOMMENT
MEDIUM…ders/digitalocean/gpu-configs/rtx-4000-ada-values.yaml47# Enable comprehensive metrics collectionCOMMENT
MEDIUM…ders/digitalocean/gpu-configs/rtx-6000-ada-values.yaml36# Enable comprehensive metrics collectionCOMMENT
MEDIUMguides/pd-disaggregation/benchmark-templates/tpu.yaml17 namespace: *namespace # Namespace where harness is deployed. Typically with stack.CODE
MEDIUMguides/pd-disaggregation/benchmark-templates/tpu.yaml28workload: # yaml configuration for harness workload(s)CODE
MEDIUM…chmark-templates/agentic-serving-nemotron-3-ultra.yaml35 namespace: *namespace # Namespace where harness is deployed. Typically with stack.CODE
MEDIUMguides/agentic-serving/benchmark-templates/guide.yaml39 namespace: *namespace # Namespace where harness is deployed. Typically with stack.CODE
MEDIUMguides/agentic-serving/benchmark-templates/guide.yaml49workload: # yaml configuration for harness workload(s)CODE
LOWscripts/warn-vllm-precompiled.sh41# Append to vllm user's rc if it exists; otherwise just create itCOMMENT
MEDIUM.github/workflows/slash-test-nightly.yaml13# harness=<name> Benchmark harness (default: inference-perf)COMMENT
MEDIUM.github/scripts/e2e/e2e-validate-flow-control.sh274# missed by scrape timing, so it is the robust backbone assertion.COMMENT
Redundant / Tautological Comments12 hits · 18 pts
SeverityFileLineSnippetContext
LOW…ture/providers/digitalocean/verify-do-prerequisites.sh60 # Check if we're on DOKSCOMMENT
LOW…ture/providers/digitalocean/verify-do-prerequisites.sh123 # Check if DO CSI driver is availableCOMMENT
LOW…/providers/digitalocean/monitoring/setup-monitoring.sh72 # Check if kubectl can connect to clusterCOMMENT
LOW…/providers/digitalocean/monitoring/setup-monitoring.sh177 # Check if release already existsCOMMENT
LOW…prefix-cache/modelserver/tpu/base/vllm/patch-vllm.yaml22 # Check if the VFIO IOMMU module parameter exists, and if so, increase theCOMMENT
LOW…recipes/observability/generate-prometheus-tls-certs.sh185 # Check if namespace existsCOMMENT
LOWguides/recipes/observability/load-llm-d-dashboards.sh31# Check if namespace existsCOMMENT
LOWguides/recipes/observability/load-llm-d-dashboards.sh37# Check if dashboard directory existsCOMMENT
LOW…es/recipes/observability/install-prometheus-grafana.sh240 # Check if certificates already existCOMMENT
LOW…es/recipes/observability/install-prometheus-grafana.sh268 # Check if user workload monitoring is enabledCOMMENT
LOW…s/agentic-serving/modelserver/tpu/vllm/patch-vllm.yaml19 # Check if the VFIO IOMMU module parameter exists, and if so, increase theCOMMENT
LOW.github/scripts/e2e/e2e-validate.sh134 # Check if we got a specific errorCOMMENT
Hyper-Verbose Identifiers7 hits · 8 pts
SeverityFileLineSnippetContext
LOWdocker/scripts/cpu/install_nixl.py45def install_system_dependencies():CODE
LOWdocker/scripts/cpu/install_nixl.py74def build_and_install_prerequisites(args):CODE
LOWguides/flow-control/scripts/tuning_wizard.py49def calculate_compute_constraint(throughput: float, latency_sec: float) -> int:CODE
LOWguides/flow-control/scripts/tuning_wizard.py53def calculate_memory_constraint(CODE
LOWguides/flow-control/scripts/tuning_wizard.py91def calculate_lookahead_buffer(active_batch: int, max_num_batched_tokens: int, isl_mean: Optional[float]) -> int:CODE
LOWscripts/lint-envvars.py36def find_locally_defined_vars(script_content: str) -> Set[str]:CODE
LOWscripts/lint-dockerfile-envvars.py12def parse_script_requirements(script_path: Path) -> Set[str]:CODE
Self-Referential Comments2 hits · 6 pts
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MEDIUM…recipes/observability/generate-prometheus-tls-certs.sh104 # Define the Prometheus service DNS namesCOMMENT
MEDIUM…recipes/observability/generate-prometheus-tls-certs.sh197 # Create the secretCOMMENT
Excessive Try-Catch Wrapping4 hits · 6 pts
SeverityFileLineSnippetContext
LOWscripts/lint-envvars.py94 except Exception as e:CODE
LOWscripts/lint-dockerfile-envvars.py16 except Exception:CODE
MEDIUMscripts/lint-dockerfile-envvars.py180 print(f"Error: Scripts directory not found: {scripts_dir}", file=sys.stderr)CODE
MEDIUMscripts/lint-dockerfile-envvars.py186 print(f"Error: Dockerfile not found: {dockerfile}", file=sys.stderr)CODE
Example Usage Blocks2 hits · 4 pts
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LOWguides/recipes/router/calibration/calibrate.sh11# Usage:COMMENT
LOW…ers/local-llm-d-cuda-builder/build-local-llm-d-cuda.sh5# Usage:COMMENT
Deep Nesting2 hits · 2 pts
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LOWscripts/sync-nightly-matrix.py147CODE
LOWscripts/lint-dockerfile-envvars.py45CODE