Arize Evaluator
github/awesome-copilot
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and…
Generate tuned manifests and evaluate node tuning snapshots. An agent skill from openshift-eng/ai-helpers.
$ npx skills add openshift-eng/ai-helpers --skill scripts -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install openshift-eng/ai-helpers scripts --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/node-tuning/skills/scripts .claude/skills/scripts && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "scripts" agent skill from https://github.com/openshift-eng/ai-helpers/tree/main/plugins/node-tuning/skills/scripts into .claude/skills/scripts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scripts", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/openshift-eng/ai-helpers/tree/main/plugins/node-tuning/skills/scriptsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add openshift-eng/ai-helpers --skill scripts -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install openshift-eng/ai-helpers scripts --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/node-tuning/skills/scripts .agents/skills/scripts && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "scripts" agent skill from https://github.com/openshift-eng/ai-helpers/tree/main/plugins/node-tuning/skills/scripts into .agents/skills/scripts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scripts", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add openshift-eng/ai-helpers --skill scripts -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install openshift-eng/ai-helpers scripts --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/node-tuning/skills/scripts .cursor/skills/scripts && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "scripts" agent skill from https://github.com/openshift-eng/ai-helpers/tree/main/plugins/node-tuning/skills/scripts into .cursor/skills/scripts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scripts", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/openshift-eng/ai-helpers.git --path plugins/node-tuning/skills/scripts--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add openshift-eng/ai-helpers --skill scripts -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install openshift-eng/ai-helpers scripts --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/node-tuning/skills/scripts .gemini/skills/scripts && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "scripts" agent skill from https://github.com/openshift-eng/ai-helpers/tree/main/plugins/node-tuning/skills/scripts into .gemini/skills/scripts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scripts", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install openshift-eng/ai-helpers scriptsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add openshift-eng/ai-helpers --skill scripts -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/node-tuning/skills/scripts .github/skills/scripts && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "scripts" agent skill from https://github.com/openshift-eng/ai-helpers/tree/main/plugins/node-tuning/skills/scripts into .github/skills/scripts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scripts", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add openshift-eng/ai-helpers --skill scripts -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install openshift-eng/ai-helpers scripts --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/node-tuning/skills/scripts .opencode/skills/scripts && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "scripts" agent skill from https://github.com/openshift-eng/ai-helpers/tree/main/plugins/node-tuning/skills/scripts into .opencode/skills/scripts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scripts", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
scriptsGenerate tuned manifests and evaluate node tuning snapshots. An agent skill from openshift-eng/ai-helpers.
Scripts is an agent skill from openshift-eng/ai-helpers. Generate tuned manifests and evaluate node tuning snapshots
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `analyze_node_tuning.py` and `generate_tuned_profile.py`).
The repository describes itself as: Developer productivity tools for Claude Code & other AI assistants. The licence is Apache-2.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit a627176. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
python3From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Scripts loads about 2.2k tokens when it runs. Until then it costs about 17 tokens; SKILL.md has 618 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from openshift-eng/ai-helpers at commit a627176, republished under its Apache-2.0 licence (© openshift-eng). 618 words, ~2,176 tokens.
.claude/skills/scripts/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Detailed instructions for invoking the helper utilities that back /node-tuning commands:
generate_tuned_profile.py renders Tuned manifests (tuned.openshift.io/v1).analyze_node_tuning.py inspects live nodes or sosreports for tuning gaps.python3 --version).plugins/node-tuning/skills/scripts/ are accessible.oc CLI when validating or applying manifests.oc CLI access plus a valid KUBECONFIG when capturing /proc//sys or sosreport via oc debug node/<name>. The sosreport workflow pulls the registry.redhat.io/rhel9/support-tools image (override with --toolbox-image or TOOLBOX_IMAGE) and requires registry access. HTTP(S) proxy env vars from the host are forwarded automatically when present, but using a proxy is optional.generate_tuned_profile.pyCollect Inputs
--profile-name: Tuned resource name.--summary: [main] section summary.--include, --main-option, --variable, --sysctl, --section (SECTION:KEY=VALUE).--machine-config-label key=value, --match-label key[=value].--priority (default 20), --namespace, --output, --dry-run.--list-nodes/--node-selector to inspect nodes and --label-node NODE:KEY[=VALUE] (plus --overwrite-labels) to tag machines.Inspect or Label Nodes (optional)
# List all worker nodes
python3 plugins/node-tuning/skills/scripts/generate_tuned_profile.py --list-nodes --node-selector "node-role.kubernetes.io/worker" --skip-manifest
# Label a specific node for the worker-hp pool
python3 plugins/node-tuning/skills/scripts/generate_tuned_profile.py \
--label-node ip-10-0-1-23.ec2.internal:node-role.kubernetes.io/worker-hp= \
--overwrite-labels \
--skip-manifestRender the Manifest
python3 plugins/node-tuning/skills/scripts/generate_tuned_profile.py \
--profile-name "$PROFILE" \
--summary "$SUMMARY" \
--sysctl net.core.netdev_max_backlog=16384 \
--match-label tuned.openshift.io/custom-net \
--output .work/node-tuning/$PROFILE/tuned.yaml--output to write <profile-name>.yaml in the current directory.--dry-run to print the manifest to stdout.Review Output
yq or open in an editor for readability.Validate and Apply
oc apply --server-dry-run=client -f <manifest>.oc apply -f <manifest>.ValueError with descriptive messages.--machine-config-label or --match-label) are supplied.python3 plugins/node-tuning/skills/scripts/generate_tuned_profile.py \
--profile-name realtime-worker \
--summary "Realtime tuned profile" \
--include openshift-node --include realtime \
--variable isolated_cores=1 \
--section bootloader:cmdline_ocp_realtime=+systemd.cpu_affinity=${not_isolated_cores_expanded} \
--machine-config-label machineconfiguration.openshift.io/role=worker-rt \
--priority 25 \
--output .work/node-tuning/realtime-worker/tuned.yamlpython3 plugins/node-tuning/skills/scripts/generate_tuned_profile.py \
--profile-name openshift-node-hugepages \
--summary "Boot time configuration for hugepages" \
--include openshift-node \
--section bootloader:cmdline_openshift_node_hugepages="hugepagesz=2M hugepages=50" \
--machine-config-label machineconfiguration.openshift.io/role=worker-hp \
--priority 30 \
--output .work/node-tuning/openshift-node-hugepages/hugepages-tuned-boottime.yamlanalyze_node_tuning.pyInspect either a live node (/proc, /sys) or an extracted sosreport snapshot for tuning signals (CPU isolation, IRQ affinity, huge pages, sysctl state, networking counters) and emit actionable recommendations.
python3 plugins/node-tuning/skills/scripts/analyze_node_tuning.py --format markdownpython3 plugins/node-tuning/skills/scripts/analyze_node_tuning.py \
--node worker-rt-0 \
--kubeconfig ~/.kube/prod \
--format markdownpython3 plugins/node-tuning/skills/scripts/analyze_node_tuning.py \
--node worker-rt-0 \
--toolbox-image registry.example.com/support-tools:latest \
--sosreport-arg "--case-id=01234567" \
--sosreport-output .work/node-tuning/sosreports \
--format jsonpython3 plugins/node-tuning/skills/scripts/analyze_node_tuning.py \
--sosreport /path/to/sosreport-2025-10-20python3 plugins/node-tuning/skills/scripts/analyze_node_tuning.py \
--sosreport /path/to/sosreport \
--format json --output .work/node-tuning/node-analysis.json--node <name> (with optional --kubeconfig / --oc-binary). By default the helper runs sosreport remotely from inside the RHCOS toolbox container (registry.redhat.io/rhel9/support-tools). Override the image with --toolbox-image, extend the sosreport command with --sosreport-arg, or disable the curated OpenShift flags via --skip-default-sosreport-flags. Pass --no-collect-sosreport to fall back to the direct /proc snapshot mode.--sosreport <dir> for archived diagnostics; detection finds embedded proc/ and sys/./proc and /sys).--proc-root or --sys-root when the layout differs.cpuinfo, kernel cmdline parameters (isolcpus, nohz_full, tuned.non_isolcpus), default IRQ affinities, huge page counters, sysctl values (net, vm, kernel), transparent hugepage settings, netstat/sockstat counters, and ps snapshots (when available in sosreport)./node-tuning:generate-tuned-profile to codify desired state.proc/ or sys/ directories trigger descriptive errors.# Node Tuning Analysis
## System Overview
- Hostname: worker-rt-1
- Kernel: 4.18.0-477.el8
- NUMA nodes: 2
- Kernel cmdline: `BOOT_IMAGE=... isolcpus=2-15 tuned.non_isolcpus=0-1`
## CPU & Isolation
- Logical CPUs: 32
- Physical cores: 16 across 2 socket(s)
- SMT detected: yes
- Isolated CPUs: 2-15
...
## Recommended Actions
- Configure net.core.netdev_max_backlog (>=32768) to accommodate bursty NIC traffic.
- Transparent Hugepages are not disabled (`[never]` not selected). Consider setting to `never` for latency-sensitive workloads.
- 4 IRQs overlap isolated CPUs. Relocate interrupt affinities using tuned profiles or irqbalance..work/node-tuning/<host>/analysis.json for historical tracing.© openshift-eng, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in plugins/node-tuning/skills/scripts of openshift-eng/ai-helpers.
Open the folder on GitHubat commit a627176
Scripts next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Scripts this skillopenshift-eng/ai-helpers | 120 | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 2 repos | ~8.1k | Automated safety check: Notes | MIT | |
| LLM Evaluationdavila7/claude-code-templates | 32k | 12 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Agent Evaluationsickn33/agentic-awesome-skills | 47k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| LLM Fine Tuningsickn33/agentic-awesome-skills | 47k | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| EvaluatorsArize-ai/phoenix | 12k | — | ~1.7k | Automated safety check: Pass | Custom licence |
github/awesome-copilot
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and…
davila7/claude-code-templates
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
sickn33/agentic-awesome-skills
Evaluate agent behavior with versioned cases and explicit verifiers.
sickn33/agentic-awesome-skills
Set up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP.
Arize-ai/phoenix
Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output.
Jeffallan/claude-skills
Guides LLM fine-tuning with LoRA and QLoRA through Hugging Face PEFT, from dataset validation and training checks to adapter merging, quantization and deployment.
openshift-eng/ai-helpers
Find and independently validate actionable reliability defects across OpenShift release jobs and presubmits, then export portable issue handoffs.
openshift-eng/ai-helpers
Fetch and address all PR review comments — categorize by priority, make code changes, post replies, and push.
openshift-eng/ai-helpers
Categorize Jira issues into Red Hat Sankey Activity Type categories using MCP Jira tools.
openshift-eng/ai-helpers
Decide whether a GitHub PR has unanswered authorized review comments or new required CI failures worth a follow-up agent.
openshift-eng/ai-helpers
Analyze OpenShift must-gather diagnostic data including cluster operators, pods, nodes, and network components.
openshift-eng/ai-helpers
Schema for the autodl JSON data file produced by payload-analysis for database ingestion — you must use this skill whenever generating the autodl JSON file
Generate tuned manifests and evaluate node tuning snapshots. An agent skill from openshift-eng/ai-helpers. Scripts is an agent skill from openshift-eng/ai-helpers.
Run `npx skills add openshift-eng/ai-helpers --skill scripts -a claude-code`. Or copy the skill folder (plugins/node-tuning/skills/scripts in openshift-eng/ai-helpers) into .claude/skills/scripts in your project. Claude Code loads it when a task matches its description.
Run `npx skills add openshift-eng/ai-helpers --skill scripts -a codex`. Or copy the skill folder (plugins/node-tuning/skills/scripts in openshift-eng/ai-helpers) into .agents/skills/scripts in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openshift-eng/ai-helpers --skill scripts -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scripts, .gemini/skills/scripts, .github/skills/scripts and .opencode/skills/scripts in your project.
Going by SKILL.md and its folder, Scripts needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Scripts is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Scripts: Arize Evaluator (github/awesome-copilot, 40k stars), LLM Evaluation (davila7/claude-code-templates, 32k stars), Agent Evaluation (sickn33/agentic-awesome-skills, 47k stars) and LLM Fine Tuning (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
openshift-eng (a GitHub organization) maintains it in openshift-eng/ai-helpers, which has 120 GitHub stars. The repository holds 118 skills in this directory. The repository was last updated on October 6, 2026.
Source: openshift-eng/ai-helpers on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.