Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node).
Install Launch With Slurm
$ npx skills add mlc-ai/pith-train --skill launch-with-slurm -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mlc-ai/pith-train launch-with-slurm --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/mlc-ai/pith-train.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/launch-with-slurm .claude/skills/launch-with-slurm && 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 "launch-with-slurm" agent skill from https://github.com/mlc-ai/pith-train/tree/main/.agents/skills/launch-with-slurm into .claude/skills/launch-with-slurm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launch-with-slurm", 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/mlc-ai/pith-train/tree/main/.agents/skills/launch-with-slurmType 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 mlc-ai/pith-train --skill launch-with-slurm -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mlc-ai/pith-train launch-with-slurm --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mlc-ai/pith-train.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/launch-with-slurm .agents/skills/launch-with-slurm && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "launch-with-slurm" agent skill from https://github.com/mlc-ai/pith-train/tree/main/.agents/skills/launch-with-slurm into .agents/skills/launch-with-slurm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launch-with-slurm", 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 mlc-ai/pith-train --skill launch-with-slurm -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mlc-ai/pith-train launch-with-slurm --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mlc-ai/pith-train.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/launch-with-slurm .cursor/skills/launch-with-slurm && 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 "launch-with-slurm" agent skill from https://github.com/mlc-ai/pith-train/tree/main/.agents/skills/launch-with-slurm into .cursor/skills/launch-with-slurm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launch-with-slurm", 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/mlc-ai/pith-train.git --path .agents/skills/launch-with-slurm--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 mlc-ai/pith-train --skill launch-with-slurm -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mlc-ai/pith-train launch-with-slurm --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mlc-ai/pith-train.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/launch-with-slurm .gemini/skills/launch-with-slurm && 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 "launch-with-slurm" agent skill from https://github.com/mlc-ai/pith-train/tree/main/.agents/skills/launch-with-slurm into .gemini/skills/launch-with-slurm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launch-with-slurm", 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 mlc-ai/pith-train launch-with-slurmInstalls 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 mlc-ai/pith-train --skill launch-with-slurm -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mlc-ai/pith-train.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/launch-with-slurm .github/skills/launch-with-slurm && 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 "launch-with-slurm" agent skill from https://github.com/mlc-ai/pith-train/tree/main/.agents/skills/launch-with-slurm into .github/skills/launch-with-slurm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launch-with-slurm", 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 mlc-ai/pith-train --skill launch-with-slurm -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mlc-ai/pith-train launch-with-slurm --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mlc-ai/pith-train.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/launch-with-slurm .opencode/skills/launch-with-slurm && 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 "launch-with-slurm" agent skill from https://github.com/mlc-ai/pith-train/tree/main/.agents/skills/launch-with-slurm into .opencode/skills/launch-with-slurm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "launch-with-slurm", 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.
Facts
- Skill name
launch-with-slurm- GitHub stars
- 355
- Token cost
- ~1.4k tokens
- SKILL.md length
- 644 words
- Files
- 1
- Skills in repo
- 10
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node).
- Works in 2 steps: Find the Job ID → Build the srun Command
- Work needs to run on allocated compute — from direct user requests (run on the cluster
- SKILL.md covers Step 1: Find the Job ID, Step 2: Build the srun Command and References
- Calls ssh and bash
What it does
Launch With Slurm is an agent skill from mlc-ai/pith-train. Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node). Use whenever work needs to run on allocated compute — from direct user requests ("run on the cluster", "use my running job", "on my allocation", "launch on slurm", "train across N nodes", "dispatch the job") OR from within another skill's workflow (e.g., validate-correctness running validation on the allocation, add-new-model reaching pp=2/ep=2). Covers finding the target job ($SLURMJOBID or squeue, including array-job ID…
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering. The repository describes itself as: Compact and Agent-Native MoE Training System. The licence is Apache-2.0.
When your agent uses it
- Work needs to run on allocated compute — from direct user requests (run on the cluster
- Use my running job
- On my allocation
- Launch on slurm
Example prompts
- “run on the cluster”
- “use my running job”
- “on my allocation”
- “/launch-with-slurm”
Workflow steps
2 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 87208d9. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
Shell commands in SKILL.md call:
sshbash
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
slurm.schedmd.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Launch With Slurm loads about 1.4k tokens when it runs. Until then it costs about 191 tokens; SKILL.md has 644 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.
Safety
Auto-check passed
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.
SKILL.md
The full file from mlc-ai/pith-train at commit 87208d9, republished under its Apache-2.0 licence (© mlc-ai). 644 words, ~1,402 tokens.
Download SKILL.mdSave it as
.claude/skills/launch-with-slurm/SKILL.md (or your agent's skills folder).- name
- launch-with-slurm
- description
- Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node). Use whenever work needs to run on allocated compute — from direct user requests ("run on the cluster", "use my running job", "on my allocation", "launch on slurm", "train across N nodes", "dispatch the job") OR from within another skill's workflow (e.g., validate-correctness running validation on the allocation, add-new-model reaching pp=2/ep=2). Covers finding the target job ($SLURM_JOB_ID or squeue, including array-job ID resolution), reading the allocation with scontrol, the srun flags that matter (--jobid, -W 0, -N, -o, --open-mode, --nodelist, --overlap), and gotchas like GPU sharing with live steps and distributed-aware output redirection.
Launch with SLURM
srun --jobid=<jobid> attaches a step to a running allocation regardless of where it is invoked — login node, ssh'd compute node, or inside the job itself. The scheduler draws nodes from the allocation pool and may pick a node other than the invoking one. Default to it over raw torchrun/bash: srun propagates env vars, handles distributed-aware I/O, and manages signals across ranks correctly — even on a single node with multiple GPUs. The examples/*/launch.sh scripts read SLURM_NNODES/SLURM_NODEID, which srun sets inside a step, so they work unchanged.
Step 1: Find the Job ID
Two sources, one answer — the numeric JobId of the job (e.g. 2193449):
$SLURM_JOB_IDis set — you are inside the job, or in ansshsession on an allocated node that inherits the job environment. Use it as-is; it is already the numeric JobId.- Otherwise — list the user's jobs and pick a RUNNING one: by name if the user named it, ask if several are ambiguous or none is RUNNING, skip PENDING (no compute yet):
squeue -u $USER -o "%.10i %.9T %.6D %.24N %.24j %.10L"Array jobs: resolve to the numeric JobId. squeue reports elements as <master>_<task> (e.g. 2193448_1), but --jobid wants the element's own numeric JobId — given 2193448_1, it strips the _1, resolves to the array master, and fails with "Job is pending execution" when the master has pending elements:
scontrol show job 2193448_1 | grep -oP '^JobId=\K[0-9]+' # → 2193449Then read the allocation — don't guess, ask SLURM:
scontrol show job $JOBIDKey fields to extract:
| Field | Example | What it tells you |
|---|---|---|
AllocTRES | cpu=208,mem=1860368M,node=1,billing=208,gres/gpu=8 | Node count, GPUs per node, CPUs, memory |
NodeList | orchard-flame-5 or orchard-flame-[3-6] | Which hosts; on most clusters ssh <name> gives direct access |
For a quick remaining-time check, use squeue directly — it returns D-HH:MM:SS without needing to parse timestamps:
squeue -h -j $JOBID -o %LBefore launching anything long-running, compare this against the estimated runtime. If the budget is too tight, surface this to the user instead of launching and getting killed mid-run.
Show full SKILL.md (345 more words)Show less
Step 2: Build the srun Command
--jobid=<jobid>— anchor the step to the allocation. Required when$SLURM_JOB_IDis unset or holds a different job; redundant but harmless when it already holds the target.-N <n>— number of nodes to dispatch to. In most training runs this matches PP, but the full parallelism plan and GPUs-per-node determine total nodes (e.g., PP=1 with EP=16 on 8-GPU nodes still needs 2).-N1borrows one node of a multi-node allocation for probes or single-node tests.-W 0— wait indefinitely for stragglers after the first task exits. The default behavior terminates remaining tasks shortly after the first one ends, which kills workers that are still cleanly shutting down. Always use-W 0for training and evaluation runs.-o <file>— stdout redirection. Use this instead of piping throughtee. On multi-node,teeing srun output collapses concurrent writes from all ranks.-ois distributed-aware — srun collects output from every rank into the single specified file, preserving the one-command-one-log abstraction. By convention, PithTrain runs log underlogging/<descriptive-name>.log.--open-mode=appendvs--open-mode=truncate— for resumed training,appendpreserves history across restarts. Usetruncatefor fresh runs where overwriting is intended.--nodelist=<hosts>— restrict dispatch to specific nodes. Useful for debugging at a smaller scale (e.g., 4 nodes allocated, but debug with 2 specific ones).--overlap— share the allocation's CPUs, memory, and GPUs with the job's other steps. Without it, a step reserves the whole allocation, so any later step silently pends until it finishes — pass--overlapwhenever launching alongside a running step.--gres=gpu:<k>— request a subset of the job's GPUs for the step. By default a step sees every GPU the job holds on the node, sotorchrun --nproc-per-node=gpujust works. Sharing means contention: probenvidia-smiutilization before launching heavy work onto a job whose GPUs are already busy; if it's saturated, surface this to the user instead of piling on.
srun execs the command directly, not through a shell — invoke scripts as bash <script> or ensure the +x bit.
References
- srun options — full list of flags
- srun environment variables —
SLURM_*variables available inside scripts launched by srun
© mlc-ai, 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
Files
Just SKILL.md in .agents/skills/launch-with-slurm of mlc-ai/pith-train.
Open the folder on GitHubat commit 87208d9
Compare with similar skills
Launch With Slurm 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 |
|---|---|---|---|---|---|---|
| Launch With Slurm this skillmlc-ai/pith-train | 355 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.9k | 14 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
Similar skills
Add Uint Support
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
Segment Anything Model Guide
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
1password
trpc-group/trpc-agent-go
Set up and use 1Password CLI (op). An agent skill from trpc-group/trpc-agent-go.
Planning With Files
jarrodwatts/claude-code-config
Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage.
More from mlc-ai/pith-train
All 10 skills in this repoAnalyze Nsys Profile
mlc-ai/pith-train
Query a captured PithTrain Nsight Systems profile to measure compute/communication overlap, locate exposed comm by DualPipeV stage, and inspect per-rank stream behavior.
Capture Nsys Profile
mlc-ai/pith-train
Capture a Nsight Systems (.nsys-rep) profile of a short PithTrain run for performance analysis.
Validate Correctness
mlc-ai/pith-train
Validates that code changes do not break training correctness by comparing loss deltas against a base-vs-base run-to-run envelope.
Validate Performance
mlc-ai/pith-train
Measures the throughput difference between two branches with force-balanced routing.
Setup Benchmark Inputs
mlc-ai/pith-train
Set up the minimal set of artifacts (tokenized DCLM corpus shard + released HuggingFace checkpoint converted to DCP) required to benchmark, profile, or regression-test a MoE model in PithTrain.
Add New Model
mlc-ai/pith-train
Adds support for a new MoE language model to PithTrain. An agent skill from mlc-ai/pith-train.
Related
Categories
Questions about Launch With Slurm
What does Launch With Slurm do?
Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node). Launch With Slurm is an agent skill from mlc-ai/pith-train. Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node).
When should I use Launch With Slurm?
Launch With Slurm fits situations like: work needs to run on allocated compute — from direct user requests (run on the cluster; use my running job; on my allocation; launch on slurm.
How do I install Launch With Slurm in Claude Code?
Run `npx skills add mlc-ai/pith-train --skill launch-with-slurm -a claude-code`. Or copy the skill folder (.agents/skills/launch-with-slurm in mlc-ai/pith-train) into .claude/skills/launch-with-slurm in your project. Claude Code loads it when a task matches its description.
How do I install Launch With Slurm in Codex?
Run `npx skills add mlc-ai/pith-train --skill launch-with-slurm -a codex`. Or copy the skill folder (.agents/skills/launch-with-slurm in mlc-ai/pith-train) into .agents/skills/launch-with-slurm in your project. Codex loads it when a task matches its description.
Can I use Launch With Slurm in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mlc-ai/pith-train --skill launch-with-slurm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/launch-with-slurm, .gemini/skills/launch-with-slurm, .github/skills/launch-with-slurm and .opencode/skills/launch-with-slurm in your project.
What does Launch With Slurm need to run?
Going by SKILL.md and its folder, Launch With Slurm needs the command-line tools its instructions call (ssh and bash).
Does Launch With Slurm access the network?
SKILL.md names 1 domain. As links in the text: slurm.schedmd.com. This is read from the text; nothing was executed.
Is Launch With Slurm safe to install?
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.
What licence does Launch With Slurm use?
Launch With Slurm 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.
How many tokens does Launch With Slurm use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Launch With Slurm?
Skills that share tags, products or a category with Launch With Slurm: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Launch With Slurm?
mlc-ai (a GitHub organization) maintains it in mlc-ai/pith-train, which has 355 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 9, 2026.
Source: mlc-ai/pith-train on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.