Run Shell commands as computational jobs, on local machines or HPC clusters, through Shell, Slurm, PBS, LSF, Bohrium, etc.

LGPL-3.0-or-laterAuto-check passedBackend & APIs

Install Dpdisp Submit

skills CLI
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill dpdisp-submit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills dpdisp-submit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tools/dpdisp-submit .claude/skills/dpdisp-submit && rm -rf skills-src

Use ~/.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/

Facts

Skill name
dpdisp-submit
GitHub stars
148
Token cost
~3.1k tokens
SKILL.md length
1,356 words
Files
1
Skills in repo
62
Repo updated
First seen
Licence
LGPL-3.0-or-later

At a glance

Run Shell commands as computational jobs, on local machines or HPC clusters, through Shell, Slurm, PBS, LSF, Bohrium, etc.

  • The user needs to submit batch jobs to a cluster
  • SKILL.md covers Syntax & Protocol, Execution Workflow, Long-Running Jobs and Strict Guardrails, plus 1 more section
  • Calls uvx
  • Run commands on a remote server

What it does

Dpdisp Submit is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Run Shell commands as computational jobs, on local machines or HPC clusters, through Shell, Slurm, PBS, LSF, Bohrium, etc. USE WHEN the user needs to submit batch jobs to a cluster, run commands on a remote server, execute tasks via job schedulers (Slurm, PBS, LSF), or safely run long-term/background shell commands that require state tracking and auto-recovery.

Its SKILL.md is about 3.1k 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 Backend & APIs, covering Background jobs. The repository describes itself as: Agent skills to run computational-chemistry tasks, used in OpenClaw. The licence is LGPL-3.0-or-later.

When your agent uses it

  • The user needs to submit batch jobs to a cluster
  • Run commands on a remote server
  • Execute tasks via job schedulers (Slurm
  • Safely run long-term/background shell commands that require state tracking and auto-recovery

Example prompts

  • “/dpdisp-submit”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 5c19e75. 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:

    • uvx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uvx, which can reach the network depending on how they are called.

    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

Dpdisp Submit loads about 3.1k tokens when it runs. Until then it costs about 94 tokens; SKILL.md has 1,356 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

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 jinzhezenggroup/computational-chemistry-agent-skills at commit 5c19e75, republished under its LGPL-3.0-or-later licence (© jinzhezenggroup). 1,356 words, ~3,061 tokens.

Download SKILL.mdSave it as .claude/skills/dpdisp-submit/SKILL.md (or your agent's skills folder).
name
dpdisp-submit
description
Run Shell commands as computational jobs, on local machines or HPC clusters, through Shell, Slurm, PBS, LSF, Bohrium, etc. USE WHEN the user needs to submit batch jobs to a cluster, run commands on a remote server, execute tasks via job schedulers (Slurm, PBS, LSF), or safely run long-term/background shell commands that require state tracking and auto-recovery.
license
LGPL-3.0-or-later
metadata.author
deepmodeling
metadata.version
1.0

dpdisp-submit

This Skill guides the Agent to use the DPDispatcher tool to convert Shell commands into computational jobs and submit them to local machines or High-Performance Computing (HPC) clusters (supporting environments such as Shell, Slurm, PBS, LSF, Bohrium, etc.).

Prerequisites: uv and internet access.

Syntax & Protocol

This section defines the field mappings, variable syntax, and special flags for the configuration file.

Protocol Acquisition (Initialize)

As an Agent, before gathering information and building the configuration, you MUST FIRST execute the following command to read and learn the latest Schema protocol specifications and requirements:

bash
uvx --with dpdispatcher dargs doc dpdispatcher.entrypoints.submit.submission_args
Field Mapping

You must accurately translate the gathered user requirements into the following core JSON hierarchy:

  • machine: Defines the execution location and method (connection protocol, scheduler type).
  • resources: Defines the computational resource requirements (nodes, CPUs, GPUs, queues, etc.).
  • task_list: Defines the specific Shell commands to be executed and the file flow paths.
Environment Variable Syntax & Injection Rules

If user-specified values (e.g., username, Token, remote path) need to be read from local environment variables, you must strictly use the ${ENV_VAR_NAME} format in the template.

  • Example: "remote_root": "${USER_HPC_WORKSPACE}"
Reference & Reuse

The protocol allows the use of the {"$ref": "other.json"} syntax to nest and reference reusable configuration snippets from other JSON files (the referenced file is loaded first, and then the current file's fields override or extend it). The relative path for $ref is resolved relative to the execution directory where submission.json is located. You must ensure that the execution path strictly matches the path pointed to by $ref.

Path Resolution Rules
  • Base Directory (work_base): Defines the base working directory level for all tasks, typically set to . (i.e., the current execution directory).
  • Task and File Path Resolution: task_work_path is resolved relative to work_base, whereas the file paths specified in forward_files are strictly resolved relative to task_work_path.
Dry-Run Testing (--dry-run)

Parses the configuration, generates local directories, and validates the Schema, but DOES NOT actually submit the job to the machine or cluster. You can use this flag for a final safety check before real execution.

Execution Workflow

As an Agent, you MUST strictly execute tasks in the sequence of the following stages, without skipping any steps:

Information Gathering

When feeling vague or uncertain about the specific parameters and configuration information for running the job, you MUST proactively ask the user in natural language to supplement the necessary information.

Secure Build

You MUST generate the configuration file based on the acquired Schema protocol and the gathered information.

  • Pure Static Configuration: If no environment variable injection is needed, directly generate the final submission.json.
  • Environment Variable Injection Required:
    • You must generate a submission.template.json file, using the ${VAR_NAME} syntax ONLY for the variables that need to be replaced.
    • You must use the envsubst command and explicitly list the variables to be replaced to prevent unrelated $… symbols in the JSON (such as "$ref") from being accidentally expanded.
    • Example:
      bash
      envsubst '${USER_HPC_WORKSPACE} ${USER_OTHER_VAR}' < submission.template.json > submission.json
Validate & Submit

You MUST choose the command chain that matches the configuration and execute it in sequence. Keep the same choice for every later resume or synchronization run.

For a configuration without $ref, keep external references disabled:

bash
# Logic and Schema Validation
uvx --with dpdispatcher dargs check -f dpdispatcher.entrypoints.submit.submission_args submission.json
# Submit Job
uvx --from dpdispatcher dpdisp submit submission.json

For a configuration that uses $ref, pass --allow-ref to both validation and submission; omitting it makes a valid referenced configuration fail:

bash
# Logic and Schema Validation
uvx --with dpdispatcher dargs check --allow-ref -f dpdispatcher.entrypoints.submit.submission_args submission.json
# Submit Job
uvx --from dpdispatcher dpdisp submit --allow-ref submission.json
Reporting Standard

After execution finishes, you MUST output a structured report to the user with the following fixed elements:

  • Task Summary: Briefly describe the user's request (execution location, executed command, allocated resources).
  • Current State: Explicitly point out the status of the job (started / running / finished / failed).
  • Artifact Path: Explicitly point out the location of the output files (for example, when task_work_path is ., point out the specific paths of log and err).
  • Exception Guidance: If the job encounters an interruption or partial failure, provide the user with detailed issue information and execute according to the user's further instructions.

Long-Running Jobs

High-performance computing tasks usually take an extremely long time (from hours to weeks), and there is a long time gap between the submission command and the final result. This is not a one-off, instant Q&A process, and you must choose the appropriate disconnect-prevention execution mode based on the specific scenario:

Blocking Mode
  • Wrap in tmux: Run the standard dpdisp submit submission.json. The program will continuously hang and wait until the job is truly finished calculating on the cluster and the files are downloaded back before exiting. You must run it inside a tmux session to prevent any possible disconnection from killing the process.
Show full SKILL.md (619 more words)Show less
Non-blocking Mode
  • Use the --exit-on-submit flag: After successfully handing over the job to the scheduling system (e.g., Slurm), the program will immediately exit the terminal and return <exit_code>. It will not wait for execution to complete or download outputs.
  • State Definition: In this mode, you must strictly distinguish between the following two states for the user:
    • Successfully Submitted (Submitted): Just finished executing the command with the flag and returned 0. At this time, the job is only accepted by the backend, may be queuing, and output files are temporarily unavailable.
    • Fully Completed (Completed): After re-running the synchronization command later, the backend task finishes successfully, AND all required output files have been successfully retrieved to the local machine.
  • Idempotent Recovery Principle (Resuming Jobs): DPDispatcher has built-in state tracking and idempotency. It will automatically resume unfinished tasks and will not repeatedly execute completed ones.
    • Trigger Conditions: Used for state synchronization and file downloading in non-blocking mode; or when the job fails, times out, is unexpectedly interrupted, the user explicitly requests to "resume" or "retry", or your own SSH/network disconnects during monitoring.
    • Recovery Action: You do not need to modify submission.json or attempt to clean up the remote directory. You simply need to re-execute the exact same submission command (e.g., uvx --from dpdispatcher dpdisp submit submission.json --allow-ref) as is in the same directory.

Timeline Example (Non-blocking Mode):

  • [Day 1, 10:00] Submit job: dpdisp submit --exit-on-submit submis_task.json
  • [Day 1, 10:01] The command exits immediately and returns 0. At this time, it is only in the Successfully Submitted state. The Agent can exit the terminal to execute other tasks.
  • [Waiting Period] (A long queuing and calculation phase, potentially lasting for days)
  • [Day 3, 15:00] The Agent returns to the directory to check: triggers the idempotent recovery mechanism, re-runs dpdisp submit submis_task.json without the flag as is to synchronize the state and trigger file downloading. Only after the download is complete is it marked as Fully Completed.

Strict Guardrails

Before performing any operation, as an Agent, you MUST UNCONDITIONALLY obey the following security baselines:

  • Direct SSH Connections are Strictly Prohibited: You are absolutely not allowed to attempt connecting directly to the remote HPC using ssh, write custom Paramiko/Fabric Python scripts, or manually execute remote commands. All remote connections, file transfers, and job management MUST AND ONLY be safely handled by DPDispatcher by generating submission.json and calling the dpdisp submit tool.
  • Reading External Reference JSON Files is Strictly Prohibited: If the user provides a JSON file to supply certain information, you are ABSOLUTELY PROHIBITED from reading or printing the contents of that file. The file contains raw sensitive data, and reading it will cause confidential information to leak into the current conversation context.
  • Reading Configuration Files with Sensitive Data is Strictly Prohibited: After injecting environment variables via envsubst to generate the final submission.json, you are ABSOLUTELY PROHIBITED from reading or printing the contents of the file. The file contains raw sensitive data, and reading it will cause confidential information to leak into the current conversation context.

Example

User request: "Please run the simulation located in the task02 directory on my Slurm cluster. Load my username from $HPC_USER and the workspace path from $HPC_WORKDIR. We already have a resource_defaults.json in the parent workspace directory, please reference it and just add the debug queue."

The Agent discovers that the current directory structure is as follows:

text
<WORKSPACE>/
├── resource_defaults.json
├── ...
└── run_dir/
    ├── ...
    └── task02/
        ├── run_simulation.sh
        ├── ...
        └── data/
            ├── input.dat
            └── ...

The Agent decides to create the configuration file submis_task02.template.json within the run_dir/ directory (at the same level as the task02/ folder). The Agent has remembered the $ref pointing to the parent directory ../, the task_work_path explicitly targeting "task02", and forward_files remaining strictly relative to that task_work_path. Then it writes down:

json
{
  "work_base": ".",
  "machine": {
    "batch_type": "Slurm",
    "context_type": "SSHContext",
    "remote_profile": {
      "hostname": "<target-host>",
      "username": "${HPC_USER}",
      "port": 22
    },
    "remote_root": "${HPC_WORKDIR}/dpdisp_run"
  },
  "resources": {
    "$ref": "../resource_defaults.json",
    "queue_name": "debug",
    "group_size": 1
  },
  "task_list": [
    {
      "command": "bash run_simulation.sh",
      "task_work_path": "task02",
      "forward_files": [
        "run_simulation.sh",
        "data/input.dat"
      ],
      "backward_files": [
        "result.out",
        "log",
        "err"
      ]
    }
  ]
}

Then the Agent run the validation and submission commands from within the <WORKSPACE> directory:

bash
cd run_dir/
envsubst '${HPC_USER} ${HPC_WORKDIR}' < submis_task02.template.json > submis_task02.json
uvx --with dpdispatcher dargs check --allow-ref -f dpdispatcher.entrypoints.submit.submission_args submis_task02.json
tmux new-session -d -s dpdisp_task02 "uvx --from dpdispatcher dpdisp submit --allow-ref submis_task02.json"
tmux ls

© jinzhezenggroup, LGPL-3.0-or-later. 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 tools/dpdisp-submit of jinzhezenggroup/computational-chemistry-agent-skills.

Open the folder on GitHubat commit 5c19e75

Compare with similar skills

Dpdisp Submit 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.

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Laravel SpecialistJeffallan/claude-skills12k1 repos~2.1kAutomated safety check: PassMIT
Trigger.dev Realtimepapermark/papermark9.2k—~1.7kAutomated safety check: PassCustom licence
NubaseOtterMind/Nubase622—~2.2kAutomated safety check: NotesApache-2.0

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Categories

Questions about Dpdisp Submit

What does Dpdisp Submit do?

Run Shell commands as computational jobs, on local machines or HPC clusters, through Shell, Slurm, PBS, LSF, Bohrium, etc. Dpdisp Submit is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Run Shell commands as computational jobs, on local machines or HPC clusters, through Shell, Slurm, PBS, LSF, Bohrium, etc.

When should I use Dpdisp Submit?

Dpdisp Submit fits situations like: the user needs to submit batch jobs to a cluster; run commands on a remote server; execute tasks via job schedulers (Slurm; safely run long-term/background shell commands that require state tracking and auto-recovery.

How do I install Dpdisp Submit in Claude Code?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill dpdisp-submit -a claude-code`. Or copy the skill folder (tools/dpdisp-submit in jinzhezenggroup/computational-chemistry-agent-skills) into .claude/skills/dpdisp-submit in your project. Claude Code loads it when a task matches its description.

How do I install Dpdisp Submit in Codex?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill dpdisp-submit -a codex`. Or copy the skill folder (tools/dpdisp-submit in jinzhezenggroup/computational-chemistry-agent-skills) into .agents/skills/dpdisp-submit in your project. Codex loads it when a task matches its description.

Can I use Dpdisp Submit 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 jinzhezenggroup/computational-chemistry-agent-skills --skill dpdisp-submit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dpdisp-submit, .gemini/skills/dpdisp-submit, .github/skills/dpdisp-submit and .opencode/skills/dpdisp-submit in your project.

What does Dpdisp Submit need to run?

Going by SKILL.md and its folder, Dpdisp Submit needs the command-line tools its instructions call (uvx). Our summary lists: Python 3.

Does Dpdisp Submit access the network?

SKILL.md contains no URLs. Its commands use uvx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Dpdisp Submit 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 Dpdisp Submit use?

Dpdisp Submit is published under the LGPL-3.0-or-later licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dpdisp Submit use?

About 3.1k tokens (SKILL.md is roughly 12k 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 Dpdisp Submit?

Skills that share tags, products or a category with Dpdisp Submit: Trigger.dev Configuration (papermark/papermark, 9.2k stars), Foundatio (FoundatioFx/Foundatio, 2.1k stars), Laravel Specialist (Jeffallan/claude-skills, 12k stars) and Trigger.dev Realtime (papermark/papermark, 9.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dpdisp Submit?

jinzhezenggroup (a GitHub organization) maintains it in jinzhezenggroup/computational-chemistry-agent-skills, which has 148 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 9, 2026.

Source: jinzhezenggroup/computational-chemistry-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.