# Ospool

> |

- **Type:** Skill
- **Install:** `agentstack add skill-comses-skills-ospool`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [comses](https://agentstack.voostack.com/s/comses)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [comses](https://github.com/comses)
- **Source:** https://github.com/comses/skills/tree/main/skills/ospool

## Install

```sh
agentstack add skill-comses-skills-ospool
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# OSPool HTCondor Scaffolder Skill

## When to Use This Skill

Use this skill when:

- You have a working computational model and want to run it on the Open Science Grid (OSPool)
- You need to execute a parameter sweep or sensitivity analysis across many parameter combinations
- You want to submit batch jobs that leverage distributed HTCondor scheduling
- You need a dry-run mode to validate your job configuration before submission
- You are setting up checkpoint/restart for long-running simulations

## Key Inputs

This skill works best with:

- **Model executable or script** (Python .py, R script, compiled binary, or bash wrapper)
- **Parameter ranges** (variables to sweep over, with min/max or discrete values)
- **Input files** (data files, configuration, or dependencies your model needs)
- **Runtime estimate** (approximate wall-clock time per job in minutes)
- **Resource requirements** (CPU cores, memory, scratch disk)
- **Success criteria** (what constitutes a successful run)

## Step-by-Step Instructions

### 1. Prepare Your Model for OSPool

Before scaffolding, ensure:

- **Model is self-contained:** All code, dependencies, and input files can be packaged or referenced via HTTP
- **Single entry point:** Model can be invoked with a simple command: `python model.py --param1 value1 --param2 value2`
- **Output to stdout/file:** Model writes results to a file (not database) so outputs can be staged back from the worker node
- **No GUI/graphics:** HTCondor jobs are headless; remove any display dependencies
- **Reproducible with seed:** If your model uses randomness, accept a seed parameter: `--seed 42`

### 2. Define Parameter Space

Specify parameters to sweep:

```yaml
# Example sweep configuration (YAML)
sweep_type: factorial # or: one-at-a-time, latin-hypercube
parameters:
  population_size: [10, 50, 100]
  patch_count: [5, 10, 20]
  mutation_rate: [0.01, 0.05, 0.1]
replicas: 3 # replicate runs per parameter combination
```

The skill will generate all combinations (or use a design-of-experiments strategy).

### 3. Generate HTCondor Submit Files

The skill creates:

- **Main submit file** (.submit): Job description, input/output, resource requests
- **DAG file** (optional): Coordinate dependencies among jobs (e.g., run analysis after all simulations complete)
- **Parameter sweep file** (CSV or JSON): All parameter combinations to be executed
- **Wrapper script** (bash): Handles environment setup, input staging, and output staging

### 4. Dry-Run Validation

Before submitting to OSPool:

```bash
python scripts/validate_htcondor.py my_job.submit
# Checks for:
# - Valid HTCondor syntax
# - Executable is present or accessible
# - Input files are staged
# - Output directory is writable
# - Resource requests are reasonable (not too large/small)
```

Address any validation errors before proceeding.

### 5. Submit to OSPool

Once validated:

```bash
condor_submit my_job.submit
# Submits N jobs to OSPool
# Track status: condor_q
# Monitor: htop (if interactive) or check submission log
```

## ⚠️ Gotchas

- **Path dependencies:** HTCondor workers run in a sandbox directory. Use relative paths or stage files explicitly. Absolute paths like `/home/user/...` will break on worker nodes.
- **Environment differences:** Worker nodes may have different OS versions, libraries, or Python versions. Container/Singularity is recommended for complex dependencies.
- **Data staging:** Large input files should be staged via HTTP or OSPool's data cache, not embedded in submit files.
- **Output size:** Limit per-job output size. HTCondor has file size caps. If outputs are large, write directly to OSPool storage or compress before staging back.
- **Long-running jobs:** HTCondor has wall-clock time limits (~24 hours typical). If your model runs longer, implement checkpointing or break into smaller jobs.
- **Random seed conflicts:** If multiple replicas use the same random seed, they'll produce identical results. The skill will assign unique seeds automatically if you set `replicas > 1`.

## Templates & Resources

- **HTCondor Basics:** See `references/OSPOOL-QUICKSTART.md` for OSPool setup and account creation
- **HTCondor vs Slurm:** See `references/CONDOR-VS-SLURM.md` for environment comparison
- **Validation script:** Use `scripts/validate_htcondor.py` to check your submit configuration
- **Submit file template:** See `assets/htcondor-template.submit`
- **Parameter sweep examples:** See `examples/parameter-sweep-config.yaml`
- **DAG examples:** See `examples/simple-dag.dag` for job coordination

## Example

**Input:** Python model `run_sim.py` with parameters, parameter sweep config

**Output:**

1. **HTCondor submit file** (`sim_sweep.submit`):

   ```
   universe = vanilla
   executable = scripts/run_wrapper.sh
   arguments = run_sim.py --population $(population) --patches $(patches)
   input = run_sim.py, data.csv
   output = results_$(ClusterId)_$(ProcId).csv
   error = err_$(ClusterId)_$(ProcId).log
   log = job_$(ClusterId)_$(ProcId).log
   request_cpus = 1
   request_memory = 512MB
   request_disk = 1GB
   queue 30  # 30 jobs (3 population × 2 patches × 5 replicates)
   ```

2. **Parameter sweep file** (swept combinations):

   ```csv
   population,patches,seed
   10,5,1001
   10,5,1002
   10,10,1001
   ...
   100,20,1005
   ```

3. **Validation output:**
   ```
   ✓ Submit file syntax is valid
   ✓ Executable found: scripts/run_wrapper.sh
   ✓ Input files present: run_sim.py, data.csv
   ✓ Resource requests reasonable (1 CPU, 512MB RAM)
   ✓ Ready to submit: 30 total jobs
   ```

---

## Quick Reference

| Task                     | Command/Reference                          |
| ------------------------ | ------------------------------------------ |
| Validate HTCondor config | `python scripts/validate_htcondor.py`      |
| OSPool quickstart        | See `references/OSPOOL-QUICKSTART.md`      |
| Condor vs Slurm          | See `references/CONDOR-VS-SLURM.md`        |
| Parameter sweep template | See `examples/parameter-sweep-config.yaml` |

---

For community feedback or issues, see the [COMSES Skills](https://github.com/comses-network/skills) repository.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [comses](https://github.com/comses)
- **Source:** [comses/skills](https://github.com/comses/skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-comses-skills-ospool
- Seller: https://agentstack.voostack.com/s/comses
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.