Automated summary
Extract physical observables with error estimates from completed DQMC simulations.
Strengths
- Directly computes mean and standard error from binned data.
- Supports a wide range of observables (density, double occupancy, spin correlations, structure factors).
- Can handle multiple simulation directories and aggregate results.
- Validates sign problem severity by checking error on the sign.
Limitations
- Requires pre-existing binned HDF5 files from a completed DQMC simulation.
- Does not perform further statistical analysis beyond binning.
- Some observables (e.g., susceptibilities) need a specific simulation setting (period_uneqlt > 0) to be available.
Use when you need to quickly extract and print or process measured quantities from a DQMC simulation with proper error bars.
Do not use for raw data processing before binning, or for simulations that have not been completed.
Security analysis
SafeThe skill only performs local file reading and statistical analysis on simulation data. There are no network calls, shell commands, destructive operations, or data exfiltration risks.
No concerns found
How this analysis is madeExamples
I have a DQMC simulation output in 'data/run/', can you extract the fermion sign and density with error bars?Collect all observables from multiple subdirectories under 'simulations/' and compute the average double occupancy with error bars.Compute the magnetic moment squared from the spin correlator for the run in 'data/run/' using the zzr observable.name: dqmc-analyze description: Extract physical observables with error estimates from completed DQMC simulations. Use when computing density, double occupancy, spin correlations, structure factors, or any measured quantity from simulation data.
Analyze Results
Inputs
- Directory containing
bin_*.h5files (completed simulations) - Observable names (see table below)
Outputs
- Dictionary with parameters and
(mean, stderr)tuples for each observable
Procedure
Basic analysis:
from dqmc_util import analyze_hub
data = analyze_hub.get("data/run/", "sign", "den", "zzr")
print(f"sign = {data['sign'][0]:.4f} +/- {data['sign'][1]:.4f}")
print(f"density = {data['den'][0]:.4f} +/- {data['den'][1]:.4f}")
Available observables:
| Name | Description | Requires |
|------|-------------|----------|
| sign | Fermion sign | - |
| den | Density <n> | - |
| docc | Double occupancy <n_up n_down> | - |
| gr, gk | Green's function (real/k-space) | - |
| nnr, nnq | Density correlator / structure factor | - |
| zzr, zzq | Spin-z correlator / structure factor | - |
| xxr | Spin-x correlator | - |
| swq0 | S-wave pair structure factor | - |
| nnrw0, zzrw0 | Zero-freq susceptibilities | period_uneqlt > 0 |
| dwq0t | D-wave pair susceptibility | period_uneqlt > 0 |
Collect from multiple directories:
import os
def collect_results(base_dir, observables):
results = []
for subdir in sorted(os.listdir(base_dir)):
path = os.path.join(base_dir, subdir)
if os.path.isdir(path):
try:
results.append(analyze_hub.get(path + "/", *observables))
except Exception as e:
print(f"Skipping {path}: {e}")
return results
Compute derived quantities:
# Magnetic moment squared from spin correlator
path = "data/run/"
data = analyze_hub.get(path, "zzr")
mz2 = 4 * data["zzr"][0][0, 0] # [0] = mean, shape (Ny, Nx)
mz2_err = 4 * data["zzr"][1][0, 0] # [1] = stderr
Validation
- [ ] Errorbar on sign is significantly less than mean. Otherwise, sign problem is too severe.
- [ ] Errorbars on observable are reasonable (not >> mean)
Failure Modes
| Symptom | Cause | Recovery |
|---------|-------|----------|
| KeyError for observable | Observable not computed | Check period_uneqlt setting |
| "No files found" | Wrong path or no bin_*.h5 | Verify directory structure |
| Large error bars | Insufficient statistics | Run more sweeps or bins |
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/dqmc-results-analysis-mm5t89dt/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/dqmc-results-analysis-mm5t89dt' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/dqmc-results-analysis-mm5t89dt/download' --output '.skill-downloads/dqmc-results-analysis-mm5t89dt/SKILL.md.part' && mv '.skill-downloads/dqmc-results-analysis-mm5t89dt/SKILL.md.part' '.skill-downloads/dqmc-results-analysis-mm5t89dt/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/dqmc-results-analysis-mm5t89dt/SKILL.md' && test -s '.skill-downloads/dqmc-results-analysis-mm5t89dt/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/dqmc-analyze' && cp -R '.skill-downloads/dqmc-results-analysis-mm5t89dt' '.claude/skills/dqmc-analyze'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/dqmc-results-analysis-mm5t89dt'openskills read 'dqmc-results-analysis-mm5t89dt'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/dqmc-results-analysis-mm5t89dt/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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