method-qmc
method-qmc is a Physics, mathematics & simulation skill for AI agents, published by QuantumBFS in the quantum.harness repository on GitHub. Skillspace rates method-qmc C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Use when a quantum Monte Carlo (QMC) reproduction needs method-level route and tool selection — stochastic series expansion (SSE) for sign-free spin/boson finite-temperature targets, constrained-path / phaseless auxiliary-field QMC (CPMC/AFQMC) for fermionic Hubbard ground states, or fixed-node diffusion Monte Carlo (DMC) for plane-wave solids using Quantum ESPRESSO orbitals. Triggers include "QMC", "SSE", "stochastic series expansion", "finite-temperature susceptibility", "sign problem", "AFQMC", "CPMC", "constrained path", "Hubbard ground state by Monte Carlo", "DMC", "diffusion Monte Carlo", "QMCPACK", "Slater-Jastrow", "twist averaging", "fixed-node".
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 35
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.