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Synthetic stub skill for forward-compatibility testing (T-SF-9 / C4). Demonstrates that a new BSM model can be added without modifying _shared/ or tools/. Do not invoke in production.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Numerically evaluate a FormCalc-reduced one-loop amplitude with LoopTools 2.16 (Passarino-Veltman integrals via the Wolfram/LoopTools MathLink) and emit a scattering/v1 JSON (σ_SI, σ_SD) for direct detection. Unblocks the 2HDM+a loop-only DD path. Self-heals via _shared/installs/looptools preflight.
Reduce a FeynArts amplitude list with FormCalc 9.10. Produces amp_reduced.m and amp_reduced.meta.json sidecar conforming to amp_reduced.meta/v1. Self-heals via _shared/installs/formcalc preflight.
Generate Feynman diagrams and amplitudes using FeynArts 3.11. Supports built-in models (SM, SMQCD, THDM, MSSM) and SARAH-generated models. Outputs FeynAmpList.m, diagrams.pdf, topologies.json, and metadata sidecar.
Compute direct-detection likelihoods and 90%-CL exclusion verdicts using DDCalc 2.2.0. Leaf consumer of scattering/v1 JSON from /micromegas or /formcalc.
MadDM — dark matter relic density, direct detection cross-sections, indirect detection rates, parameter scans with experimental limit comparison
Run HiggsBounds-5 + HiggsSignals-2 constraint checks on a model SLHA file. Computes hb_allowed (per-channel AND), hs_consistent (Δχ² < 6.18), p-values, and per-channel CSV. Supports single-point and scan-directory modes.
| name | _test_model_x |
| description | Synthetic stub skill for forward-compatibility testing (T-SF-9 / C4). Demonstrates that a new BSM model can be added without modifying _shared/ or tools/. Do not invoke in production. |
This is a synthetic stub used to verify the C4 forward-compatibility protocol.
It demonstrates the 6-step recipe for adding a new BSM model. See CONVENTIONS.md.
model: model-x (synthetic)benchmarks/synthetic-1/expectations.jsonWhen a real run completes, write summary.json conforming to the per-skill schema:
{
"schema_version": "1",
"model": "model-x",
"run_at": "<ISO-8601-UTC>",
"ran": ["relic"],
"skipped_constraints": [],
"artifacts_dir": "./demo_output/model-x",
"headline": "model-x relic density run complete",
"benchmark_id": "synthetic-1",
"provenance_ref": "provenance.json"
}
Write provenance.json before summary.json (atomic-write invariant).
Cite benchmarks/synthetic-1/expectations.json in any plan gate comparing omega_h2.