Skip to content
cccskills
FreePublished on GitHub

matlab-model-code-generator

Generate and run minimal reproducible MATLAB or Beita Tianyuan compatible code for the human-approved main method and usable baseline, with compact experiment artifacts and a canonical run summary.

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md2.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Preconditions

  • G2.5 human method choice is recorded.
  • code/matlab/Qx/qx_code_plan.md exists.
  • Required cleaned data and profile exist.
  • The plan targets MATLAB or 北太天元.

Legacy artifacts may be read during migration but do not override the human decision.

Workflow

  1. Read the code plan, decision ledger, method card, probe conditions, and data profile.
  2. Confirm scope: approved main plus usable baseline. Implement a fallback only after activation.
  3. Generate conservative .m files under code/matlab/Qx/.
  4. Prefer basic matrix/table operations and avoid optional toolboxes unless the plan approves them.
  5. Save tables, metrics, useful figures, and run_summary.json under results/Qx/experiments/roundN/.
  6. Evaluate output-degeneracy and fallback-trigger metrics required by the plan.
  7. Use diary or another full log only for a failure or reproducibility warning.
  8. Run in the available compatible runtime. If unavailable, report the unexecuted state explicitly.
  9. Hand off to code-reviewer.

Script Layout

code/matlab/Qx/
├── qx_code_plan.md
├── qx_baseline.m
├── qx_main.m
└── run_all.m        % only when useful

Do not create scripts for unapproved candidates or a duplicate README.

Compatibility Rules

  • Prefer readtable, readmatrix, writetable, writematrix, save, load, and fullfile.
  • Use rng(2026) or the recorded seed.
  • Use jsonencode when supported; otherwise write the required JSON fields deterministically.
  • Avoid Live Scripts, App Designer, GUI code, Simulink, and toolbox-only functions unless explicitly approved.
  • Note any 北太天元 compatibility risk in the run summary.

Run Summary

Follow the model-code-analyzer contract, including approved decision ID, roles, paths, metrics, output-degeneracy evidence, fallback state, timing, seed, environment, warnings, and errors.

Rules

  • Do not change the selected mathematical method.
  • Do not access or overwrite raw data.
  • Do not fabricate successful execution when MATLAB/北太天元 is unavailable.
  • Keep only evidence-bearing intermediate outputs.
  • Separate Type 1 diagnostics from paper figures.

Verification

  • Main and baseline are directly comparable and both executed when a runtime is available.
  • Fallback code exists only when activated.
  • Formal outputs and run summary exist.
  • Compatibility, seed, inputs, warnings, and errors are recorded.
  • Required concentration/degeneracy checks are saved.
  • Next handoff is code-reviewer.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.

The English summary is being prepared. Showing the original description.

zhnnky329/MathModeling-skills1.1KUpdated Sep 24, 2026

Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.

The English summary is being prepared. Showing the original description.

zhnnky329/MathModeling-skills1.1KUpdated Sep 24, 2026

Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.

The English summary is being prepared. Showing the original description.

zhnnky329/MathModeling-skills1.1KUpdated Sep 24, 2026

Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.

The English summary is being prepared. Showing the original description.

zhnnky329/MathModeling-skills1.1KUpdated Sep 24, 2026

Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.

The English summary is being prepared. Showing the original description.

zhnnky329/MathModeling-skills1.1KUpdated Sep 24, 2026

Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.

The English summary is being prepared. Showing the original description.

zhnnky329/MathModeling-skills1.1KUpdated Sep 24, 2026

See all skills by zhnnky329

Report a problem with this skill