Mathmodel Skill
handsomeZR-netizen/mathmodel-skill
CUMCM 国赛、MCM/ICM 美赛与电工杯数学建模竞赛的端到端协作工作流。Use when a user explicitly works on one of these modeling contests or asks to run/review a modeling-competition paper from problem selection through modeling…
Evidence-driven mathematical modeling contest workflow for CUMCM, MCM/ICM, HiMCM, Huawei Cup, and similar events.
Install My Mathmodel Agent
$ npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install chengyike20110519-create/MathModelRun my-mathmodel-agent --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/chengyike20110519-create/MathModelRun.git skills-src && mkdir -p .claude/skills && cp -r skills-src/my-mathmodel-agent .claude/skills/my-mathmodel-agent && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "my-mathmodel-agent" agent skill from https://github.com/chengyike20110519-create/MathModelRun/tree/main/my-mathmodel-agent into .claude/skills/my-mathmodel-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "my-mathmodel-agent", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/chengyike20110519-create/MathModelRun/tree/main/my-mathmodel-agentType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install chengyike20110519-create/MathModelRun my-mathmodel-agent --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chengyike20110519-create/MathModelRun.git skills-src && mkdir -p .agents/skills && cp -r skills-src/my-mathmodel-agent .agents/skills/my-mathmodel-agent && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "my-mathmodel-agent" agent skill from https://github.com/chengyike20110519-create/MathModelRun/tree/main/my-mathmodel-agent into .agents/skills/my-mathmodel-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "my-mathmodel-agent", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install chengyike20110519-create/MathModelRun my-mathmodel-agent --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chengyike20110519-create/MathModelRun.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/my-mathmodel-agent .cursor/skills/my-mathmodel-agent && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "my-mathmodel-agent" agent skill from https://github.com/chengyike20110519-create/MathModelRun/tree/main/my-mathmodel-agent into .cursor/skills/my-mathmodel-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "my-mathmodel-agent", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/chengyike20110519-create/MathModelRun.git --path my-mathmodel-agent--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install chengyike20110519-create/MathModelRun my-mathmodel-agent --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chengyike20110519-create/MathModelRun.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/my-mathmodel-agent .gemini/skills/my-mathmodel-agent && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "my-mathmodel-agent" agent skill from https://github.com/chengyike20110519-create/MathModelRun/tree/main/my-mathmodel-agent into .gemini/skills/my-mathmodel-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "my-mathmodel-agent", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install chengyike20110519-create/MathModelRun my-mathmodel-agentInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/chengyike20110519-create/MathModelRun.git skills-src && mkdir -p .github/skills && cp -r skills-src/my-mathmodel-agent .github/skills/my-mathmodel-agent && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "my-mathmodel-agent" agent skill from https://github.com/chengyike20110519-create/MathModelRun/tree/main/my-mathmodel-agent into .github/skills/my-mathmodel-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "my-mathmodel-agent", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install chengyike20110519-create/MathModelRun my-mathmodel-agent --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chengyike20110519-create/MathModelRun.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/my-mathmodel-agent .opencode/skills/my-mathmodel-agent && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "my-mathmodel-agent" agent skill from https://github.com/chengyike20110519-create/MathModelRun/tree/main/my-mathmodel-agent into .opencode/skills/my-mathmodel-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "my-mathmodel-agent", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
my-mathmodel-agent- GitHub stars
- 128
- Token cost
- ~2.3k tokens
- SKILL.md length
- 946 words
- Files
- 10 (incl. scripts, references)
- Skills in repo
- 2
- Repo updated
- First seen
- Licence
- MIT
At a glance
Evidence-driven mathematical modeling contest workflow for CUMCM, MCM/ICM, HiMCM, Huawei Cup, and similar events.
- Works in 6 steps: Read state.json, project_manifest.json,… → Read the current stage artifact and its… → Run python3… → …
- Decompose a problem
- SKILL.md covers Use this skill when, Choose the operating mode, Startup ritual and Stage machine, plus 8 more sections
- Runs Python scripts from its folder; calls python3
What it does
My Mathmodel Agent is an agent skill from chengyike20110519-create/MathModelRun. Evidence-driven mathematical modeling contest workflow for CUMCM, MCM/ICM, HiMCM, Huawei Cup, and similar events. Use it to decompose a problem, choose and validate model routes, run reproducible Python/AMPL experiments, freeze results, write LaTeX or Typst papers, render them, and audit every claim. Not for casual homework, simple calculations, or one-off coding questions.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/artifact-contracts.md` and `references/evaluation-rubric.md`).
It sits in Documents & Office, covering LaTeX. It works with Python and LaTeX. The repository describes itself as: Evidence-driven mathematical modeling workflow for CUMCM, MCM/ICM, and HiMCM: from problem files to reproducible experiments, audited papers, and defensible results. The licence is MIT.
When your agent uses it
- Decompose a problem
- Choose and validate model routes
- Run reproducible Python/AMPL experiments
- Audit every claim
Example prompts
- “/my-mathmodel-agent”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Read state.json, project_manifest.json, and PROGRESS.md.
- Read the current stage artifact and its upstream evidence.
- Run python3 my-mathmodel-agent/scripts/status.py .
- Identify the first gate whose value is false.
- Work only on that gate, or record why a fallback is required.
- At the end, update the state, progress log, decision log, and evidence map.
What it can do on your machine
Read from SKILL.md and the folder at commit b5cb535. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 5 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
My Mathmodel Agent loads about 2.3k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 946 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from chengyike20110519-create/MathModelRun at commit b5cb535, republished under its MIT licence (© chengyike20110519-create). 946 words, ~2,335 tokens.
Download SKILL.mdSave it as
.claude/skills/my-mathmodel-agent/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.- name
- my-mathmodel-agent
- description
- Evidence-driven mathematical modeling contest workflow for CUMCM, MCM/ICM, HiMCM, Huawei Cup, and similar events. Use it to decompose a problem, choose and validate model routes, run reproducible Python/AMPL experiments, freeze results, write LaTeX or Typst papers, render them, and audit every claim. Not for casual homework, simple calculations, or one-off coding questions.
- metadata.short-description
- Run a math-modeling contest from problem files to audited paper
MathModel Run
Math modeling is not only a modeling problem. Under contest time pressure, it is a decision, evidence, and delivery problem:
- A model looks sophisticated but has no validation path.
- The code runs, but the paper quotes a number from an old run.
- Every subproblem "passes" locally, yet the final PDF has broken figures.
- A reviewer asks why the primary model beats the baseline, and there is no reproducible answer.
This skill turns the contest into a stateful, evidence-backed pipeline. It
borrows the useful harness ideas from MAGA2010/hackathon-run: one feature at
a time, default-FAIL contracts, machine-checkable evidence, fresh-context
review, bounded fallback, and an append-only decision log.
Use this skill when
Use it for a contest workspace that contains problem files and needs a
reproducible path to a defensible paper. Invoke it with $my-mathmodel-agent.
Do not use it for:
- a single formula or short homework answer;
- a coding request that is unrelated to a contest project;
- changing a paper's wording when no evidence or project state is involved.
Choose the operating mode
| Mode | Use when | First action |
|---|---|---|
SETUP | New contest folder | Run init_project.py, then verify inputs |
RUN | Existing project needs to advance | Read state.json, find the first false gate |
REVIEW | A stage claims to be complete | Independently reproduce the evidence |
RECOVER | A model, run, number, or PDF failed | Return to the earliest invalid gate |
DELIVER | Paper is rendered | Run the final acceptance audit |
Never skip from a plan to a polished paper. The only valid path is through validated methods, reproducible experiments, frozen numbers, and audit.
Startup ritual
Before acting on a project:
- Read
state.json,project_manifest.json, andPROGRESS.md. - Read the current stage artifact and its upstream evidence.
- Run
python3 my-mathmodel-agent/scripts/status.py <project>. - Identify the first gate whose value is
false. - Work only on that gate, or record why a fallback is required.
- At the end, update the state, progress log, decision log, and evidence map.
If the project directory is new, scaffold it with:
python3 my-mathmodel-agent/scripts/init_project.py <project-name>If the environment is uncertain, run:
python3 my-mathmodel-agent/scripts/doctor.pyStage machine
S0 PREFLIGHT
input_manifest
|
v
S1 ANALYZE
problem_analysis.json
|
v
S2 ROUTE
model_route.json
|
v
S3 DATA_PLAN
data_plan.json + visualization_plan.json
|
v
S4 METHOD_POC
method_validation.json + runnable PoC
|
v
S5 EXPERIMENT
run_manifest.json + metrics + logs + figures
|
v
S6 FREEZE
frozen_numbers.json
|
v
S7 WRITE
paper + evidence_map.json
|
v
S8 RENDER
PDF + render_log.json
|
v
S9 AUDIT
audit/final_report.md
|
v
READYThe detailed rules for each stage are in references/workflow-contract.md. Read the stage section you are executing, not the entire document by default.
Default-FAIL contract
Every subproblem and model route starts with passes: false.
A gate changes to true only when its acceptance criteria have
machine-checkable evidence. Evidence is one of:
command: the exact command and exit result;test: an automated check with a pass/fail result;data: a source file, row count, hash, or schema check;figure: a generated figure plus the script that produced it;log: a run log that records parameters, seed, environment, and output;manual: visual inspection or a human decision, with the inspected path.
The generator may propose evidence. The independent reviewer must reproduce it. A claim such as "the model works" or "the fit is good" is not evidence.
Role split
Use separate contexts whenever the environment supports them:
| Role | Owns | Must not do |
|---|---|---|
| Orchestrator | state, gates, handoff, blockers | mark its own work passing |
| Analyst / modeler | problem cards and model routes | edit final paper numbers |
| Coder | reproducible experiments and logs | declare a result frozen |
| Writer | paper and evidence map | use numbers outside frozen_numbers.json |
| Reviewer | independent reproduction and audit | "fix" the artifact being reviewed |
For Codex, stay in the current context but explicitly switch roles. For
Claude Code, the mirrored agents live under .claude/agents/.
Show full SKILL.md (392 more words)Show less
Required artifacts
| Artifact | Purpose | Default |
|---|---|---|
input_manifest.json | Problem-file inventory and hashes | incomplete |
planning/problem_analysis.json | One default-FAIL task card per subproblem | all gates false |
methods/model_route.json | Baseline, primary, fallback, validation plan | all gates false |
data_cleaned/data_plan.json | Fields, units, cleaning, leakage checks | incomplete |
figures/visualization_plan.json | Figure purpose, source, script, destination | incomplete |
methods/method_validation.json | Smallest runnable PoC and its evidence | pending |
results/run_manifest.json | Reproducible run records | pending |
frozen_numbers.json | Final numbers with provenance | no values |
paper/evidence_map.json | Claim -> number/figure/run mapping | incomplete |
paper/render_log.json | Compilation and page inspection | incomplete |
audit/gate_evidence.json | Evidence behind every state gate | empty |
audit/final_report.md | Final independent review | absent |
planning/decision_log.jsonl | KEEP/CUT/DEFER/PIVOT decisions | empty |
PROGRESS.md | Durable handoff log across fresh contexts | initialized |
Machine-readable contract details are in references/artifact-contracts.md.
Evidence gates
The state gates are intentionally conservative:
input_snapshot
problem_decomposed
model_route_selected
data_plan_ready
method_validated
experiments_reproduced
results_frozen
paper_written
pdf_verified
audit_passedREADY requires all ten gates to be true and audit/final_report.md to
contain zero hard errors. A successful solver exit is not sufficient. A
compiled PDF is not sufficient. A plausible explanation is not sufficient.
Recovery rules
Return to the earliest invalid gate:
| Failure | Return to |
|---|---|
| Question misunderstood or data scope wrong | S1 |
| Primary model cannot be justified | S2 |
| Data leakage, missing fields, bad units | S3 |
| PoC does not run or baseline comparison is invalid | S4 |
| Code, solver status, metrics, or seed unreliable | S5 |
| Paper number lacks provenance or run changed | S6 |
| Claim, figure, or citation does not match evidence | S7 |
| Compile error, overflow, missing figure, bad encoding | S8 |
| Hard error in audit | earliest failed gate |
When a primary route fails, use the recorded fallback. Do not silently replace
the model or rewrite acceptance criteria to make it pass. Record the decision
in planning/decision_log.jsonl.
Commands
# create a contest workspace
python3 my-mathmodel-agent/scripts/init_project.py 2026-cumcm-a
# show current stage, false gates, and next action
python3 my-mathmodel-agent/scripts/status.py 2026-cumcm-a
# inspect local tooling
python3 my-mathmodel-agent/scripts/doctor.py
# freeze a verified result file with provenance
python3 my-mathmodel-agent/scripts/freeze_numbers.py \
results/verified_results.json --output 2026-cumcm-a/frozen_numbers.json
# independent final audit
python3 my-mathmodel-agent/scripts/audit_workspace.py 2026-cumcm-aThe start page and human-facing map are in ../../index.html. They explain
the workflow without replacing the machine contracts above.
Completion behavior
Report:
- current stage and gate status;
- artifacts created or changed;
- evidence reproduced;
- unresolved risks or blockers;
- the exact next entry point.
Do not say "done", "paper finished", or "results final" unless the relevant gates are true and the evidence exists at the paths named above.
Reference routing
- Read references/workflow-contract.md when executing or reviewing a stage.
- Read references/artifact-contracts.md when creating, editing, or validating state files.
- Read references/evaluation-rubric.md when acting as the independent reviewer or preparing the final submission.
Keep the project's original files in place when updating them. Replace the
current version at the same path, remove obsolete content, and do not leave
older v1, v2, or backup copies unless the user explicitly requests them.
© chengyike20110519-create, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 9 other files (scripts, references) in my-mathmodel-agent of chengyike20110519-create/MathModelRun.
- SKILL.md
- agents/openai.yaml
- references/artifact-contracts.md
- references/evaluation-rubric.md
- references/workflow-contract.md
- scripts/audit_workspace.py
- scripts/doctor.py
- scripts/freeze_numbers.py
- scripts/init_project.py
- scripts/status.py
Open the folder on GitHubat commit b5cb535
Compare with similar skills
My Mathmodel Agent next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| My Mathmodel Agent this skillchengyike20110519-create/MathModelRun | 128 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Mathmodel SkillhandsomeZR-netizen/mathmodel-skill | 292 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Literature Surveyai4s-research/ai4s-skills | 237 | 2 repos | ~2k | Automated safety check: Pass | MIT | |
| MineruNebutra/MinerU-Skill | 123 | — | ~504 | Automated safety check: Pass | MIT | |
| Cumcm Live Workflowhaoxilin/cumcm-live-workflow-skill | 181 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Academic LaTeX Formatterlingzhi227/agent-research-skills | 390 | — | ~603 | Automated safety check: Pass | None |
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Related
Categories
Questions about My Mathmodel Agent
What does My Mathmodel Agent do?
Evidence-driven mathematical modeling contest workflow for CUMCM, MCM/ICM, HiMCM, Huawei Cup, and similar events. My Mathmodel Agent is an agent skill from chengyike20110519-create/MathModelRun. Evidence-driven mathematical modeling contest workflow for CUMCM, MCM/ICM, HiMCM, Huawei Cup, and similar events.
When should I use My Mathmodel Agent?
My Mathmodel Agent fits situations like: decompose a problem; choose and validate model routes; run reproducible Python/AMPL experiments; audit every claim.
How do I install My Mathmodel Agent in Claude Code?
Run `npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a claude-code`. Or copy the skill folder (my-mathmodel-agent in chengyike20110519-create/MathModelRun) into .claude/skills/my-mathmodel-agent in your project. Claude Code loads it when a task matches its description.
How do I install My Mathmodel Agent in Codex?
Run `npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a codex`. Or copy the skill folder (my-mathmodel-agent in chengyike20110519-create/MathModelRun) into .agents/skills/my-mathmodel-agent in your project. Codex loads it when a task matches its description.
Can I use My Mathmodel Agent in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add chengyike20110519-create/MathModelRun --skill my-mathmodel-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/my-mathmodel-agent, .gemini/skills/my-mathmodel-agent, .github/skills/my-mathmodel-agent and .opencode/skills/my-mathmodel-agent in your project.
What does My Mathmodel Agent need to run?
Going by SKILL.md and its folder, My Mathmodel Agent needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does My Mathmodel Agent access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is My Mathmodel Agent safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does My Mathmodel Agent use?
My Mathmodel Agent is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does My Mathmodel Agent use?
About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.1k tokens, read only when the agent opens those files.
What are the alternatives to My Mathmodel Agent?
Skills that share tags, products or a category with My Mathmodel Agent: Mathmodel Skill (handsomeZR-netizen/mathmodel-skill, 292 stars), Literature Survey (ai4s-research/ai4s-skills, 237 stars), Mineru (Nebutra/MinerU-Skill, 123 stars) and Cumcm Live Workflow (haoxilin/cumcm-live-workflow-skill, 181 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains My Mathmodel Agent?
chengyike20110519-create (a GitHub user) maintains it in chengyike20110519-create/MathModelRun, which has 128 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 6, 2026.
Source: chengyike20110519-create/MathModelRun on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.