Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yushui2022/MathModel-Skill --skill paper-workflow-orchestratorgit clone --depth 1 https://github.com/yushui2022/MathModel-SkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yushui2022/mathmodel-skill/paper-workflow-orchestrator)<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/paper-workflow-orchestrator"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/paper-workflow-orchestrator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/paper-workflow-orchestrator"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/paper-workflow-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.01505 |
| Opus 5.5 | $0.00036 | $0.00602 |
| Sonnet 5.5 | $0.00018 | $0.00301 |
| Haiku 4.5 | $0.00009 | $0.00151 |
Grade A, and why
paper-workflow-orchestrator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MathModel Standard Orchestrator
Resolve <SKILL_ROOT> to the parent directory of this loaded skill folder, using its actual absolute path. Run commands from the contest project root, never from the installed skill directory. The placeholder is not a shell environment variable.
Use this skill as the only entry router for a complete competition-paper task. Standard targets strong models that can maintain a long evidence chain and execute tools reliably while keeping cost controlled. It does not use Pro multi-agent tournaments or approval checkpoints.
Start Or Resume
From the contest project root, run:
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/preflight_check.py"
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --status
Read paper_output/qa/workflow_guard_report.json and follow recommended_skill plus next_action. The current files and hashes override conversational memory.
Do not run downstream skills before their guard requirement passes. After a child skill finishes, return here and evaluate status again.
S0-S8
S0 Input Admission
preflight_check.py inventories problem_files/, hashes every input, checks runtime dependencies, prepares paper_output/, and rejects mixed MathModel editions. Required outputs:
paper_output/preflight_report.jsonpaper_output/input_manifest.jsonpaper_output/OUTPUT_LAYOUT.md
S1 Problem Analysis
Use problem-doc-model-selector to create paper_output/step1/problem_analysis.json. Every question, attachment, field, objective, constraint, ambiguity, and required output must be traceable.
S2 Model And Rubric Route
Use modeling-paper-rubric-and-model-selector. Produce:
paper_output/plan/model_route.jsonpaper_output/plan/rubric_alignment.jsonpaper_output/plan/scoring_strategy.md
Use authoritative-data-harvester only when public external data is necessary. Keep source identity and retrieval notes.
S3 Data And Visualization Plan
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 353 B
- MATHMODEL_EDITION.json 4.5 KB
- scripts/installation.py 21 KB runs code
- scripts/preflight_check.py 23 KB runs code
- scripts/prepare_output_layout.py 6.6 KB runs code
- scripts/quickstart_run.py 5.8 KB runs code
- scripts/run_all.py 1.5 KB runs code
- scripts/workflow_guard.py 32 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago Changed · +1 lines · +40 tokens per session 2d54670092b7
- 8d ago Changed · +2 lines e14655207acb
- 1mo ago Changed f749a183cf3d
- 1mo ago Changed · -161 lines · -14 tokens per session b8d3cbf8edd5
- 1mo ago First seen · 302 lines · 64 tokens per session scan A ebe38aa98042
paper-workflow-orchestrator is a skill published in the GitHub repository yushui2022/MathModel-Skill (456 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 1,505 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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