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 woodfishhhh/EZ_math_model --skill ez-math-modelgit clone --depth 1 https://github.com/woodfishhhh/EZ_math_modelWrote 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/woodfishhhh/ez_math_model/ez-math-model)<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/ez-math-model"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/ez-math-model.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.03637 |
| Opus 5 | $0.00024 | $0.01818 |
| Sonnet 5 | $0.00010 | $0.00727 |
| Haiku 4.5 | $0.00005 | $0.00364 |
Grade A, and why
ez-math-model 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 7d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ez-math-model
数学建模一站式 skill。把题目交给我，我会自动走完七个阶段：setup → 题目解析 →
建模方案 → 代码求解 → 论文撰写 → 质量审查 → 标准打包交付。首次使用必须完成 setup
提问；完成后写入 external/tools/setup_state.json。未经用户确认，只能使用本次临时默认，
不得写永久配置。
一句话主流程
收到题目 → 识别项目总文件夹 → 检查
external/tools/setup_state.json→ 创建用户输入/runtime/output标准目录 → 判定formal/demo/blocked→ 识别赛事/年份/题号 → 选择模型 → 写脚本并执行 → 出图并过 chart gate → 写论文 → 跑对象级质量门 → staging 导出与审查 → 原子同步到output/并打包output.zip。
触发后的第一动作
先读以下七份文件确认本次运行的契约：
pipeline/00-environment-setup.md— 验环境（Python、字体、工作目录、上游缓存状态）references/project-root-protocol.md— 项目总文件夹与用户输入/runtime/outputreferences/setup-policy.md— setup 硬门与临时默认边界references/run-mode-protocol.md—formal/demo/blocked判定references/workdir-protocol.md— runtime 产物结构与命名references/chart-quality-gate.md— 图表有效性门references/artifact-manifest.md— 产物 manifest 契约
formal 交付不得只检查“文件是否存在”。质量门必须覆盖图表语义、公式语法、 表格对象、优秀论文格式抽取、正文工程痕迹、DOCX/PDF 对象级导出和 zip/manifest 一致性。
然后立刻执行 setup gate：
- 读取
external/tools/setup_state.json。 - 若文件不存在、JSON 无法解析、
setup_completed不是true，或用户说"重新配置工具 / 重置 setup / 重新 setup"，必须先运行 pipeline 00 的强制提问，禁止进入 pipeline 01、建模、写代码或写论文。 - setup 提问完成后，必须写入
external/tools/setup_state.json，再继续本次任务。 - 若
setup_completed: true，默认跳过交互式 setup，只做本次任务所需的轻量环境检查 和工作目录创建。
不要用"默认不追问"绕过首次 setup；首次 setup 是硬门禁。若用户要求继续但无法
完成交互式 setup，只能写 runtime/{task_id}/setup_assumptions.json，并把
setup_status 标为 temporary_default，最终质量等级最高为 provisional_pass。
Pipeline 索引
| # | 文件 | 入口条件 | 关键产出 |
|---|---|---|---|
| 00 | pipeline/00-environment-setup.md |
用户给出题目或附件；setup gate 未完成时必须先问 | setup 状态明确、runtime/ 创建完成、env-check 通过 |
| 01 | pipeline/01-problem-intake.md |
env-check 通过 | runtime/.../problem.md、intake.json、attachments/、run_state.json |
| 02 | pipeline/02-modeling-plan.md |
intake 完成 | modeling_plan.md、（首次询问 zhanwen 拉取） |
| 03 | pipeline/03-coding-solve.md |
modeling_plan 落盘 | src/*.py、results/*、figures/*.png |
| 04 | pipeline/04-paper-writing.md |
coding 完成 | paper.md |
| 05 | pipeline/05-quality-audit.md |
paper.md 落盘 | quality_report.json、quality_report.md（含证据与未通过项） |
| 06 | pipeline/06-packaging-output.md |
质量门评估完成 | export_report.json、export_audit.json、output/paper/* 四格式、output/manifest.json、output.zip |
What ships with it
60 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.
- .env.example 1.2 KB
- external/paper-orchestra/CHANGELOG.md 2.7 KB
- external/paper-orchestra/CITATION.cff 769 B
- external/paper-orchestra/LICENSE 1.6 KB
- external/paper-orchestra/README.md 18 KB
- external/paper-orchestra/requirements.txt 536 B
- external/paper-orchestra/setup.sh 6.7 KB runs code
- external/paper-orchestra/skills/agent-research-aggregator/references/extraction-prompt.md 4.5 KB
- external/paper-orchestra/skills/agent-research-aggregator/references/log-formats.md 5.5 KB
- external/paper-orchestra/skills/agent-research-aggregator/references/synthesis-prompt.md 5.1 KB
- external/paper-orchestra/skills/agent-research-aggregator/scripts/discover_logs.py 16 KB runs code
- external/paper-orchestra/skills/agent-research-aggregator/scripts/extract_experiments.py 7.9 KB runs code
- external/paper-orchestra/skills/agent-research-aggregator/scripts/format_po_inputs.py 13 KB runs code
- external/paper-orchestra/skills/agent-research-aggregator/SKILL.md 14 KB
- external/paper-orchestra/skills/content-refinement-agent/references/ai-failure-modes.md 813 B
- external/paper-orchestra/skills/content-refinement-agent/references/da-reviewer.md 1.7 KB
- external/paper-orchestra/skills/content-refinement-agent/references/halt-rules.md 4.4 KB
- external/paper-orchestra/skills/content-refinement-agent/references/prompt.md 5.7 KB
- external/paper-orchestra/skills/content-refinement-agent/references/reviewer-rubric.md 5.4 KB
- external/paper-orchestra/skills/content-refinement-agent/references/safe-revision-rules.md 4.8 KB
- external/paper-orchestra/skills/content-refinement-agent/references/writing-quality-check.md 720 B
- external/paper-orchestra/skills/content-refinement-agent/scripts/apply_worklog.py 3.1 KB runs code
- external/paper-orchestra/skills/content-refinement-agent/scripts/score_delta.py 5.3 KB runs code
- external/paper-orchestra/skills/content-refinement-agent/scripts/score_trajectory.py 8.6 KB runs code
- external/paper-orchestra/skills/content-refinement-agent/scripts/snapshot.py 1.6 KB runs code
- external/paper-orchestra/skills/content-refinement-agent/SKILL.md 12 KB
- external/paper-orchestra/skills/literature-review-agent/references/citation-density-rule.md 2.8 KB
- external/paper-orchestra/skills/literature-review-agent/references/discovery-pipeline.md 4.8 KB
- external/paper-orchestra/skills/literature-review-agent/references/exa-search-cookbook.md 9.1 KB
- external/paper-orchestra/skills/literature-review-agent/references/prompt.md 3.2 KB
- external/paper-orchestra/skills/literature-review-agent/references/s2-api-cookbook.md 4.1 KB
- external/paper-orchestra/skills/literature-review-agent/references/verification-rules.md 4.2 KB
- external/paper-orchestra/skills/literature-review-agent/scripts/bibtex_format.py 5.2 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/check_cutoff.py 2.2 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/citation_coverage.py 3.1 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/dedupe_by_id.py 3.1 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/exa_search.py 5.9 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/levenshtein_match.py 2.1 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/pre_dedup_candidates.py 4.9 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/s2_cache.py 3.5 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/s2_search.py 7.0 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/sync_keys.py 4.0 KB runs code
- external/paper-orchestra/skills/literature-review-agent/scripts/validate_pool.py 4.8 KB runs code
- external/paper-orchestra/skills/literature-review-agent/SKILL.md 15 KB
- external/paper-orchestra/skills/outline-agent/references/allowed-values.md 2.2 KB
- external/paper-orchestra/skills/outline-agent/references/example-output.json 4.8 KB
- external/paper-orchestra/skills/outline-agent/references/outline_schema.json 4.0 KB
- external/paper-orchestra/skills/outline-agent/references/outline-schema.md 4.2 KB
- external/paper-orchestra/skills/outline-agent/references/prompt.md 6.7 KB
- external/paper-orchestra/skills/outline-agent/scripts/validate_outline.py 5.0 KB runs code
- external/paper-orchestra/skills/outline-agent/SKILL.md 5.2 KB
- external/paper-orchestra/skills/paper-autoraters/references/citation-f1-prompt.md 2.5 KB
- external/paper-orchestra/skills/paper-autoraters/references/litreview-quality-prompt.md 7.9 KB
- external/paper-orchestra/skills/paper-autoraters/references/sxs-litreview-prompt.md 2.3 KB
- external/paper-orchestra/skills/paper-autoraters/references/sxs-paper-quality-prompt.md 3.4 KB
- external/paper-orchestra/skills/paper-autoraters/scripts/compute_f1.py 3.6 KB runs code
- external/paper-orchestra/skills/paper-autoraters/SKILL.md 6.4 KB
- external/paper-orchestra/skills/paper-orchestra/references/anti-leakage-prompt.md 2.5 KB
- external/paper-orchestra/skills/paper-orchestra/references/host-integration.md 7.8 KB
- external/paper-orchestra/skills/paper-orchestra/references/io-contract.md 7.1 KB
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.
- 7d ago First seen · 248 lines · 48 tokens per session scan A 8ea155521a71
ez-math-model is a skill published in the GitHub repository woodfishhhh/EZ_math_model (40 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 3,637 once invoked, about $0.0002 per session on Opus 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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