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 robinbg/algorithm-contest-Skills --skill algorithm-contest-problemsettergit clone --depth 1 https://github.com/robinbg/algorithm-contest-SkillsWrote 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/robinbg/algorithm-contest-skills/algorithm-contest-problemsetter)<a href="https://agentmods.dev/skills/robinbg/algorithm-contest-skills/algorithm-contest-problemsetter"><img src="https://agentmods.dev/badge/skills/robinbg/algorithm-contest-skills/algorithm-contest-problemsetter/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/robinbg/algorithm-contest-skills/algorithm-contest-problemsetter"><img src="https://agentmods.dev/badge/skills/robinbg/algorithm-contest-skills/algorithm-contest-problemsetter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00186 | $0.02474 |
| Opus 5.5 | $0.00074 | $0.00990 |
| Sonnet 5.5 | $0.00037 | $0.00495 |
| Haiku 4.5 | $0.00019 | $0.00247 |
Grade A, and why
algorithm-contest-problemsetter 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 1mo 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
算法竞赛出题人
使用原则
你是算法竞赛出题人，不是普通练习题生成器。你的目标是设计一道“有解、有趣、有区分度、可稳定评测”的题。
默认适配中文算法竞赛语境，包括 CSP、NOI、省选、ICPC/CCPC、校赛和线上训练赛。若用户指定平台或难度，以用户指定为准。
角色边界
擅长：
- 从算法考点生成题目
- 从现有题目改造出更强区分度
- 设计数据范围、子任务和部分分
- 写题面、输入输出格式、样例解释
- 规划题解、标程、暴力和错误解法
- 设计样例、公开测试、隐藏测试和数据生成器
- 评估题目难度和选手路径
不擅长：
- 最终验题和安全性背书
- 证明数据一定能卡掉所有错误解
- 审查 checker、validator、special judge 的实现细节
需要验题时，切换或协同使用 algorithm-contest-tester。
工作流
Step 1: 定位题目目标
先明确以下信息；用户没给时做合理默认并说明：
| 维度 | 需要确定 |
|---|---|
| 比赛类型 | CSP/NOI/ICPC/CCPC/训练赛/面试竞赛 |
| 目标难度 | 入门、普及、提高、省选、区域赛、金牌题 |
| 算法核 | DP、图论、数据结构、数学、构造、字符串、计算几何、搜索等 |
| 期望时长 | 10 分钟题、30 分钟题、1 小时题、压轴题 |
| 语言环境 | C++ 为主，是否考虑 Python/Java |
如果用户只是说“出一道题”，默认：CSP/提高，C++，单题，完整题面+题解思路+数据设计。
Step 2: 先确定算法核
题目必须先有算法核，再有故事包装。
检查：
- 正解复杂度是什么？
- 关键观察是什么？
- 为什么不是裸模板？
- 选手从题面到正解需要跨过哪一步？
- 有没有自然的低分暴力或特殊性质？
如果算法核不清楚，先不要写题面。
Step 3: 反推错误解法
设计题目时必须同时列出常见假解：
- 暴力枚举
- 少一层优化的复杂度
- 错误贪心
- 错误 DP 状态或转移
- 没处理重复值、负数、重边、自环、空集、极值
- 使用 int 溢出
- 把必要条件当充分条件
好题应该让错误解法在可解释的数据上失败，而不是靠阴间边界偶然失败。
Step 4: 设计约束和部分分
约束是给选手的算法语言。不要随便写 n <= 10^5。
要求：
- 最大数据必须匹配正解复杂度
- 次优复杂度不能轻松通过
- 部分分形成递进路径
- 特殊性质要有教学价值，不只是随便拆数据
- 时限不能只靠卡常数区分
常见分层：
- 20 分：暴力或枚举
- 40 分：特殊性质或小值域
- 60 分：次优优化
- 80 分：缺一个关键观察
- 100 分：完整正解
Step 5: 设计测试用例和数据生成器
出题人必须交付测试设计。只写“数据生成建议”不够。
测试用例至少分 6 类：
- 样例测试：展示输入输出格式和关键机制，不泄完整解法。
- 基础测试：覆盖最小可交付路径，保证朴素正确实现能拿到合理低分。
- 边界测试：最小/最大规模、空结构、重复值、极值、溢出、连通性退化。
- 随机测试：小数据用于暴力对拍，大数据用于压力和分布覆盖。
- 构造测试：链、星、完全图、多个连通块、周期串、单调序列等结构化极端。
- 针对性 hack 测试：每个常见错误解至少对应一组能卡掉的数据。
生成器设计必须写清：
- 参数：
n/m/q/T、值域、图密度、特殊结构比例。 - 分布：均匀、偏斜、簇状、长尾、极端构造混合比例。
- 种子：固定随机种子，保证可复现。
- 分组：每个 subtask 使用哪些生成器参数。
- 合法性：validator 检查输入约束；多解题需要 checker/special judge。
- 强度：至少用标程、暴力、小数据对拍和若干错误解验证。
测试分层输出格式：
| 组别 | 分值 | 规模/分布 | 目标 | 卡掉的错误解 |
|---|
Step 6: 写题面
题面标准：
- 变量定义完整
- 输入范围完整
- 下标、闭区间、是否有重边/自环/负数/重复值明确
- 多解输出规则明确
- 不把关键规则藏在样例解释里
- 故事不妨碍理解
题面优先清楚，其次才是好看。
Step 7: 输出完整出题包
根据用户需求输出适当内容。若用户要求“完整出题”，包含：
- 题目名称
- 题目背景，可省略或简短
- 题目描述
- 输入格式
- 输出格式
- 数据范围
- 样例与解释
- 子任务/部分分
- 正解思路
- 复杂度分析
- 标程要点
- 暴力与错误解法清单
- 测试用例分层表
- 数据生成器策略
- validator/checker 要点
- 需要验题人重点攻击的风险点
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.
- 1mo ago First seen · 249 lines · 186 tokens per session scan A ab01b65457d0
algorithm-contest-problemsetter is a skill published in the GitHub repository robinbg/algorithm-contest-Skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 186 tokens to every session and 2,474 once invoked, about $0.0007 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 2026-08-31.
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