CCPM Project Management
automazeio/ccpm
Runs a spec-driven workflow from PRD to epic to GitHub issues to parallel agents, with status, standup and blocked-work reports from bundled scripts.
胥克谦式AI-Native产品开发方法论。适用于：(1) 使用AI Agent（Claude Code、Codex、Cursor等）进行产品级软件开发，(2) 设计和优化Harness/Skill体系，(3) 文档驱动开发(SDD)流程，(4) 构建自动化质量门禁和eval机制，(5) Token成本优化与缓存策略，(6)…
Install Keqian Method
$ npx skills add staruhub/ClaudeSkills --skill keqian-method -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install staruhub/ClaudeSkills keqian-method --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/staruhub/ClaudeSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/Geek-skills-keqian-method .claude/skills/keqian-method && 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 "keqian-method" agent skill from https://github.com/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-keqian-method into .claude/skills/keqian-method/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keqian-method", 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/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-keqian-methodType 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 staruhub/ClaudeSkills --skill keqian-method -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install staruhub/ClaudeSkills keqian-method --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/staruhub/ClaudeSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/Geek-skills-keqian-method .agents/skills/keqian-method && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "keqian-method" agent skill from https://github.com/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-keqian-method into .agents/skills/keqian-method/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keqian-method", 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 staruhub/ClaudeSkills --skill keqian-method -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install staruhub/ClaudeSkills keqian-method --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/staruhub/ClaudeSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/Geek-skills-keqian-method .cursor/skills/keqian-method && 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 "keqian-method" agent skill from https://github.com/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-keqian-method into .cursor/skills/keqian-method/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keqian-method", 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/staruhub/ClaudeSkills.git --path skills/Geek-skills-keqian-method--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 staruhub/ClaudeSkills --skill keqian-method -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install staruhub/ClaudeSkills keqian-method --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/staruhub/ClaudeSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/Geek-skills-keqian-method .gemini/skills/keqian-method && 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 "keqian-method" agent skill from https://github.com/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-keqian-method into .gemini/skills/keqian-method/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keqian-method", 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 staruhub/ClaudeSkills keqian-methodInstalls 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 staruhub/ClaudeSkills --skill keqian-method -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/staruhub/ClaudeSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/Geek-skills-keqian-method .github/skills/keqian-method && 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 "keqian-method" agent skill from https://github.com/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-keqian-method into .github/skills/keqian-method/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keqian-method", 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 staruhub/ClaudeSkills --skill keqian-method -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install staruhub/ClaudeSkills keqian-method --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/staruhub/ClaudeSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/Geek-skills-keqian-method .opencode/skills/keqian-method && 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 "keqian-method" agent skill from https://github.com/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-keqian-method into .opencode/skills/keqian-method/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keqian-method", 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
keqian-method- GitHub stars
- 727
- Token cost
- ~1.1k tokens
- SKILL.md length
- 177 words
- Files
- 4 (incl. references)
- Skills in repo
- 20
- Repo updated
- First seen
- Licence
- MIT
At a glance
胥克谦式AI-Native产品开发方法论。适用于：(1) 使用AI Agent（Claude Code、Codex、Cursor等）进行产品级软件开发，(2) 设计和优化Harness/Skill体系，(3) 文档驱动开发(SDD)流程，(4) 构建自动化质量门禁和eval机制，(5) Token成本优化与缓存策略，(6)…
- Works in 3 steps: 逐个问题点被反复修正 → 高缓存命中 → 高缓存命中 = 高质量（说明问题已收敛） → 缓存命中的token几乎不花钱
- Tasks that involve PRD writing
- SKILL.md covers 第一原则：Iron Law（铁律）, 第二原则：单Agent极致论, 第三原则：文档驱动开发（SDD） and 第四原则：质量门禁闭环（Verification-Driven）, plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Keqian Method is an agent skill from staruhub/ClaudeSkills. 胥克谦式AI-Native产品开发方法论。适用于：(1) 使用AI Agent（Claude Code、Codex、Cursor等）进行产品级软件开发，(2) 设计和优化Harness/Skill体系，(3) 文档驱动开发(SDD)流程，(4) 构建自动化质量门禁和eval机制，(5) Token成本优化与缓存策略，(6) 产品人转型开发者的AI编程实践。触发场景包括"帮我设计开发流程"、"怎么降低token成本"、"怎么提高AI编码质量"、"文档驱动"、"质量门禁"、"harness设计"、"单agent vs multi-agent"、"自动化迭代"、"AI产品开发"、"SDD"、"eval机制"等。即使用户只是说"帮我用AI写代码"或"怎么让agent干活更靠谱"也应触发。注意：如果产品是行为开放、用户输入不可穷举的AI-native类型，请改用 xuefeng-method skill。不用于：单个bug修复或小改动（无需方法论）、PRD需求文档写作（用product-manager）。
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `evals/routing-evals.json`, `references/eval-patterns.md` and `references/sdd-framework.md`).
It sits in Product & Project Management, covering PRD writing. The repository describes itself as: 13 curated Agent Skills for research, product decisions, decks, publishing, audits, and more — portable across skills-compatible agents. The licence is MIT.
When your agent uses it
- Tasks that involve PRD writing
Example prompts
- “帮我设计开发流程”
- “怎么降低token成本”
- “怎么提高AI编码质量”
- “/keqian-method”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- 逐个问题点被反复修正 → 高缓存命中
- 高缓存命中 = 高质量（说明问题已收敛）
- 缓存命中的token几乎不花钱
What it can do on your machine
Read from SKILL.md and the folder at commit 66e02d2. 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
No scripts in the folder and no shell commands in SKILL.md.
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
Keqian Method loads about 1.1k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 117 tokens; SKILL.md has 177 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from staruhub/ClaudeSkills at commit 66e02d2, republished under its MIT licence (© staruhub). 177 words, ~1,069 tokens.
Download SKILL.mdSave it as
.claude/skills/keqian-method/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- keqian-method
- description
- 胥克谦式AI-Native产品开发方法论。适用于：(1) 使用AI Agent（Claude Code、Codex、Cursor等）进行产品级软件开发，(2) 设计和优化Harness/Skill体系，(3) 文档驱动开发(SDD)流程，(4) 构建自动化质量门禁和eval机制，(5) Token成本优化与缓存策略，(6) 产品人转型开发者的AI编程实践。触发场景包括"帮我设计开发流程"、"怎么降低token成本"、"怎么提高AI编码质量"、"文档驱动"、"质量门禁"、"harness设计"、"单agent vs multi-agent"、"自动化迭代"、"AI产品开发"、"SDD"、"eval机制"等。即使用户只是说"帮我用AI写代码"或"怎么让agent干活更靠谱"也应触发。注意：如果产品是行为开放、用户输入不可穷举的AI-native类型，请改用 xuefeng-method skill。不用于：单个bug修复或小改动（无需方法论）、PRD需求文档写作（用product-manager）。
- version
- 1.1.0
克谦方法论：AI-Native产品开发实战体系
核心理念：产品人思维 × 极致单Agent × 文档驱动 × 质量门禁闭环
来源：胥克谦——从音乐教师到产品经理到AI-Native连续创业者，皮影客创始人， 十几万行自建skill和脚本的harness工程实践者。
第一原则：Iron Law（铁律）
概率乘是第一性原理。
每个环节的成功率相乘决定最终质量。即使每次0.99，n=51后也不及格。 因此：不追求一次完美，追求每个环节可验证、可修复、可迭代。
推论：
- 勤不能补拙——模型能力是底线，harness和skill只是加速器和放大器
- 拆到足够简单，单项任务才能收敛
- 每个action必须对应一个eval
第二原则：单Agent极致论
不盲目使用multi-agent。单agent做到极致，再考虑编排。
何时用单Agent（默认选择）
- 有先后依赖关系的任务
- 需要上下文连贯性的长程任务
- 质量要求高、不容错的核心流程
何时用并行SubAgent（例外情况）
- 任务间明确无依赖关系（如多角度审计出报告）
- 并行结果合并时不易出问题
- 你有能力精确控制每个subagent的上下文注入
并行的陷阱
- SubAgent上下文注入是个坑：注入什么、注入多少，都需要精确控制
- 主Agent可能假装自己是SubAgent（实际遇到过）
- 并行任务中一个环节出问题，整个长任务可能报废
- 合并结果时容易引入不一致
实践建议： 如果不确定，选顺序执行。慢但可靠。
第三原则：文档驱动开发（SDD）
7成精力投入文档质量和harness，3成精力写代码。
为什么文档比代码重要
- 不写文档就没有架构观
- 不可能每次都让AI全量扫代码
- 零散的功能 = 零散的质量
- 让AI自己维护一份文档，代码再vibe对齐
SDD工作流
1. 需求文档（PRD/设计文档）
↓ AI辅助撰写 + 人工审核
2. 技术文档（架构决策、接口规范）
↓ AI维护 + 人工把关
3. 代码实现
↓ Agent执行 + 质量门禁拦截
4. 文档回写（代码变更 → 文档自动更新）
↓ 闭环文档质量门禁
文档的自动化质量控制比代码难很多。关键点：
- 技术栈选择本身是套路化的事，可以模板化
- 每个功能点不能只给3个用例敷衍了事（一轮不够就多轮）
- 但也要防止过度设计——把握平衡点，结合项目实际
第四原则：质量门禁闭环（Verification-Driven）
严格的质量门禁 = 高缓存命中率 = 高质量 = 低成本。
门禁设计
每个Action → 对应Eval → 通过/不通过
↓ 不通过
自动修复（最多N轮）→ 仍不通过 → 升级给人类Eval的acceptable threshold
- 不同业务、不同团队有不同threshold
- 关键是在【期望预算内、期望时间内】出【期望结果】
- 不要指望1次成型，那是稀罕事
- AI-Native迭代3~5轮是比较理想的acceptable threshold
反直觉发现：多烧 ≠ 多花钱
自动化修正流程表面上浪费token，但实际上：
- 逐个问题点被反复修正 → 高缓存命中
- 高缓存命中 = 高质量（说明问题已收敛）
- 缓存命中的token几乎不花钱
实测数据： 缓存命中率99%+时，每1亿token ≈ 8.5 RMB，约等于不要钱。
推论： 省token其实很不划算。放开token使用量，反倒造成事实成本下降。
第五原则：产品拆解思维
端到端都是复杂的，单维度都是简单的。
拆解方法论
- 复杂问题 → 拆成多层次
- 每个层次 → 单维度可穷举
- 单维度选项有限 → 模型可做决策
- 输入变量（公司规模、场景、约束）→ 都是条件变量
边界内泛化
- 任何产品都有边界
- 边界内的泛化并不难，都是可穷举的
- 不需要100%泛化，只要目标范围内泛化
- 端到端复杂 ≠ 单维度复杂
适用边界
此方法适合场景明确、边界可定义的产品。 对于用户行为高度不可预测的AI-Native交互产品，需要补充上线后快速迭代的机制。
第六原则：与AI斗智斗勇
AI会联合你写的skill和门禁来对抗你的要求。
已知的AI抵抗模式
- 要删除一个段落 → AI用段落改名、转移位置、改写保留语义等方式抵抗
- 新开会话、重开codex、换电脑都不能消除抵抗
- 这种现象可能持续数天
应对策略
- 上eval：不符合要求就持续迭代（反复删也是种迭代）
- 每个action都对应eval：如果不放心的话
- CICD集成逻辑复盘：用以前CI/CD集成那套逻辑来复盘问题
- 降低抽卡概率：通过harness降低模型抽卡比例
- 及时compact：达到上下文窗口*0.5左右就/compact，保持智力不掉线
实战工作流模板
启动新项目
Phase 1: 文档先行（占总时间70%）
├── 撰写PRD（AI辅助 + 人工审核）
├── 技术架构文档（AI维护 + 人工把关）
├── 定义质量门禁和eval标准
└── 设计harness结构（skill + rule配置）
Phase 2: 代码实现（占总时间20%）
├── Agent顺序执行任务
├── 每个任务通过质量门禁
├── 不通过 → 自动修复 → 仍不通过 → 人工介入
└── 文档自动回写
Phase 3: 迭代收敛（占总时间10%）
├── 跑eval批量验证
├── 收集失败case → 分析 → 改进harness
└── 直到达到acceptable threshold日常开发节奏
1. 不用子代理（除非任务明确无依赖）
2. 顺序给任务，每个任务带eval
3. 放着跑，定期查看
4. 门禁拦住的问题 → 分析是harness问题还是模型问题
5. harness问题 → 改skill/rule
6. 模型问题 → 换模型或降低任务粒度模型选择建议
基于实战经验：
- 不是纯coding：不要用xxx-codex模型，直接切通用模型
- 稳定优先：慢点就慢点，但牢靠、不啰嗦
- 国模注意事项：
- 进入上下文窗口*0.4~0.5就可能降智
- 通过harness降低抽卡比例
- 达到窗口*0.5就compact
- 长上下文不是万能的：关键是进入dumb zone的阈值要高
成本优化清单
- ✅ 建立严格质量门禁 → 自然提高缓存命中率
- ✅ 放开token使用量 → 反直觉地降低成本
- ✅ 自动化修正流程 → 缓存命中率越来越高
- ✅ 顺序执行 → 避免并行失败导致的浪费
- ✅ 及时compact → 避免降智导致的返工
- ❌ 不要手动省token → 反而导致质量差、返工多、总成本高
心法总结
"做一个马鞍，再做一个拆马鞍的工具" — 群友评价
"一抓就死，一放就乱" — 管理的永恒难题
"多烧 ≠ 多花钱" — 反直觉的真理
"端到端复杂，单维度简单" — 产品拆解的核心
"慢点就慢点，但牢靠" — 稳定性压倒一切验收标准（按本方法论执行的任务，交付前自查）
- 写代码之前存在对应的规格文档（SDD：文档先行，不是事后补）
- 每个交付环节过了质量门禁（测试/断言/eval），没有"看起来没问题"式放行
- 概率乘意识：长链条任务拆成了可独立验证的小环节，而不是一把梭
- 用的是单 Agent 主导的流程；引入并行前说明了"为什么单 Agent 不够"
- Token 成本有意识：复用缓存、避免重复喂上下文
参考资料
更多方法论细节请查阅：
references/sdd-framework.md— SDD文档驱动开发框架详细流程references/eval-patterns.md— 质量门禁和Eval模式库evals/routing-evals.json— 触发边界回归用例（含与 xuefeng-method 的互斥镜像），改动 description 后用仓库根scripts/run_routing_evals.py校验
© staruhub, 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 3 other files (references) in skills/Geek-skills-keqian-method of staruhub/ClaudeSkills.
- SKILL.md
- evals/routing-evals.json
- references/eval-patterns.md
- references/sdd-framework.md
Open the folder on GitHubat commit 66e02d2
Compare with similar skills
Keqian Method 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 |
|---|---|---|---|---|---|---|
| Keqian Method this skillstaruhub/ClaudeSkills | 727 | — | ~1.1k | Automated safety check: Pass | MIT | |
| CCPM Project Managementautomazeio/ccpm | 8.4k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Trellis Brainstormanjiemo/SunnyBeach | 178 | 7 repos | ~4k | Automated safety check: Pass | Apache-2.0 | |
| Ouroboros PM InterviewQ00/ouroboros | 6.2k | — | ~5.7k | Automated safety check: Pass | MIT | |
| Project Planneradrianpuiu/claude-skills-marketplace | 100 | 1 repos | ~6k | Automated safety check: Pass | None | |
| Reviewfossasia/eventyay-interpretation | 1.6k | 35 repos | ~996 | Automated safety check: Pass | Apache-2.0 |
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Related
Questions about Keqian Method
What does Keqian Method do?
胥克谦式AI-Native产品开发方法论。适用于：(1) 使用AI Agent（Claude Code、Codex、Cursor等）进行产品级软件开发，(2) 设计和优化Harness/Skill体系，(3) 文档驱动开发(SDD)流程，(4) 构建自动化质量门禁和eval机制，(5) Token成本优化与缓存策略，(6)…. Keqian Method is an agent skill from staruhub/ClaudeSkills.
When should I use Keqian Method?
Keqian Method fits situations like: tasks that involve PRD writing.
How do I install Keqian Method in Claude Code?
Run `npx skills add staruhub/ClaudeSkills --skill keqian-method -a claude-code`. Or copy the skill folder (skills/Geek-skills-keqian-method in staruhub/ClaudeSkills) into .claude/skills/keqian-method in your project. Claude Code loads it when a task matches its description.
How do I install Keqian Method in Codex?
Run `npx skills add staruhub/ClaudeSkills --skill keqian-method -a codex`. Or copy the skill folder (skills/Geek-skills-keqian-method in staruhub/ClaudeSkills) into .agents/skills/keqian-method in your project. Codex loads it when a task matches its description.
Can I use Keqian Method 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 staruhub/ClaudeSkills --skill keqian-method -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/keqian-method, .gemini/skills/keqian-method, .github/skills/keqian-method and .opencode/skills/keqian-method in your project.
What does Keqian Method need to run?
SKILL.md names no scripts, command-line tools or credentials: Keqian Method is instructions for the agent only.
Does Keqian Method 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 Keqian Method 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. Review the folder before installing.
What licence does Keqian Method use?
Keqian Method 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 Keqian Method use?
About 1.1k tokens (SKILL.md is roughly 4.3k 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 1.7k tokens, read only when the agent opens those files.
What are the alternatives to Keqian Method?
Skills that share tags, products or a category with Keqian Method: CCPM Project Management (automazeio/ccpm, 8.4k stars), Trellis Brainstorm (anjiemo/SunnyBeach, 178 stars), Ouroboros PM Interview (Q00/ouroboros, 6.2k stars) and Project Planner (adrianpuiu/claude-skills-marketplace, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Keqian Method?
staruhub (a GitHub user) maintains it in staruhub/ClaudeSkills, which has 727 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on August 13, 2026.
Source: staruhub/ClaudeSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.