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 agentmods add commands/snakeying/textum/scaffoldgit clone --depth 1 https://github.com/snakeying/TextumWhat 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 | $0.00000 | $0.01313 |
| Opus 5 | $0.00000 | $0.00656 |
| Sonnet 5 | $0.00000 | $0.00263 |
| Haiku 4.5 | $0.00000 | $0.00131 |
Grade A, and why
scaffold 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 2d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
阶段2: 上下文提取
读取:docs/PRD.md(只读) | 写入:docs/GLOBAL-CONTEXT.md | 模板:.claude/textum/GLOBAL-CONTEXT-template.md | 可选输入:FAIL/DECISION 清单
从 PRD 抽取生成 GLOBAL-CONTEXT:只抽取/归纳;不新增信息。
输入(可选)
FAIL/DECISION清单(用于精准修正)
修正模式(当提供 FAIL/DECISION 清单时必须遵守)
- 以现有
docs/GLOBAL-CONTEXT.md为基线:只修改清单命中的位置,其余内容不做无关改动(最小 diff) - 若清单需要补充事实但 PRD 无法支撑:不要新增信息;保持
N/A并输出DECISION(需要先更新 PRD)
输出规则(必须遵守)
- 不输出 GC 原文
- 输出必须二选一:
PASS:在末尾追加:已写入:docs/GLOBAL-CONTEXT.md下一步:/scaffold-check
DECISION:当且仅当“修正模式”中存在需要 PRD 补充事实但 PRD 无法支撑的条目时输出(D-001起编号;每条包含:定位 / 问题 / 影响 / 建议动作),并在末尾追加:已写入:docs/GLOBAL-CONTEXT.md下一步:/scaffold-check
硬约束(必须遵守)
N/A_STRICT 判定口径(权威;逐字一致;禁止改写):
-
定位 PRD 小节
### 9.2 接口清单(必填)的正文(标题行之后到下一同级小节标题之前) -
判定
N/A_STRICT = true当且仅当:正文去掉空行并 Trim 后仅剩 1 行且该行严格等于N/A -
只做抽取/归纳:不得新增 PRD 中不存在的新规则/新枚举/新接口/新字段/新默认值
-
禁止占位符:输出中不得出现
TBD/[...] -
禁止 fenced code blocks:输出中不得出现任何 ```
-
禁止 PRD 锚点:
docs/GLOBAL-CONTEXT.md中不得出现任何PRD#...或<!-- PRD#... --> -
技术栈(GC 第 1 节):只允许从 PRD 显式抽取;PRD 未提供则写
N/A;不得猜测/推荐 -
若提供
FAIL/DECISION清单:必须逐条完成清单中的“修复方式/建议动作” -
必须按模板输出全部一级章节:
- 模板中为
N/A的小节:保持该小节正文仅一行N/A(不加额外解释) - 模板中为表格的小节:无信息则保留
N/A行/单元格(不加额外解释)
- 模板中为
抽取口径(按节;只取“已确认事实”)
- 第 1 节 技术栈:只写 PRD 明确出现的语言/框架/数据库等;否则保持
N/A - 第 2 节 项目结构:
| Path | 职责 |表:仅列 PRD8.0 功能点→落点映射中出现的FILE:<path>(去重;Path 写<path>,去掉FILE:前缀)### 项目验证命令表:PRD 未提供可执行命令则保持N/A
- 第 3 节 枚举值:从 PRD 第 7 节抽取;无则
N/A - 第 4 节 业务规则:从 PRD 第 6 节规则表抽取
BR-###与一句话规则描述(不扩写、不补充) - 第 5 节 权限矩阵:从 PRD 第 4 节抽取为行式表格
操作|角色|权限|说明(权限只能A/D/O) - 第 6 节 数据模型概览:仅抽取 PRD
8.1 表清单与8.3 表关系的摘要;无则N/A(不要复制8.2字段表) - 第 7 节 命名规范:仅抽取 PRD 明确给出的命名约定;无则
N/A - 第 8 节 API规范:
- 先按
N/A_STRICT判定 PRD 是否有 API - 若无 API:该节正文必须严格为 1 行
N/A - 若有 API:只抽取 PRD
9.1 通用约定中已确认的项;缺失项写N/A;不要新增默认错误码/示例 JSON
- 先按
最小读取范围(必须;避免通读)
- PRD:
4(权限矩阵)、6(规则表)、7.1/7.2(枚举/状态机)、7.3(命名规范;若正文为单行N/A则 GC 第 7 节保持单行N/A)、8.0(落点映射)、8.1/8.3(表清单/关系)、9.1/9.2(API 约定/清单;若N/A_STRICT则跳过)、10(非功能底线)
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.
- 2d ago First seen · 62 lines · 0 tokens per session scan A 25d7cb3d4c51
scaffold is a command published in the GitHub repository snakeying/Textum (41 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,313 tokens. 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.
Other commands, from other repositories
ask
数学研究操作系统入口:自动诊断用户意图,路由到思想透镜、数学知识库或设计翻译层。 English: Math Research OS entry: auto-diagnose user intent, route to thinking lenses, math knowledge base, or design translation layer.
ask
Math Research OS entry: auto-diagnose user intent, route to thinking lenses, math knowledge base, or design translation layer.
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.