Borrowing it
Nothing to install: this file belongs to Peiiii/nextclaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Peiiii/nextclaw/master/.agents/skills/user-facing-content-boundary/SKILL.mdgit clone --depth 1 https://github.com/Peiiii/nextclawWrote 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/peiiii/nextclaw/user-facing-content-boundary)<a href="https://agentmods.dev/skills/peiiii/nextclaw/user-facing-content-boundary"><img src="https://agentmods.dev/badge/skills/peiiii/nextclaw/user-facing-content-boundary/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/peiiii/nextclaw/user-facing-content-boundary"><img src="https://agentmods.dev/badge/skills/peiiii/nextclaw/user-facing-content-boundary.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 | $0.00110 | $0.01751 |
| Opus 5 | $0.00055 | $0.00875 |
| Sonnet 5 | $0.00022 | $0.00350 |
| Haiku 4.5 | $0.00011 | $0.00175 |
Grade A, and why
user-facing-content-boundary 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 9d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
用户可见内容边界
目标
把“内部怎么判断、怎么生产”和“用户最终看到什么”隔离开。用户可见内容只呈现产品结果、用户场景、可观察能力和必要边界,不暴露团队内部的方案讨论、方法论、分类依据或改版理由。
固定流程
- 先判定表面:这是对外页面、UI 文案、更新提示、用户文档或 release note,还是内部 design / plan / log。只有内部文档才可以解释决策过程。
- 先定事实源:重写或重新设计时,按“当前产品愿景与源码/真实运行界面 > 已确认仍有效的对外事实 > 同类产品调研 > 旧页面”的顺序取材。旧页面只是待核实的信息来源,不因已经存在就自动成为信息架构、篇幅或文案基线。
- 审计旧内容:对遗留页面逐项标记保留、重写、取消导航或删除;先按当前产品和用户任务建立完整覆盖矩阵,再决定复用哪些内容。禁止只换主题、导航或首页,却继续沿用已经失效的内容骨架。
- 拆分材料:把内部依据、原则、取舍、分类、实现顺序放到方案文档或协作回复;把用户能感知的能力、结果、步骤和限制放到最终内容。
- 结果化表达:每句话都回答用户关心的一个问题:我能做什么、在哪里做、得到什么、有什么限制、下一步怎么用。
- 截图数据源校准:官网、产品介绍或用户文档中的同一批产品截图必须使用一致的数据源。用户要求真实/本地数据时,所有被引用截图都要来自真实实例和代表性内容,不得混入空态、临时 mock 或只替换局部真实数据。NextClaw 对外产品截图只使用默认或雾蓝主题,同批素材保持一致。
- 截图与文案对齐:产品界面截图必须直接截自正在运行的真实界面;合成宣传卡片、重新排版的界面拼图或仅嵌入真实素材的概念图不得冒充产品截图。每张截图必须完整看见文案声称的关键界面、对象或结果;只出现空壳、标签栏、加载态、空白预览区、被裁断的结果或相邻能力时,必须换图或重截,不能靠文字解释补足。
- 用例校准:用例必须是用户带着待办来完成的具体任务,标题应包含对象、动作或结果;
接入模型、安装技能、统一入口、连接渠道这类能力入口只能放在能力说明区,不能冒充用例。 - 动作与受众检查:标题、价值主张和用例都必须让读者想到具体对象、界面、动作与结果,并能拍成一段真实操作。声称覆盖多类用户时,要逐类指出读者从哪段文字看到了什么实际选择理由;列举“最典型 / 最容易感受价值”的受众时,不得写成排他的“只适合 / 主要面向”名单,除非产品确实限制其他用户。产品对比优先正面说明自身优势在哪些需求中格外明显,不得靠暗示其他用户不适合、其他能力较弱或其他场景不如竞品来建立可信度。
开源、本地、可扩展等泛标签不能单独算作命中。工具或资源名词的并列清单也不算功能说明,必须改写为读者能完成的动作、对象和结果。融合、留在工作台、长期表面等只有产品作者能解释的结构词,必须改成用户实际会做和看到的事情。 - 显式分组守恒:用户已经明确给出受众、主题、优势或场景的数量与分组时,把它当成内容结构合同;不得擅自增删类别,也不得把支撑能力拆成新的并列项。确需重组时,先在讨论中说明理由并获得确认。
- 中文语感检查:中文用户可见内容必须像中文直接写成,而不是英文翻译。优先短句和动词句,少用抽象名词堆叠;如果大声读起来不顺,就改成用户日常会说的话。
- 红旗扫描:最终内容里出现这些信号时必须重写或删除:
我们决定、应该展示、官网优先、这个设计、本轮、核心判断、第一现场、不是只展示、不抢权重、方法论、取舍、分类依据、内部、方案、原则。 - 翻译腔扫描:中文最终稿里出现这些模式时必须重写:
能力进入流程、能力在背后连接、任务优先的界面、持续看见上下文、工作流里发现能力、连续堆叠能力 / 流程 / 入口 / 上下文 / 场景 / 体系等抽象词。 - 视角转换:把“我们为什么这样做”改成“用户现在可以怎样完成事”。如果一句话只能说明生产过程,移回内部文档。
- 最终自检:发布前至少读一遍成品,不看设计文档也能成立;读者不需要知道团队内部争论,也能理解产品价值。
可用句式
- 用户可以在
<界面/入口>中<动作>,并<结果>。 - 当
<场景>发生时,NextClaw 会<可观察行为>。 - 目前支持
<能力范围>;<限制>仍需用户确认或配置。 - 从
<起点>到<结果>,中间的<上下文/工具/材料>会保留在同一个工作流里。
常见改写
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.
- 9d ago First seen · 52 lines · 110 tokens per session scan A fe7b4f0221fc
user-facing-content-boundary is a skill published in the GitHub repository Peiiii/nextclaw (256 stars, last pushed today), licensed MIT. It adds 110 tokens to every session and 1,751 once invoked, about $0.0006 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.
Other skills, from other repositories
openclaw-github-dedupe
Investigate a cluster of GitHub issues and PRs, determine canonical candidates, post duplicate/related status, preserve contributor credit, and execute cleanup actions. Supports autonomous mode for provided-link-only closeout, merge/fix follow-through, changelog, and post-merge issue/PR cleanup.
tmux-lane-orchestrator
Manage one tmux agent lane from its matching ops pane, inspect live pane state and Codex logs on cold start, and produce concise manager summaries for OpenClaw and adjacent project work.
ghcrawl-cluster-operator
Use when inspecting a ghcrawl SQLite store, pulling GitHub issue/PR data, refreshing summaries, embeddings, and clusters, or extracting one cluster and its evidence through the ghcrawl CLI.
opik-optimizer
Optimize LLM prompts, tools, and agents in Opik using standardized optimizer workflows (prompt optimization, tool optimization, and parameter tuning), dataset/metric wiring, and result interpretation.
org-branch-cleanup
Audit and safely prune stale branches across a GitHub organization with immutable snapshots, conservative merged-PR classification, live SHA/protection/open-PR revalidation, resumable deletion ledgers, and post-delete verification. Use when a maintainer asks to clean up old, dead, merged, bot-created, or abandoned…
crabpot-perf-metrics
Interpret Crabpot and OpenClaw performance dashboard metrics, import-loop profiles, runtime profiles, and branch-to-branch perf deltas without over-reading noisy samples.