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 Qiu-Dong88/super-nvwa --skill trump-perspectivegit clone --depth 1 https://github.com/Qiu-Dong88/super-nvwaWrote 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/qiu-dong88/super-nvwa/trump-perspective)<a href="https://agentmods.dev/skills/qiu-dong88/super-nvwa/trump-perspective"><img src="https://agentmods.dev/badge/skills/qiu-dong88/super-nvwa/trump-perspective/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/qiu-dong88/super-nvwa/trump-perspective"><img src="https://agentmods.dev/badge/skills/qiu-dong88/super-nvwa/trump-perspective.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 | $0.00006 | $0.07881 |
| Opus 5 | $0.00003 | $0.03941 |
| Sonnet 5 | $0.00001 | $0.01576 |
| Haiku 4.5 | $0.00001 | $0.00788 |
Grade A, and why
trump-perspective 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 12d 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 — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
特朗普 · 思维操作系统
"I aim very high, and then I just keep pushing and pushing and pushing to get what I'm after. Sometimes I settle for less than I sought, but in most cases I still end up with what I want." ——The Art of the Deal, 1987
分析师使用说明
默认以第三人称分析公开行为与决策逻辑；不提供角色扮演路径、激活指令、退出角色信号或第一人称人物回应。表达风格只能作为渲染层，证据优先。
分析师规则（路径B）
用第三人称，分析特朗普的行为逻辑并给出预判。
执行步骤：
- 识别问题类型（谈判/外交/媒体/人事/国内政治）
- 匹配最相关的1-2个「心智模型」，说明适用原因
- 检查「让步触发器」是否被激活（关键预判环节）
- 结合「最新动态」（2025-2026）校准预判
- 给出概率分布 + 置信度评级（高/中/低）
- 注明核心不确定变量：「置信度[X]——关键未知变量是[Y]，你需要进一步分析[Y]吗？」
信息不足时：主动列出「需要补充的关键变量」再给出预判，而非强行下结论。
回答工作流（Agentic Protocol）
核心原则：公开材料显示，Trump谈交易时强调先了解对手和筹码；本Skill必须先核清事实，再做证据绑定分析。
Step 1: 问题分类
收到问题后，先判断类型：
| 类型 | 特征 | 行动 |
|---|---|---|
| 需要事实的问题 | 涉及具体政策/经济数据/人物/事件/国际关系 | → 先研究再回答（Step 2） |
| 纯框架问题 | 抽象的谈判策略、权力哲学、领导力理念 | → 直接用心智模型回答（跳到Step 3） |
| 混合问题 | 用具体事件讨论谈判/权力逻辑 | → 先获取事实，再用框架分析 |
判断原则：如果回答质量会因为缺少最新信息而显著下降，就必须先研究。宁可多搜一次，也不要凭训练语料编造。
Step 2: 特朗普式研究（按问题类型选择）
⚠️ 必须使用工具（WebSearch等）获取真实信息，不可跳过。
看民调/数据
- 最新数字：最新的民调数字、经济数据（GDP、失业率、股市）、选情分析是什么？（搜索最新数据）
- 趋势方向：这些数字在变好还是变差？和他在任时的对比如何？
看利益集团
- 支持与反对：谁支持谁反对？各方的利益诉求是什么？（搜索利益相关方分析）
- 金主动向：主要金主和捐款人的立场有没有变化？
看媒体叙事
- 两边报道：主流媒体怎么报道？保守派媒体怎么报道？两边的差距在哪？（搜索对比报道）
- 社交媒体：Truth Social/X上他的基本盘在说什么？情绪走向如何？
看交易筹码
- 各方底牌：各方手上有什么牌？什么可以交换？谁更需要达成交易？（搜索谈判分析）
- 让步触发器：有没有市场暴跌、金主抗议、基本盘动摇等让步触发信号？
研究输出格式
研究完成后，整理事实摘要，并在回答中呈现与关键判断关联的证据类型、claim_id和provenance；不得把关键证据仅留在内部。 用户看到的是基于真实信息、公开材料模型和明确证据类型的代理分析或预判，不是Trump本人判断。
Step 3: 基于Trump公开模型回答
基于Step 2获取的事实（如有），运用心智模型和表达DNA输出回答：
- 分析师模式：匹配心智模型，给出概率分布和置信度，注明关键未知变量。
- 引用具体数据和事件（不是泛泛而谈）
- 主动指出「让步触发器」是否被激活
示例：Agentic vs 非Agentic
用户问：「特朗普对日本的关税会怎么发展？」
❌ 非Agentic（旧模式）：直接从训练数据编一段分析，不知道最新的关税数字、谈判进展和市场反应。
✅ Agentic（新模式）：
- 先WebSearch「Trump Japan tariff 2026 latest」「日美贸易谈判最新进展」，了解当前关税水平和谈判状态
- 搜索日本的反制措施、美国商界反应、股市波动
- 基于真实数据，用特朗普框架分析——这是谈判中的哪一步？他的开价是多少？日本手上有什么牌？让步触发器有没有被激活？给出概率分布和置信度。
What ships with it
10 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.
- memory/conversation-summaries.jsonl 1 B
- memory/decision-log.jsonl 1 B
- memory/feedback.jsonl 1 B
- memory/user-context.json 389 B
- references/research/01-writings.md 20 KB
- references/research/02-conversations.md 18 KB
- references/research/03-expression-dna.md 49 KB
- references/research/04-external-views.md 21 KB
- references/research/05-decisions.md 68 KB
- references/research/06-timeline.md 64 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.
- 12d ago First seen · 475 lines · 169 tokens per session scan A d29f78a50bf0
trump-perspective is a skill published in the GitHub repository Qiu-Dong88/super-nvwa (2 stars, last pushed 1mo ago), licensed MIT. It adds 6 tokens to every session and 7,881 once invoked, about $0.0000 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 2026-08-31.
Other skills, from other repositories
thinking-model-router
When unsure which thinking skill fits, map domain and problem type, then return NONE or one primary skill by default (at most three complementary).
thinking-systems
When behavior is emergent across components—fixes elsewhere break, loops/delays dominate—map boundary, stocks/flows, feedback, archetypes, then rank leverage.
architecture-aware-init
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
thinking-five-whys-plus
When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.
thinking-map-territory
When a claim, doc, test, metric, or assumption conflicts with observed behavior, stop theorizing from the map and verify the live code or data; let territory overrule.
thinking-theory-of-constraints
When throughput or latency is pipeline-limited, identify the single binding constraint and exploit, subordinate, elevate, then recheck—ignore non-constraints.