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 Zhangs-11/zs-skills --skill deep-researchgit clone --depth 1 https://github.com/Zhangs-11/zs-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/zhangs-11/zs-skills/deep-research)<a href="https://agentmods.dev/skills/zhangs-11/zs-skills/deep-research"><img src="https://agentmods.dev/badge/skills/zhangs-11/zs-skills/deep-research/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/zhangs-11/zs-skills/deep-research"><img src="https://agentmods.dev/badge/skills/zhangs-11/zs-skills/deep-research.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 | $0.00147 | $0.01399 |
| Opus 5 | $0.00073 | $0.00700 |
| Sonnet 5 | $0.00029 | $0.00280 |
| Haiku 4.5 | $0.00015 | $0.00140 |
Grade A, and why
deep-research 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 11d 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.
What it actually says
可追溯深度研究
目标不是堆满资料,而是让读者在有限时间内形成一个可检查的判断框架:研究对象为什么成为今天这样、它与真正可比对象差在哪里、这些历史能力和约束将怎样影响未来。
先固定研究问题
从用户真正要做的判断反推研究范围。明确研究对象、截止日期、读者、用途、时间跨度和需要比较的维度。能从上下文确定时直接开始;只有选择会实质改变研究对象或结论时,才一次询问一个关键变量。
按需要选择深度:
- 快速框架:帮助陌生读者建立基本地图,保留核心结论、关键时间线、最小对比表和未知项。
- 标准研究:完整走通纵向、横向、合并判断和事实核查。
- 深入报告:用户明确要求长报告、重大决策或全面研究时,扩大来源、时间跨度、对照样本和反证覆盖;不以固定字数冒充深度。
建立证据底座
优先使用离主张最近的一手来源:官方文档、原始数据、源代码或规范、论文、财报、监管文件、正式公告和当事人完整访谈。二手资料适合发现线索与补充争议背景,不能替代它声称引用的一手来源。
为重要结论就近记录来源与日期。遇到来源冲突时并列呈现口径、时间和样本差异,不按数量投票;找不到足够证据时明确写“暂未核实”。研究对象、竞争格局、价格、人员、法规和产品状态可能变化时,以执行当天为截止时间重新核验。
纵向:解释它怎样走到今天
围绕会改变当前能力与约束的节点研究:
- 它在什么背景、需求和约束下出现,关键推动者是谁;
- 哪些成功、失败、产品转向、技术选择或外部变化构成真正转折;
- 哪些早期选择沉淀为今天的能力、路径依赖、组织惯性或包袱;
- 哪些常见历史叙事只是事后归因,缺少当时证据。
时间线只保留改变因果链的节点,不把所有发布日期排成流水账。
横向:用统一维度比较真实替代品
比较对象由用户或市场实际会在其中选择的替代方案决定,不只选名字相似者。先说明为什么选择这些对象,再用同一组维度比较目标用户、核心机制、能力边界、成本、分发或采用路径、主要优势与短板。
继续追问两件事:用户、客户或市场为什么选择它;又在什么条件下放弃它。市场份额、融资、声量或单项跑分不能单独代表产品质量或长期胜负。
合并两条轴
把历史形成的能力、路径依赖和现实约束映射到当前竞争差异,再推导未来。除非用户明确要求更多,最多给出三条真正不同的可能路径,并为每条写清:成立前提、最早可观察信号、主要反证和失效条件。相近趋势合并到同一路径下,不用增加条目制造全面感。
未来部分属于推断,不得写成已经发生的事实。若不同路径取决于用户的价值排序,说明实际取舍;若取决于尚未取得但可观察的数据,给出最短验证路径。
逐条核查关键主张
将会影响结论的说法拆成三类:
- 事实:可由外部证据核验,例如日期、数字、功能、事件和公开表态;
- 推断:从事实推出的原因、影响、趋势或未来判断;
- 价值判断:取决于谁的目标、成本与偏好,例如“更好”“值得”“应该”。
只给事实使用证据等级:已证实、基本成立但需要收窄、存在争议、证据不足、明显错误。事实成立后仍要单独检查推断是否混淆相关性与因果、遗漏其他解释或越过适用范围。价值判断说明它服务谁、牺牲什么以及什么目标改变时结论会反转。
输出
按研究深度裁剪,默认顺序是:
- 核心结论;
- 关键时间线;
- 横向对比表;
- 纵横合并后的详细分析;
- 未来路径、前提与预警信号;
- 关键事实核查与仍待确认的问题。
先给结论,再给能支撑结论的证据。事实、推断和观点在语言上可区分;引用靠近对应主张。用户没有要求文件时直接在当前对话交付;要求沉淀、交接或仓库研究记录时,再写入符合目标项目惯例的 Markdown 文件。
完成标准
完成前确认:研究问题和截止时间明确;关键结论可追溯到足够新的事实源;纵向节点解释了当前能力或约束;横向对象是真实替代品且维度统一;主要反证和冲突信息没有被隐藏;事实、推断和价值判断没有混写;未来路径不超过用户要求的数量并带有前提、信号与失效条件;篇幅由问题需要决定而非固定字数。
What ships with it
3 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.
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.
- 11d ago First seen · 75 lines · 147 tokens per session scan A cdbed67f1388
deep-research is a skill published in the GitHub repository Zhangs-11/zs-skills (2 stars, last pushed 4d ago), licensed MIT. It adds 147 tokens to every session and 1,399 once invoked, about $0.0007 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.
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