deep-research-partnership-planner

deep-research-partnership-planner is a skill for Claude Code, Codex from cafe3310/public-agent-skills. It costs 27 tokens per session (2,425 once invoked), scanned A, original, Apache-2.0.

A research-led workflow for planning partnerships with companies or ecosystems in the large-language-model field. It requires external research and combines the findings with the user's own business context before producing a partnership and go-to-market plan.

In plain words
What is it for?
Exploring potential partners, generating research prompts, reviewing research findings, identifying cooperation opportunities, and creating business and promotion action plans.
Why use it?
It reduces the risk of inventing partnership ideas without evidence. It also keeps human input in the process so the plan reflects real goals and opportunities.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Exploring potential partners, generating research prompts, reviewing research findings, identifying cooperation opportunities, and creating business and promotion action plans.

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Install with agentmods
npx agentmods add skills/cafe3310/public-agent-skills/deep-research-partnership-planner
Install

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.

Any agent
npx skills add cafe3310/public-agent-skills --skill deep-research-partnership-planner
Clone the repo
git clone --depth 1 https://github.com/cafe3310/public-agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for deep-research-partnership-planner

README.md
[![agentmods](https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/deep-research-partnership-planner/github.svg)](https://agentmods.dev/skills/cafe3310/public-agent-skills/deep-research-partnership-planner)
Your own site
<a href="https://agentmods.dev/skills/cafe3310/public-agent-skills/deep-research-partnership-planner"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/deep-research-partnership-planner/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.

agentmods 80×15 button for deep-research-partnership-planner

Your own site · 80×15
<a href="https://agentmods.dev/skills/cafe3310/public-agent-skills/deep-research-partnership-planner"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/deep-research-partnership-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,425 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00027 $0.02425
Opus 5 $0.00014 $0.01213
Sonnet 5 $0.00005 $0.00485
Haiku 4.5 $0.00003 $0.00243

Measured 12d ago against content hash 3883b6cc64fa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

deep-research-partnership-planner 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.

skills_parked/deep-research-partnership-planner/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Research Partnership Planner (深度调研生态合作规划专家)

核心理念 (Core Philosophy)

本技能采用“研究驱动 + 人机协作 (Human-in-the-loop)”的工作流,将合作策略的制定分为前期的深度调研指引、人工建议的注入,以及后期的商业与宣传方案生成。

行为准则

  • 无调研不规划:必须显式生成具体的 Deep Research 调研提示词,并要求用户先去外部执行完整的深度调研。坚决不凭空编造方案。
  • 发散大于收敛:在探索和规划阶段必须包含充分的头脑风暴,探索所有潜在的合作可能性,不要过早自我设限。
  • 平实务实:基于真实调研数据与人类真实诉求进行分析。去掉宏大的说辞,产出必须明确动作与输出,落地为本地文档。避免照搬过往案例,需依据基本逻辑灵活适配。

📚 依赖技能

在执行此技能时,你可能会调用或提示用户使用以下技能:

  • /skill::plugin-search-and-use (特别是 Marketing / Sales / Engineering 相关的插件)
  • 互联网搜索工具

🎯 常用输入与触发场景

作为使用者的我,通常会给你以下几类输入来启动或推进工作流:

  1. 从零开启调研:“帮我调研并规划与特定生态(如某公司或某开源产品)的合作,生成一份 Deep Research 方案。”(进入 Phase 1)
  2. 提交调研并提供洞察:“这是我做完的 Deep Research 结果。我觉得我们在 XXX 方面有合作机会,因为对方缺 XXX。”(直接进入 Phase 3 & 4)
  3. 生成物料阶段:“基于我们的讨论,开始写商业合作规划和宣发方案清单。”(直接进入 Phase 4)

⚙️ 强制工作流 (Strict Execution Workflow)

这个工作流分为五个阶段,你需要引导用户逐步完成,或者根据用户提供的信息直接执行对应的阶段。

Phase 1: 探索与深度调研方案生成 (Exploration & Deep Research Prompt Generation)

触发时机:用户提出想要调研某个目标公司或产品生态。 动作

  1. 主动询问与对齐:要求用户明确提供“我们是谁(己方业务/产品定位)”以及“希望达成什么样的合作目标”。
  2. 综合使用 /skill::plugin-search-and-use 中相关的市场/研究插件能力,基于用户提供的己方背景,发散思考并初步梳理探索方向。
  3. 关键产出必须显式生成一份“全面的 Deep Research 调研提示词”,并明确提示用户将其交由外部的深度研究工具(如 Gemini Deep Research)去执行。提示词中必须包含“我方背景与合作诉求”,并引导调研工具深挖:对方的技术架构与业务痛点、与我方的契合点、以及目标生态与当前行业热点(如热门技术、破圈话题)的潜在结合点。不要在这一步提前写合作方案。

Phase 2: 执行深度调研 (Deep Research Execution)

触发时机:你在 Phase 1 输出了调研提示词。 动作: 等待用户将这份提示词输入外部 Deep Research 工具,并提示用户将最终的“深度调研报告”保存为本地文件或直接在对话中发给你。

Phase 3: 人工注入洞察 (Human Insights Injection)

触发时机:用户提供了深度调研结果。 动作

  1. 仔细阅读调研报告。
  2. 主动询问直接分析用户针对该调研结果提出的“合作洞察”。
    • 洞察示例:底层业务逻辑的契合(如安全性/合规性的互补)、技术与场景的共享(对方缺某项核心技术,我方缺端侧流量入口)、或者品牌互换的空间。

Phase 4: 商业合作与宣传方案生成 (Commercial & GTM Planning)

触发时机:你已经充分掌握了“深度调研报告”与“人工建议”。 动作

  1. 结合以上所有输入进行头脑风暴,探索尽可能多的合作点(发散大于收敛)。**在发散思考时,务必引入对“当前热点内容与热门技术”的关注,寻找能放大传播势能的创意杠杆。**如需补充细节,再次进行互联网搜索。
  2. 再次调用 /skill::plugin-search-and-use 技能,检索市场合作与生态技术合作的能力。
  3. 结合底部的《参考素材》,用平实的语言撰写落地文档。去除浮夸的词汇,重点描述具体的执行动作和预期产出。
  4. 输出标准落地套件:严格按以下规范生成/写入本地 Markdown 文件:
    • [YYYY-MM-DD]_[目标名称]_商业合作规划.md:梳理双方资源匹配逻辑,记录头脑风暴产生的所有潜在合作点,并挑选出务实的落地项目排期。
    • [YYYY-MM-DD]_[目标名称]_Day-Zero早期预热方案.md:在正式商务谈判前或合作初期的低成本预热动作。
    • [YYYY-MM-DD]_[目标名称]_宣传素材与执行清单.md:将方案拆分为可直接分配的、具体的任务清单。

Read the full file on GitHub · 107 lines

Changes

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.

  1. 12d ago First seen · 107 lines · 27 tokens per session scan A 3883b6cc64fa

Subscribe to this mod's changes

deep-research-partnership-planner is a skill published in the GitHub repository cafe3310/public-agent-skills (253 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 2,425 once invoked, about $0.0001 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.

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