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 Chris1Wang3/Idea-on-Trial --skill competitive-product-researchgit clone --depth 1 https://github.com/Chris1Wang3/Idea-on-TrialWrote 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/chris1wang3/idea-on-trial/competitive-product-research)<a href="https://agentmods.dev/skills/chris1wang3/idea-on-trial/competitive-product-research"><img src="https://agentmods.dev/badge/skills/chris1wang3/idea-on-trial/competitive-product-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/chris1wang3/idea-on-trial/competitive-product-research"><img src="https://agentmods.dev/badge/skills/chris1wang3/idea-on-trial/competitive-product-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 | $0.00072 | $0.02089 |
| Opus 5.5 | $0.00029 | $0.00836 |
| Sonnet 5.5 | $0.00014 | $0.00418 |
| Haiku 4.5 | $0.00007 | $0.00209 |
Grade A, and why
competitive-product-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 1mo 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
竞品调研 · Competitive Product Research
EN Dual-track benchmarking + strategy → source-traceable HTML / Markdown (
SRC-xxx).
中文 体验对标 + 战略诊断 → 证据可溯源 HTML / Markdown 报告。
When / 何时用： 对标 · 差异化 · SWOT/五力/PESTLE · 评审前材料
Not / 不用： 拍脑袋市场规模 · 纯概念战略 · 违规采集 · 替用户拍板
Our app post conversion is 3% — benchmark Xiaohongshu vs Instagram, first-post funnel.
我们 App 发帖转化率 3%，对标小红书与 Instagram 分析首链路。
也可「帮我做 XX 和 YY 竞品调研」/ "benchmark X against Y" — 先清单，再报告。
可直接触发的说法
以下口语输入都应触发本技能，不要求用户先按模板填写：
- 帮我拉一版 XX 和 YY 的竞品对标。
- 我们这个功能和小红书/抖音比差在哪？
- 对标一下竞品的首单/发布/开户/投顾链路。
- 我只有几张截图，先帮我做一版体验差异分析。
- 这个 PRD 评审前需要一份竞品材料。
- 帮我判断这个赛道里我们该学谁、不该学谁。
- 竞品最近这么做背后的策略是什么？
- 给我一份能给老板看的竞品 HTML 报告。
最小可用输入
最低可启动：调研目标 + 至少 1 个明确竞品。若只有 1 个竞品，先询问是否需要补第 2 个；用户不补时，可用“我方方案/行业常见做法”作为对照，并在报告中标注对照口径。
推荐输入：调研目标、对标对象、我方现状、核心场景、报告用途、输出格式、可用材料（链接/截图/PRD/数据）。
可兼容输入：
- 一句话需求：先提炼目标、对象、场景，再给采集清单。
- 截图/链接/碎片材料：先建证据索引，缺口标
SRC-GAP。 - 中英混合或口语输入：保持用户原意，统一为调研参数。
- 用户说“直接生成”：只跳过业务信息追问，用保守假设继续，并在报告首屏标注假设；不视为跳过输出格式确认。
工作流
1) 提交调研需求 2) 信息采集清单 3) 问题对齐 + 证据建库
4) 双轨分析（体验八维 + 战略四件套） 5) 按用户选择生成 HTML / Markdown 报告
信息采集清单（第 2 步）
预填 +「待补充」；可说「跳过，直接生成」。
1️⃣ 调研目标 2️⃣ 对标对象（至少 1 个，推荐 ≥2） 3️⃣ 我方现状 4️⃣ 核心场景 5️⃣ 行业
6️⃣ 补充材料 7️⃣ 战略模块（格局/SWOT/五力/PESTLE） 8️⃣ 报告用途（内部/对外脱敏）
9️⃣ 输出格式（HTML / Markdown） 🔟 约束条件
采集交互（优先使用可操作表单）：
- 必须复用 assets/intake-form.html，不得临时重写表单 UI。
- 将上下文预填为
prefillURL 参数（URI 编码 JSON）；推断值不得伪装成用户确认值，输出格式保持未选。 - 宿主支持内嵌 HTML 时直接展示；否则复制该资产到当前输出目录并用浏览器打开。提交后读取宿主回传或用户粘贴的结构化参数。回传统一使用
{schema_version:"1.0", skill, action, data}；优先window.codex.submitForm，兼容window.openai.sendFollowUpMessage，最后复制 JSON。 - 仅当宿主无法展示或打开 HTML 时退回文本清单；格式未确认不得生成，用户明确委托默认时使用 HTML。
方法概要
双轨四层法详规 → research-playbook.md
- 体验轨（八维）：竞品怎么做、我方差在哪 → D1–D8，先拆最小操作节点
- 战略轨（可选）：赛道为何如此、我方怎么打 → 格局 · SWOT · 五力 · PESTLE
- 证据：每条关键结论挂
SRC-xxx（U 用户 / P 公开 / H 经验须标验证）
输出
按采集表确认的格式生成：HTML 严格使用 report-template-pro.html；Markdown 使用同一分区结构。格式锁、事实边界、文风、自检 → research-playbook.md。
What ships with it
5 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.
- 1mo ago First seen · 126 lines · 72 tokens per session scan A bee736b7baaa
competitive-product-research is a skill published in the GitHub repository Chris1Wang3/Idea-on-Trial (4 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 2,089 once invoked, about $0.0003 per session on Opus 5.5. 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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