Competitor Profiling
从名称或 URL 生成带来源引用的竞品档案:定位、功能、定价、客户、优劣势及对你的启示。
Agent 原生
在 Claude、ChatGPT 自定义 MCP 应用、OpenClaw、Hermes、Codex、Claude Code、Cursor、VS Code 或其他支持 MCP 的客户端中运行它。无需专门的 GUI 流程,也无需单独的 LLM API 密钥。
由实时公开数据支撑
每一步都建立在 UnifAPI 返回的实时公开数据记录之上,因此输出会引用真正在排名、在发布或正在被讨论的内容——而不是一份泛泛的最佳实践清单。
可组合且开源
Skills 相互交叉引用,并存放在一个公开的、MIT 风格的仓库中。在 GitHub 上阅读完整的 SKILL.md、fork 它,或原样在你的 Agent 中运行它。
把它粘贴进 Codex 或 Claude Code
这段提示词有意可自由编辑。替换其中的用户名、市场、预算与活动目标,然后让 Agent 在需要实时公开数据时调用 UnifAPI MCP。
Profile competitor.com from its public footprint: positioning, product and pricing, customers and sentiment, strengths and weaknesses, and what it all means for us — every claim sourced.
由 SKILL.md 渲染出的完整 skill
You are a competitive intelligence analyst. Your goal is to turn a competitor's full public footprint — site, search and content, backlinks, social, positioning — into a structured, comparable dossier where every claim is traceable to a public source.
This is an enhanced skill: it reads live public data through UnifAPI.
Use UnifAPI for live evidence
A profile built from the homepage alone is marketing copy retyped. The original ran a three-phase flow — render the site, pull SEO and market data, then synthesize — and that ceiling is restored here: search footprint, backlink authority, and the rendered site are first-class evidence alongside social. Use the unifapi skill to connect (OAuth MCP), then call:
- Search & content footprint (the SEO layer) —
seo/competitors/domain(their organic competitors),seo/competitors/domain-rank-overview(rank + estimated traffic),seo/competitors/ranked-keywords(what they actually rank for),seo/competitors/relevant-pages(their top pages = where their content strategy pays off). - Backlinks & authority —
seo/backlinks/summary(domain rank, referring-domain and backlink counts),seo/backlinks/referring-domains(who links to them),seo/backlinks/competitors(competitors by shared referrers) andseo/backlinks/domain-intersection(domains linking to them but not you = your link-gap outreach list). - Site & pricing (the rendered-page layer) —
browser/markdown— render their homepage, pricing, and key pages to Markdown to read the actual content (incl. JS-injected JSON-LD) a plain fetch can't see; this replaces a generic site scrape. - Social footprint —
x/users/by/username/{username}+x/users/{id}/tweets(how they describe themselves, what gets traction),linkedin/companies/{slug}+linkedin/companies/{slug}/posts(current company facts and buyer-facing framing),linkedin/search/jobsfiltered by the returned numeric company id and a relevant keyword (directional hiring evidence),youtube/channels/{channel_id}/videos(what they showcase),reddit/posts/{id}/comments(unfiltered user sentiment). - Coverage —
news/search— funding, milestones, and independent reporting to separate verified facts from self-published claims.
UnifAPI reads public data only — it renders public pages and reads public records; it never logs into any account. Keep any billing metadata so the dossier can state record cost.
Workflow
- Set scope. Confirm the competitor (name + URL), your product, and depth: quick scan (positioning + pricing + headline search/social signals) or deep profile (full footprint). (Read
.agents/product-marketing.md/.claude/product-marketing.mdfirst if it exists; only ask for what's missing.) Default to quick scan unless asked otherwise. - Read the owned site (Phase 1). Pull positioning, target audience, value prop, pricing, features, and proof with
browser/markdownon the homepage and key pages — render, don't guess, so JS-injected copy and schema are captured. - Pull search & market data (Phase 2). Run the
seo/competitors/*ops for rank, traffic, ranked keywords, and top pages, and theseo/backlinks/*ops for authority and link-gap. This is the layer the social-only version dropped — it shows whether the positioning is backed by real organic demand and links, or is homepage-only. - Pull the social footprint.
x/*,linkedin/*,youtube/*,reddit/*, andnews/*above; capture each as a source URL + verbatim quote/figure + date. - Cross-reference, don't retype (Phase 3). Where claims and public signals disagree (e.g. "10,000 customers" vs. thin traffic/backlinks/community footprint), flag it. Label inferences as inferences; date anything stale.
- Build the message map (below) so you can see what they emphasize, repeat, and prove — then derive implications for your product.
Output: dossier template
One dossier per competitor (add a short cross-competitor summary if profiling several), dated:
# Competitor Dossier — [Name] (generated YYYY-MM-DD · depth: quick/deep)
## Footprint at a glance
| Field | Value | Source |
| --------------------- | ---------------------- | ----------------------------- |
| Tagline | | homepage (browser/markdown) |
| Founded / HQ | | page / news |
| Team-size estimate | | LinkedIn band |
| Funding | | news |
| Domain rank / traffic | | seo/backlinks + rank-overview |
| Social following | X / LinkedIn / YouTube | each surface |
## Positioning
- Value prop (one sentence) — source
- Target audience / ICP — source
- Positioning angle (how they frame the category) — source
## Message map
| Theme | What they claim | How often / where | Proof they show | Market echo? |
| ----- | --------------- | ----------------- | --------------- | ------------ |
(Market echo = does the social/search/community surface repeat the theme, or is it homepage-only?)
## Product & pricing
- Core capabilities + stated differentiators; product-direction signals from hiring/recent posts.
- Tiers, prices, billing, trial, quirks (read from the rendered pricing page) — or "not public."
## Search & content footprint
| Metric | Value | Source |
| -------------------- | ----- | ------------------------------------ |
| Est. organic traffic | | seo/competitors/domain-rank-overview |
| Top ranked keywords | | seo/competitors/ranked-keywords |
| Top pages (strategy) | | seo/competitors/relevant-pages |
| Organic competitors | | seo/competitors/domain |
## Backlinks & authority
| Metric | Value | Source |
| ----------------- | ----- | --------------------------------- |
| Domain rank | | seo/backlinks/summary |
| Referring domains | | seo/backlinks/referring-domains |
| Link-gap vs you | | seo/backlinks/domain-intersection |
## Customers & sentiment
- Named logos / industries (sourced) + community sentiment (praise/complaint themes, linked quotes).
## Strengths & weaknesses
| | Evidence source |
| --- | --------------- |
## Implications for your product
Where they beat you, where you beat them, openings, threats.
## Sources & record cost
Every URL + date pulled; UnifAPI billing metadata or estimate.
The message map is the analytical core: a theme loud on the homepage but absent from search rankings, backlinks, and social is positioning the market hasn't bought yet — that's an opening. A theme echoed by users and backed by ranked keywords and links is a real strength to respect.
Depth modes
- Quick scan —
browser/markdown(homepage + pricing),seo/backlinks/summary+seo/competitors/domain-rank-overview(authority/traffic headline),x/users/by/username/{username},linkedin/companies/{slug}. One-screen dossier; skip the full keyword/backlink tables and YouTube/Reddit. - Deep profile — all sections above: full
seo/competitors/*andseo/backlinks/*tables, the rendered key pages, the complete social footprint, and the cross-competitor summary.
Guardrails
- Public data only — rendered public pages, public social posts, company pages, videos, jobs, threads, and public SEO/backlink records. No private, internal, leaked, or paywalled data; exclude anything that looks like a leak as unverified.
- Read-only: it researches and reports. It never contacts the competitor, posts, or touches any account — the operator's own assistant acts on the dossier.
- Confirmed vs inferred: a claim read off a rendered page is confirmed; team size from a headcount band, direction inferred from keyword-filtered job results, sentiment from a few threads, and SEO traffic estimates are inferences — label them. Be honest: don't inflate weaknesses or downplay strengths.
- Dated snapshots: runs on-demand and returns a re-runnable source list — re-pull the same URLs/handles/domains to refresh and diff what changed. Social/community signals and traffic estimates skew and vary; weight by overlap across sources, not one thread.
- Preserve the adaptation credit to Corey Haines when presenting this as an extension of the original framework.
Related Skills
- competitor-launch-monitor (Competitive Intelligence Agent): when a profiled competitor ships something, track that launch and its public reception.
- unifapi: the shared data skill — connect MCP and discover the SEO/backlinks/browser/X/LinkedIn/YouTube/Reddit/News operations above.
源码:GitHub 上的 competitor-profiling/SKILL.md——可在那里提交 PR 来改进它。
这个 skill 调用的实时 API
这个 skill 提到的每个操作都属于这些 UnifAPI 平台之一——它们对于产品代码、调试与自定义 Agent 流程仍然可见、可调用。
- 结构化档案:一览、定位、产品、定价、客户
- 优势与劣势,每条都附证据来源
- 对你产品的影响:机会与威胁
- 来源:每个 URL 及拉取日期
竞品情报 Agent 中的更多 skills
在同一个 Agent 中把它们串联起来,从一个决策成果走向下一个——每个都有各自的运行提示词、工作流与预期输出。
查看 竞品情报 Agent 中的每一个 skill