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Industry Research

作者 Mario Karras · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install industry-research
功能描述
When the user wants to conduct industry research, keyword research for a campaign, search demand analysis, intent mapping, audience research, or understand w...
使用说明 (SKILL.md)

Industry Research

You conduct deep, intent-driven industry research. The core question is "what are people looking for?" -- not "what are competitors doing?" Keyword intent, search demand, and real audience language are the primary signals. Competitor analysis serves the intent research by finding gaps in what's being served.

Before Starting

Check for product marketing context first: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Then determine:

  1. Client name -- Which client to research for
  2. Industry/market -- The market category and key services/products
  3. Seed keywords -- 5-8 starting keyword phrases reflecting core services
  4. Competitor URLs -- 3-5 known competitor websites to analyze
  5. Geographic focus -- Target region for keyword data (default: US)

If dispatched via cron or orchestrator with a specific client name, use the product-marketing-context for that client to derive seed keywords and competitors automatically.


Workflow

Step 1: Keyword Research (Ahrefs)

Use Ahrefs to gather keyword data. Try the Ahrefs MCP server first (if available via mcporter or MCP tools list). If MCP is not available, fall back to the Ahrefs REST API at https://api.ahrefs.com/v3 with Authorization: Bearer $AHREFS_API_KEY.

Regardless of access method, gather data from these endpoints/capabilities:

Keywords Explorer overview -- seed keywords (batch up to 10 per request) for volume, difficulty, traffic potential:

POST /keywords-explorer/overview
Body: { "keywords": ["seed1", "seed2", ...], "country": "us" }

Keywords Explorer matching terms -- for each seed keyword, find related keywords (limit: 100 results per seed):

POST /keywords-explorer/matching-terms
Body: { "keyword": "seed keyword", "country": "us", "limit": 100 }

Keywords Explorer related terms -- semantically similar keywords:

POST /keywords-explorer/related-terms
Body: { "keyword": "seed keyword", "country": "us", "limit": 100 }

Cluster results by topic (group keywords sharing the same parent topic or SERP overlap).

Classify each keyword's intent:

  • Informational -- how/what/why questions, guides, educational content
  • Transactional -- near me, cost, buy, hire, service, pricing queries

Rate limit: Max 60 requests/min. Budget cap: 15 API calls per research run.

If neither Ahrefs MCP nor AHREFS_API_KEY is available, skip this step and note "Ahrefs data unavailable -- no MCP server or API key configured" in the artifact. Continue with Firecrawl+Exa only.

Step 2: Audience Research (Firecrawl + Exa)

Use exa.js search to find Reddit threads, forum posts, Quora answers about the industry/problem space:

exa.js search "[industry] questions problems reddit" --num-results 10
exa.js search "[industry] advice forum" --num-results 10
exa.js search "site:reddit.com [service] experience" --num-results 10

Use firecrawl.js scrape to extract content from top 5 most relevant results:

firecrawl.js scrape --url "https://reddit.com/r/relevant-thread"

Extract:

  • Exact questions people ask
  • Pain points in their own words
  • Emotional language
  • Common objections

Cross-reference with Google's People Also Ask (search for each seed keyword via Exa and extract PAA-style questions):

exa.js search "[seed keyword] questions people also ask" --num-results 5

Step 3: Content Gap Analysis

For each top keyword cluster, use exa.js search to find what currently ranks:

exa.js search "[keyword]" --num-results 10

Use firecrawl.js scrape on top 3 ranking pages per cluster to analyze content depth:

firecrawl.js scrape --url "https://top-ranking-page.com/article"

Identify gaps:

  • Topics with search demand but weak/missing/outdated content from competitors
  • Flag underserved angles -- queries where top results are generic directories (Yelp, WebMD) rather than authoritative guides
  • Note city-specific opportunities if geographic focus applies

Step 4: Competitor Landscape

Use firecrawl.js map on each competitor URL to discover their site structure:

firecrawl.js map --url "https://competitor.com"

Use firecrawl.js scrape on their key pages (homepage, services, blog, pricing) -- max 5 pages per competitor:

firecrawl.js scrape --url "https://competitor.com/services"

Analyze:

  • Positioning/messaging
  • Content strategy (blog frequency, topics)
  • SEO approach (city pages, programmatic content)

If Ahrefs is available (MCP or API), use Site Explorer organic-keywords to see what keywords competitors rank for:

GET /site-explorer/organic-keywords?target=competitor.com&limit=50

Identify messaging patterns and gaps -- what positioning angles are unclaimed.

Step 5: Compile Artifact

Write the output to .agents/industry-research-{client}.md where {client} is the lowercase client name (e.g., allcare).

Use this artifact template:

# Industry Research: {Client Name}

*Client: {Client Full Name}*
*Last full refresh: YYYY-MM-DD*

## 1. Keyword Clusters & Intent Map

*Last researched: YYYY-MM-DD*

### Cluster: {Topic Name}
| Keyword | Monthly Volume | Difficulty | Intent | Traffic Potential |
|---------|---------------|------------|--------|-------------------|
| keyword phrase | X,XXX | XX | Informational/Transactional | X,XXX |

**Intent distribution:** X% informational, X% transactional
**Primary opportunities:** Summary of top keyword opportunities

## 2. Questions & Pain Points

*Last researched: YYYY-MM-DD*

### What people ask (from PAA, Reddit, forums)
- "Exact question from audience?" (volume: X,XXX)

### Pain points (exact audience language)
- "Verbatim quote from real person" -- Source (Reddit, forum, etc.)

## 3. Content Gaps & Opportunities

*Last researched: YYYY-MM-DD*

### Underserved angles
- **Gap description:** Why it matters and the opportunity

### What's ranking (and what's weak)
| Query | Top Result | Gap/Opportunity |
|-------|-----------|-----------------|
| search query | Current top result | What's missing or weak |

## 4. Competitor Landscape

*Last researched: YYYY-MM-DD*

### Who ranks for our keywords
| Competitor | Ranks For | Positioning | Content Strategy |
|-----------|-----------|-------------|------------------|
| Competitor name | Key keywords | How they position | Blog, city pages, etc. |

### Messaging patterns
- Competitor: "Their messaging angle" -- framing type
- **Gap:** Unclaimed positioning angle

Target artifact size: 3,000-5,000 words. Synthesize and distill -- do not dump raw scraped content.

Each section has its own Last researched: timestamp so consuming skills can verify recency.


Tips

  • Focus on intent, not just volume -- a 500-volume transactional keyword outperforms a 5,000-volume informational one for conversions
  • Capture exact audience language -- "my mom can barely get to the doctor" is more valuable than "transportation barriers to healthcare access"
  • Budget API calls carefully -- 15 Ahrefs calls per run, scrape selectively (use firecrawl.js map before scrape)
  • Keep the artifact scannable -- downstream skills read specific sections, not the whole document

Related Skills

  • competitive-intelligence -- For detailed competitor analysis (company-level deep dives)
  • market-research -- For market sizing, TAM/SAM/SOM, and industry trends
  • firecrawl-cli -- For raw Firecrawl scraping (detailed tool documentation)
  • exa-company-research -- For raw Exa web search on specific companies
  • product-marketing-context -- For foundational product/service context that feeds into research
安全使用建议
Do not install blindly — the skill's instructions expect an Ahrefs API key and external CLIs (exa.js, firecrawl.js, and optional MCP tools) but the manifest doesn't declare them. Before using: (1) Confirm whether you want to provide AHREFS_API_KEY and issue a limited-scope token if possible; (2) Ensure exa.js and firecrawl.js (and mcporter/MCP if used) are installed from trusted sources, or update the manifest to include explicit install instructions; (3) Review any .agents/product-marketing-context.md or .claude/product-marketing-context.md files the agent may read for sensitive data and remove secrets; (4) Be aware the skill will write output to .agents/industry-research-{client}.md — verify that location is acceptable; (5) Ask the maintainer to update the skill metadata to list required binaries, required env vars (AHREFS_API_KEY), and any config paths, or decline installation until those inconsistencies are resolved. If you cannot verify these items, run the skill in an isolated/sandboxed environment with least-privilege credentials.
功能分析
Type: OpenClaw Skill Name: industry-research Version: 1.0.0 The industry-research skill is a legitimate tool designed for SEO and market analysis. It orchestrates data collection from Ahrefs, Firecrawl, and Exa to generate research artifacts. The workflow involves standard API usage and local file persistence within the .agents/ directory, with no evidence of malicious intent, data exfiltration, or unauthorized execution logic in SKILL.md or _meta.json.
能力评估
Purpose & Capability
The SKILL.md explicitly orchestrates Ahrefs, Firecrawl, and Exa (including mcporter/MCP usage and Ahrefs REST API), which legitimately require an AHREFS_API_KEY and presence of exa.js/firecrawl.js/built tools. The registry metadata, however, lists no required binaries, no required env vars, and no install spec. That mismatch is disproportionate and unexplained.
Instruction Scope
Runtime instructions instruct the agent to (1) read local files (.agents/product-marketing-context.md or .claude/product-marketing-context.md) if present, (2) run external commands (exa.js, firecrawl.js, mcporter/MCP), (3) call Ahrefs endpoints with Authorization: Bearer $AHREFS_API_KEY, and (4) write an output artifact into .agents/industry-research-{client}.md. Reading local context files and calling external tooling is reasonable for research, but the instructions reference resources (env var and binaries) not declared in the skill metadata and thus grant the agent broad, vaguely-specified discretion.
Install Mechanism
This is an instruction-only skill (no install spec), which is low-risk in isolation. However, the instructions depend on third-party CLIs (exa.js, firecrawl.js) and optionally an MCP server; because there's no install spec, the skill assumes those tools already exist on the host. That implicit dependency increases operational risk and should be made explicit in the manifest.
Credentials
The SKILL.md requires using AHREFS_API_KEY (Authorization: Bearer $AHREFS_API_KEY) if MCP is unavailable, but the skill metadata lists no required environment variables or primary credential. This is a clear mismatch — a secret is referenced at runtime but not declared. The skill may also access local context files which can contain sensitive information; those accesses are not declared as config paths.
Persistence & Privilege
always is false and the skill does not request persistent system privileges. It will read local product-marketing-context files and write an artifact to .agents/industry-research-{client}.md; this is reasonable for its purpose but does constitute file read/write access to the agent workspace and may expose sensitive client data. The skill does not modify other skills or global agent settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install industry-research
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /industry-research 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of the industry-research skill: - Provides a structured workflow for deep, intent-driven industry and keyword research using Ahrefs, Firecrawl, and Exa tools. - Supports keyword clustering, search intent classification, audience question mining, pain point extraction, and SERP/content gap analysis. - Integrates competitor research and positioning analysis, aligning with specific campaign and market needs. - Outputs a comprehensive Markdown artifact summarizing keyword clusters, audience insights, content gaps, and competitor landscape. - Includes clear fallback logic and instructions for cases where specific data sources (e.g., Ahrefs API) are unavailable.
元数据
Slug industry-research
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Industry Research 是什么?

When the user wants to conduct industry research, keyword research for a campaign, search demand analysis, intent mapping, audience research, or understand w... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 152 次。

如何安装 Industry Research?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install industry-research」即可一键安装,无需额外配置。

Industry Research 是免费的吗?

是的,Industry Research 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Industry Research 支持哪些平台?

Industry Research 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Industry Research?

由 Mario Karras(@mariokarras)开发并维护,当前版本 v1.0.0。

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