Goose-skills industry-scanner

install

source · Clone the upstream repo

git clone https://github.com/gooseworks-ai/goose-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/gooseworks-ai/goose-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/composites/industry-scanner" ~/.claude/skills/gooseworks-ai-goose-skills-industry-scanner && rm -rf "$T"

manifest: skills/composites/industry-scanner/SKILL.md

Industry Scanner

Daily deep-research agent that scans the internet for everything relevant to a client's industry, then generates strategic GTM opportunities based on what it finds.

Quick Start

Run an industry scan for <client>. Use the config at clients/<client>/config/industry-scanner.json.

Or for a weekly deeper scan:

Run a weekly industry scan for <client> with --lookback 7.

Inputs

Client name — determines which config and context files to load
Lookback period (optional) —
```
1
```
for daily (default),
```
7
```
for weekly deep scan
Focus area (optional) — limit scan to specific categories (e.g., "competitors only", "events only")

Step-by-Step Process

Phase 1: Load Configuration

Read
```
clients/<client>/config/industry-scanner.json
```
— this contains all the keywords, sources, competitors, and URLs to scan
Read
```
clients/<client>/context.md
```
— need the ICP, value props, and positioning to generate relevant strategies
Set the lookback period: use
```
1
```
day for daily scans,
```
7
```
for weekly, or whatever the user specifies
Note today's date for the output filename

If no client config exists, ask the user for the key inputs and offer to create one from the example at

skills/industry-scanner/config/example-config.json

Phase 2: Data Collection

Run these data sources in parallel where possible. Skip any source that isn't configured. For each source, use the existing skill's CLI or tool as documented.

IMPORTANT: Run as many of these bash commands in parallel as possible to minimize total scan time. Sources are independent of each other.

2A. Web Search (built-in WebSearch tool)

Run 5-8 web searches combining the configured

web_search_queries

with time-sensitive modifiers. Examples:

```
"<industry keyword> news this week"
```

"<competitor name> shutdown OR closing OR acquired 2026"

"<industry> conference 2026 speaker applications"

"<industry keyword> new regulation OR policy change"

"<competitor name> layoffs OR pivot OR rebrand"

Also search for each competitor name directly to catch any recent news.

2B. Industry Blogs & Publications

python3 skills/blog-feed-monitor/scripts/scrape_blogs.py \
  --urls "<comma-separated blog_urls from config>" \
  --days <lookback> --output json

Read

skills/blog-feed-monitor/SKILL.md

for full CLI reference.

2C. Reddit

For each configured subreddit, run:

python3 skills/reddit-post-finder/scripts/search_reddit.py \
  --subreddit "<comma-separated subreddits from config>" \
  --keywords "<comma-separated reddit_keywords from config>" \
  --days <lookback> --sort hot --output json

Also run a separate search with

--sort top --time week

to catch high-engagement posts.

Read

skills/reddit-post-finder/SKILL.md

for full CLI reference.

2D. Twitter/X

For each configured Twitter query:

python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
  --query "<twitter_query>" \
  --since <yesterday-YYYY-MM-DD> --until <today-YYYY-MM-DD> \
  --max-tweets 30 --output json

Read

skills/twitter-mention-tracker/SKILL.md

for full CLI reference.

2E. LinkedIn

Search each configured LinkedIn keyword via the linkedin-post-research skill.

Use

RUBE_SEARCH_TOOLS

to find

CRUSTDATA_SEARCH_LINKED_IN_POSTS_BY_KEYWORD

, then search each keyword with

date_posted: "past-day"

(or

"past-week"

for weekly scans).

Read

skills/linkedin-post-research/SKILL.md

for the full Rube/Crustdata workflow.

2F. Hacker News

python3 skills/hacker-news-scraper/scripts/search_hn.py \
  --query "<hn_query>" --days <lookback> --output json

Run once per configured

hn_queries

entry. Read

skills/hacker-news-scraper/SKILL.md

for full CLI reference.

2G. RSS News Feeds

If the client has an accounting-news-monitor (or similar) configured:

python3 skills/accounting-news-monitor/scripts/monitor_news.py \
  --new-only --days <lookback> --output json

Read

skills/accounting-news-monitor/SKILL.md

for full CLI reference.

2H. Newsletter Inbox

If the client has newsletter monitoring configured:

python3 skills/newsletter-monitor/scripts/scan_newsletters.py \
  --days <lookback> --output json

Read

skills/newsletter-monitor/SKILL.md

for full CLI reference.

2I. Review Sites

For each configured review URL:

python3 skills/review-site-scraper/scripts/scrape_reviews.py \
  --platform <platform> --url "<review_url>" \
  --days <lookback> --max-reviews 20 --output json

Read

skills/review-site-scraper/SKILL.md

for full CLI reference.

Phase 3: Consolidate & Categorize

After all data collection completes, consolidate the results:

Deduplicate — items appearing across multiple sources (e.g., a news story on both a blog and Reddit). Keep the richest version but note multi-source appearance (higher signal).
Categorize each item into one of these types:

Category	What to Look For
Competitor News	Shutdowns, launches, funding, pivots, negative reviews, leadership changes, pricing changes
Industry Events	Upcoming conferences, webinars, meetups, speaker slots, CFPs, award nominations
Market Trends	Viral discussions, hot topics, emerging themes, sentiment shifts, adoption data
Regulatory / Policy	New regulations, compliance changes, government actions, standards updates
People Moves	Key hires, departures, promotions at competitors or target companies
Technology	New product launches, integrations, platform changes, deprecations
Funding / M&A	Acquisitions, mergers, funding rounds, PE investments, IPO signals
Pain Points	People publicly complaining about problems the client solves
Content Opportunities	Trending content, viral posts, gaps in existing coverage, unanswered questions

Rate relevance — High / Medium / Low based on how directly it relates to the client's ICP and value props.
Filter out noise — Drop items rated Low relevance unless they're genuinely noteworthy. The goal is signal, not volume.

Phase 4: Generate Strategic Opportunities

Review the consolidated intelligence and identify items (or clusters of related items) that present genuine GTM opportunities.

CRITICAL: Do NOT force-fit a strategy for every item. Many items are just "good to know" — that's fine, they go in the intelligence briefing. Only generate strategy ideas where there is a real, actionable opportunity that could meaningfully impact growth.

For each genuine opportunity, produce:

Field	Description
Trigger	What happened — the intelligence item(s) that sparked this idea
Strategy	What to do about it — specific and actionable, not vague
Tactics	2-4 concrete next steps with skill references where applicable
Urgency	`Immediate` (do this today/this week), `Soon` (next 2 weeks), or `Evergreen`
Effort	`Low` (1-2 hours), `Medium` (half day), `High` (multi-day project)
Expected Impact	Why this could matter — who it reaches, what it could generate

Strategy Patterns to Draw From

Use these as inspiration, not as a checklist. Match the pattern to the trigger:

Competitor in trouble (shutdown, bad reviews, layoffs, pivot):

Publish a migration/comparison guide targeting their customers
Find their customers via review sites, LinkedIn posts mentioning them → outreach
Engage on social posts where people discuss the shutdown/issues
Create "alternative to X" content for SEO capture

Skills:

web-archive-scraper

(recover their customer list),

review-site-scraper

(find reviewers),

linkedin-post-research

(find posts about them),

cold-email-outreach

Industry event coming up:

Apply to speak (if speaker slots are open)
Plan pre-event outreach to attendees (skill:
```
luma-event-attendees
```
or
```
conference-speaker-scraper
```
)
Create event-specific content (e.g., "What We're Watching at [Event]")
Plan on-site presence and follow-up campaign

Viral post or trending discussion:

Engage thoughtfully on the thread (LinkedIn comment, Reddit reply, tweet)
Create response content (blog post, LinkedIn post) with the client's expert take
If the poster is ICP, follow up directly

Skills:

linkedin-post-research

company-contact-finder

Acquisition or merger announced:

Reach out to the acquired company's clients (they're in transition, open to alternatives)
Create content about what the acquisition means for the industry

Skills:

web-archive-scraper

(find client lists),

company-contact-finder

New regulation or policy change:

Create educational content positioning the client as an expert
Direct outreach to companies affected by the change
Host a webinar or publish a guide about compliance

Pain point surfaced (Reddit complaint, negative review, LinkedIn vent):

Engage helpfully on the post (don't pitch — add value first)
If the poster is ICP, follow up with a direct message/email
Create content addressing the specific pain point
Skills:
```
company-contact-finder
```

Trending topic or content gap:

Publish thought leadership content while the topic is hot
CEO/founder LinkedIn post with a unique take
Podcast or webinar on the trending topic

Funding round announced at target company:

Outreach to the company (post-raise = budget for new tools)

Skills:

company-contact-finder

cold-email-outreach

Phase 5: Generate Output

Save the report to the current working directory as

industry-scan-<YYYY-MM-DD>.md

(or user-specified path) using this structure:

# Industry Intelligence Briefing — <Client Name>
**Date:** <YYYY-MM-DD>
**Scan type:** Daily / Weekly
**Sources scanned:** <list of sources that returned results>

---

## Executive Summary

<2-3 sentence overview of the most important findings. What should the client pay attention to today?>

---

## Intelligence Briefing

### Competitor News
| Item | Source | Link | Relevance |
|------|--------|------|-----------|
| ... | ... | ... | High/Med |

### Industry Events
| Item | Source | Link | Date | Relevance |
|------|--------|------|------|-----------|

### Market Trends
| Item | Source | Link | Engagement | Relevance |
|------|--------|------|------------|-----------|

### Funding / M&A
| Item | Source | Link | Relevance |
|------|--------|------|-----------|

### Regulatory / Policy
| Item | Source | Link | Relevance |
|------|--------|------|-----------|

### Technology
| Item | Source | Link | Relevance |
|------|--------|------|-----------|

### People Moves
| Item | Source | Link | Relevance |
|------|--------|------|-----------|

### Pain Points & Complaints
| Item | Source | Link | Engagement | Relevance |
|------|--------|------|------------|-----------|

### Content Opportunities
| Item | Source | Link | Why | Relevance |
|------|--------|------|-----|-----------|

*(Only include sections that have items. Skip empty categories.)*

---

## Strategic Growth Opportunities

*(Only include opportunities where there's a genuine, actionable strategy with meaningful potential impact. It is completely fine to have zero opportunities on a quiet day.)*

### Opportunity 1: <Short title>

**Trigger:** <What happened>

**Strategy:** <What to do about it>

**Tactics:**
1. <Specific action> *(skill: <skill-name> if applicable)*
2. <Specific action>
3. <Specific action>

**Urgency:** Immediate / Soon / Evergreen
**Effort:** Low / Medium / High
**Expected Impact:** <Why this matters>

---

### Opportunity 2: ...

---

## Scan Statistics

- **Total items found:** X
- **By category:** Competitor News (X), Events (X), Trends (X), ...
- **Opportunities identified:** X
- **Sources that returned results:** X of Y configured

Configuration

Each client needs a config file at

clients/<client>/config/industry-scanner.json

. See

skills/industry-scanner/config/example-config.json

for the full schema.

Key fields:

```
web_search_queries
```
— broad industry search terms
```
competitors
```
— competitor names to monitor
```
subreddits
```
+
```
reddit_keywords
```
— Reddit monitoring config
```
twitter_queries
```
— Twitter/X search terms
```
linkedin_keywords
```
— LinkedIn post search terms
```
blog_urls
```
— industry publication URLs (for RSS scraping)
```
hn_queries
```
— Hacker News search terms
```
review_urls
```
— competitor review page URLs (G2, Capterra, Trustpilot)
```
event_keywords
```
— conference and event search terms

Tips

Daily vs Weekly: Daily scans (
```
--lookback 1
```
) are fast but may miss slower-developing stories. Run a weekly deep scan (
```
--lookback 7
```
) every Monday for comprehensive coverage.
Noisy sources: If a source consistently returns irrelevant results, tune the keywords in the config rather than dropping the source entirely.
Multi-source signals: Items that appear across multiple sources (e.g., on both Reddit and Twitter) are higher-signal. Flag these in the briefing.
Strategy quality > quantity: A day with zero strategic opportunities is better than a day with five forced ones. The intelligence briefing has standalone value even without opportunities.
Follow up: When an opportunity references a downstream skill (e.g.,
```
company-contact-finder
```
), the user can chain directly into that skill to take action.

Dependencies

No additional dependencies beyond what the sub-skills require:

```
requests
```
(Python) — for blog-feed-monitor, reddit-post-finder, twitter-mention-tracker, hn-scraper, review-site-scraper, news-monitor
```
APIFY_API_TOKEN
```
env var — for Reddit, Twitter, and review scraping
```
agentmail
```
+
```
python-dotenv
```
— for newsletter-monitor (if configured)
Rube/Crustdata connection — for LinkedIn post search (if configured)