Agent skill

Industry Scanner

by gooseworks-ai in gooseworks-ai/goose-skills

Daily industry intelligence scanner. An agent skill from gooseworks-ai/goose-skills.

MITAuto-check passedMarketing & SEO

Install Industry Scanner

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills industry-scanner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/competitive-intel/composites/industry-scanner .claude/skills/industry-scanner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
industry-scanner
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.6k tokens
SKILL.md length
1,291 words
Files
4
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Daily industry intelligence scanner. An agent skill from gooseworks-ai/goose-skills.

  • Works in 5 steps: Load Configuration → Data Collection → Consolidate & Categorize → …
  • Tasks that involve Go-to-market strategy
  • SKILL.md covers Quick Start, Inputs, Step-by-Step Process and Configuration, plus 2 more sections
  • Calls python3; needs APIFY_API_TOKEN

What it does

Industry Scanner is an agent skill from gooseworks-ai/goose-skills. Daily industry intelligence scanner. Scans web, social media, news, blogs, and communities for industry-relevant events, trends, and signals. Produces a comprehensive intelligence briefing plus strategic GTM opportunity ideas. Orchestrates existing scraping skills — does not reimplement data collection.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `config/example-config.json`, `skill.meta.json` and `templates/output-template.md`).

It sits in Marketing & SEO, covering Go-to-market strategy and Web scraping. It works with Reddit. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Go-to-market strategy
  • Tasks that involve Web scraping

Example prompts

  • “/industry-scanner”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Load Configuration
  2. Data Collection
  3. Consolidate & Categorize
  4. Generate Strategic Opportunities
  5. Generate Output

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • APIFY_API_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Industry Scanner loads about 3.6k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 1,291 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,291 words, ~3,575 tokens.

Download SKILL.mdSave it as .claude/skills/industry-scanner/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
industry-scanner
description
Daily industry intelligence scanner. Scans web, social media, news, blogs, and communities for industry-relevant events, trends, and signals. Produces a comprehensive intelligence briefing plus strategic GTM opportunity ideas. Orchestrates existing scraping skills — does not reimplement data collection.

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
  1. Read clients/<client>/config/industry-scanner.json — this contains all the keywords, sources, competitors, and URLs to scan
  2. Read clients/<client>/context.md — need the ICP, value props, and positioning to generate relevant strategies
  3. Set the lookback period: use 1 day for daily scans, 7 for weekly, or whatever the user specifies
  4. 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
bash
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:

bash
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:

bash
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.

Delegate to the linkedin-post-research skill (uses the apimaestro~linkedin-posts-search-scraper-no-cookies Apify actor). Search each keyword with date_posted: "past-day" (or "past-week" for weekly scans).

Read skills/linkedin-post-research/SKILL.md for the full Apify workflow.

2F. Hacker News
bash
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:

bash
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:

bash
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:

bash
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:

  1. 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).

  2. Categorize each item into one of these types:

CategoryWhat to Look For
Competitor NewsShutdowns, launches, funding, pivots, negative reviews, leadership changes, pricing changes
Industry EventsUpcoming conferences, webinars, meetups, speaker slots, CFPs, award nominations
Market TrendsViral discussions, hot topics, emerging themes, sentiment shifts, adoption data
Regulatory / PolicyNew regulations, compliance changes, government actions, standards updates
People MovesKey hires, departures, promotions at competitors or target companies
TechnologyNew product launches, integrations, platform changes, deprecations
Funding / M&AAcquisitions, mergers, funding rounds, PE investments, IPO signals
Pain PointsPeople publicly complaining about problems the client solves
Content OpportunitiesTrending content, viral posts, gaps in existing coverage, unanswered questions
  1. Rate relevance — High / Medium / Low based on how directly it relates to the client's ICP and value props.

  2. 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:

FieldDescription
TriggerWhat happened — the intelligence item(s) that sparked this idea
StrategyWhat to do about it — specific and actionable, not vague
Tactics2-4 concrete next steps with skill references where applicable
UrgencyImmediate (do this today/this week), Soon (next 2 weeks), or Evergreen
EffortLow (1-2 hours), Medium (half day), High (multi-day project)
Expected ImpactWhy this could matter — who it reaches, what it could generate
Show full SKILL.md (548 more words)Show less
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:


markdown
# 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)
  • APIFY_API_TOKEN — LinkedIn post search goes through the linkedin-post-research skill (Apify-based)

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files in skills/competitive-intel/composites/industry-scanner of gooseworks-ai/goose-skills.

  • SKILL.md
  • config/example-config.json
  • skill.meta.json
  • templates/output-template.md

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 9, 2026.

Compare with similar skills

Industry Scanner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Industry Scanner compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Industry Scanner this skillgooseworks-ai/goose-skills1.2k1 repos~3.6kAutomated safety check: PassMIT
Agent ReachPanniantong/Agent-Reach95k—~1.4kAutomated safety check: PassMIT
Apify Multi-Platform Scraperapify/agent-skills2.4k2 repos~1.4kAutomated safety check: NotesNone
Gingiris OpensourceGingiris/gingiris-opensource257—~1.4kAutomated safety check: PassMIT
Scrapecreators APIScrapeCreators/social-media-research-skills3.4k—~4kAutomated safety check: NotesMIT
Gingiris Go GlobalGingiris-1031/Competitor-analysis-tool110—~868Automated safety check: PassNone

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Works with

Questions about Industry Scanner

What does Industry Scanner do?

Daily industry intelligence scanner. An agent skill from gooseworks-ai/goose-skills. Industry Scanner is an agent skill from gooseworks-ai/goose-skills. Daily industry intelligence scanner.

When should I use Industry Scanner?

Industry Scanner fits situations like: tasks that involve Go-to-market strategy; tasks that involve Web scraping.

How do I install Industry Scanner in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a claude-code`. Or copy the skill folder (skills/competitive-intel/composites/industry-scanner in gooseworks-ai/goose-skills) into .claude/skills/industry-scanner in your project. Claude Code loads it when a task matches its description.

How do I install Industry Scanner in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a codex`. Or copy the skill folder (skills/competitive-intel/composites/industry-scanner in gooseworks-ai/goose-skills) into .agents/skills/industry-scanner in your project. Codex loads it when a task matches its description.

Can I use Industry Scanner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/industry-scanner, .gemini/skills/industry-scanner, .github/skills/industry-scanner and .opencode/skills/industry-scanner in your project.

What does Industry Scanner need to run?

Going by SKILL.md and its folder, Industry Scanner needs the command-line tools its instructions call (python3) and credentials named APIFY_API_TOKEN. Our summary lists: Python 3.

Does Industry Scanner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Industry Scanner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Industry Scanner use?

Industry Scanner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Industry Scanner use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Industry Scanner?

Skills that share tags, products or a category with Industry Scanner: Agent Reach (Panniantong/Agent-Reach, 95k stars), Apify Multi-Platform Scraper (apify/agent-skills, 2.4k stars), Gingiris Opensource (Gingiris/gingiris-opensource, 257 stars) and Scrapecreators API (ScrapeCreators/social-media-research-skills, 3.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Industry Scanner?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.