Agent Reach
Panniantong/Agent-Reach
Routes web research and platform lookups across 16 sites, including Twitter, Reddit, YouTube, Bilibili, Xiaohongshu and GitHub, through one command-line tool.
Daily industry intelligence scanner. An agent skill from gooseworks-ai/goose-skills.
$ npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills industry-scanner --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "industry-scanner" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/competitive-intel/composites/industry-scanner into .claude/skills/industry-scanner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-scanner", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gooseworks-ai/goose-skills/tree/main/skills/competitive-intel/composites/industry-scannerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills industry-scanner --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/competitive-intel/composites/industry-scanner .agents/skills/industry-scanner && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "industry-scanner" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/competitive-intel/composites/industry-scanner into .agents/skills/industry-scanner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-scanner", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills industry-scanner --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/competitive-intel/composites/industry-scanner .cursor/skills/industry-scanner && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "industry-scanner" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/competitive-intel/composites/industry-scanner into .cursor/skills/industry-scanner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-scanner", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gooseworks-ai/goose-skills.git --path skills/competitive-intel/composites/industry-scanner--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills industry-scanner --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/competitive-intel/composites/industry-scanner .gemini/skills/industry-scanner && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "industry-scanner" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/competitive-intel/composites/industry-scanner into .gemini/skills/industry-scanner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-scanner", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gooseworks-ai/goose-skills industry-scannerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/competitive-intel/composites/industry-scanner .github/skills/industry-scanner && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "industry-scanner" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/competitive-intel/composites/industry-scanner into .github/skills/industry-scanner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-scanner", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill industry-scanner -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills industry-scanner --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/competitive-intel/composites/industry-scanner .opencode/skills/industry-scanner && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "industry-scanner" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/competitive-intel/composites/industry-scanner into .opencode/skills/industry-scanner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-scanner", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
industry-scannerDaily 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. 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.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
python3From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
APIFY_API_TOKENFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,291 words, ~3,575 tokens.
.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.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.
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 7.1 for daily (default), 7 for weekly deep scanclients/<client>/config/industry-scanner.json — this contains all the keywords, sources, competitors, and URLs to scanclients/<client>/context.md — need the ICP, value props, and positioning to generate relevant strategies1 day for daily scans, 7 for weekly, or whatever the user specifiesIf 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.
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.
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.
python3 skills/blog-feed-monitor/scripts/scrape_blogs.py \
--urls "<comma-separated blog_urls from config>" \
--days <lookback> --output jsonRead skills/blog-feed-monitor/SKILL.md for full CLI reference.
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 jsonAlso 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.
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 jsonRead skills/twitter-mention-tracker/SKILL.md for full CLI reference.
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.
python3 skills/hacker-news-scraper/scripts/search_hn.py \
--query "<hn_query>" --days <lookback> --output jsonRun once per configured hn_queries entry. Read skills/hacker-news-scraper/SKILL.md for full CLI reference.
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 jsonRead skills/accounting-news-monitor/SKILL.md for full CLI reference.
If the client has newsletter monitoring configured:
python3 skills/newsletter-monitor/scripts/scan_newsletters.py \
--days <lookback> --output jsonRead skills/newsletter-monitor/SKILL.md for full CLI reference.
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 jsonRead skills/review-site-scraper/SKILL.md for full CLI reference.
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.
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 |
Use these as inspiration, not as a checklist. Match the pattern to the trigger:
Competitor in trouble (shutdown, bad reviews, layoffs, pivot):
web-archive-scraper (recover their customer list), review-site-scraper (find reviewers), linkedin-post-research (find posts about them), cold-email-outreachIndustry event coming up:
luma-event-attendees or conference-speaker-scraper)Viral post or trending discussion:
linkedin-post-research, company-contact-finderAcquisition or merger announced:
web-archive-scraper (find client lists), company-contact-finderNew regulation or policy change:
Pain point surfaced (Reddit complaint, negative review, LinkedIn vent):
company-contact-finderTrending topic or content gap:
Funding round announced at target company:
company-contact-finder, cold-email-outreachSave 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 configuredEach 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 termscompetitors — competitor names to monitorsubreddits + reddit_keywords — Reddit monitoring configtwitter_queries — Twitter/X search termslinkedin_keywords — LinkedIn post search termsblog_urls — industry publication URLs (for RSS scraping)hn_queries — Hacker News search termsreview_urls — competitor review page URLs (G2, Capterra, Trustpilot)event_keywords — conference and event search terms--lookback 1) are fast but may miss slower-developing stories. Run a weekly deep scan (--lookback 7) every Monday for comprehensive coverage.company-contact-finder), the user can chain directly into that skill to take action.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-monitorAPIFY_API_TOKEN env var — for Reddit, Twitter, and review scrapingagentmail + 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
SKILL.md and 3 other files in skills/competitive-intel/composites/industry-scanner of gooseworks-ai/goose-skills.
Open the folder on GitHubat commit c650c6d
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Industry Scanner this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Agent ReachPanniantong/Agent-Reach | 95k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Apify Multi-Platform Scraperapify/agent-skills | 2.4k | 2 repos | ~1.4k | Automated safety check: Notes | None | |
| Gingiris OpensourceGingiris/gingiris-opensource | 257 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Scrapecreators APIScrapeCreators/social-media-research-skills | 3.4k | — | ~4k | Automated safety check: Notes | MIT | |
| Gingiris Go GlobalGingiris-1031/Competitor-analysis-tool | 110 | — | ~868 | Automated safety check: Pass | None |
Panniantong/Agent-Reach
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apify/agent-skills
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Gingiris/gingiris-opensource
🇺🇸 Open Source Marketing Playbook — How to get 10k+ GitHub stars.
ScrapeCreators/social-media-research-skills
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API.
Gingiris-1031/Competitor-analysis-tool
🇺🇸 AI Product / SaaS Go-Global Complete SOP — From competitor research to launch to monetization.
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Works with
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.
Industry Scanner fits situations like: tasks that involve Go-to-market strategy; tasks that involve Web scraping.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.