competitor-content-tracker

competitor-content-tracker is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 76 tokens per session (1,440 once invoked), scanned A, original, MIT.

A recurring tracker for competitor posts on blogs, LinkedIn, and X. It produces a digest of new content, topics gaining attention, and subjects where your company may have an opportunity.

In plain words
What is it for?
Track competitor blogs and social profiles, filter by topics or keywords, and create a full or highlights-only content digest.
Why use it?
It avoids manually checking several publishing channels and makes weekly competitor content changes easier to review.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 skills/capabilities/blog-feed-monitor/scripts/scrape_blogs.py \.

Good fit Track competitor blogs and social profiles, filter by topics or keywords, and create a full or highlights-only content digest.

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About the project

Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.

gooseworks-ai/goose-skills · 1,202 stars · on GitHub · gooseworks.ai

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills
agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/competitor-content-tracker

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for competitor-content-tracker

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-content-tracker/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-content-tracker)
Your own site
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-content-tracker"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-content-tracker/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for competitor-content-tracker

Your own site · 80×15
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-content-tracker"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-content-tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,440 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00076 $0.01440
Opus 5 $0.00038 $0.00720
Sonnet 5 $0.00015 $0.00288
Haiku 4.5 $0.00008 $0.00144

Measured 8d ago against content hash 501f4ba61e65, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

competitor-content-tracker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/competitive-intel/composites/competitor-content-tracker/SKILL.md · 191 lines

How it starts

The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Competitor Content Tracker

Monitor competitor content activity across three channels — blog, LinkedIn, Twitter/X — and produce a consolidated digest highlighting what's new, what's getting traction, and where you have a content gap.

When to Use

  • "Track what [competitor] is publishing"
  • "Show me what my competitors posted this week"
  • "What topics are competitors winning on?"
  • "I want a weekly competitor content digest"

Phase 0: Intake

Competitors to Track

  1. List of competitor company names + blog URLs (e.g., https://clay.com/blog)
  2. LinkedIn profile URLs of competitor founders/CMOs to track (optional but high-value)
  3. Twitter/X handles of the competitors or their founders (optional)

Scope

  1. How far back? (default: 7 days for weekly digest, 30 days for first run)
  2. Any topics/keywords you care most about? (used to surface relevant posts first)

Output

  1. Format preference: full digest (everything) or highlights only (top 3-5 per competitor)?

Save config to clients/<client-name>/configs/competitor-content-tracker.json.

{
  "competitors": [
    {
      "name": "Clay",
      "blog_url": "https://clay.com/blog",
      "linkedin_profiles": ["https://www.linkedin.com/in/kareem-amin/"],
      "twitter_handles": ["@clay_hq", "@kareemamin"]
    }
  ],
  "days_back": 7,
  "keywords": ["GTM", "outbound", "AI agents", "growth"],
  "output_mode": "highlights"
}

Phase 1: Scrape Blog Content

Run blog-feed-monitor for each competitor blog URL:

python3 skills/capabilities/blog-feed-monitor/scripts/scrape_blogs.py \
  --urls "<competitor_blog_url>" \
  --days <days_back> \
  --keywords "<keywords>" \
  --output summary

Collect: post title, publish date, URL, excerpt.

Phase 2: Scrape LinkedIn Posts

Run linkedin-profile-post-scraper for each tracked founder/executive LinkedIn URL:

python3 skills/capabilities/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
  --profiles "<linkedin_url_1>,<linkedin_url_2>" \
  --days <days_back> \
  --max-posts 20 \
  --output summary

Read the full file on GitHub · 191 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 191 lines · 76 tokens per session scan A 501f4ba61e65

Subscribe to this mod's changes

competitor-content-tracker is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 76 tokens to every session and 1,440 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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