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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gooseworks-ai/goose-skills --skill twitter-mention-trackergit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote 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.
[](https://agentmods.dev/skills/gooseworks-ai/goose-skills/twitter-mention-tracker)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/twitter-mention-tracker"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/twitter-mention-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.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/twitter-mention-tracker"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/twitter-mention-tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.00870 |
| Opus 5 | $0.00026 | $0.00435 |
| Sonnet 5 | $0.00011 | $0.00174 |
| Haiku 4.5 | $0.00005 | $0.00087 |
Grade A, and why
twitter-mention-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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Twitter Mention Tracker
Search Twitter/X posts using the Apify apidojo/tweet-scraper actor.
Quick Start
Requires APIFY_API_TOKEN env var (or --token flag).
# Search with date range (recommended -- uses Twitter native since:/until: operators)
python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
--query "YourCompany" --since 2026-02-15 --until 2026-02-23
# Quick summary of recent mentions
python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
--query "@yourhandle" --max-tweets 20 --output summary
# Search without date filtering
python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
--query "AI content marketing" --max-tweets 50
Date Filtering
Important: The apidojo/tweet-scraper actor's built-in date parameters are unreliable.
This script embeds since:YYYY-MM-DD and until:YYYY-MM-DD directly into the search query
string, using Twitter's native advanced search syntax. This ensures date filtering works
correctly server-side.
How the Script Works
- Builds a search term with the query quoted and date operators appended
- Calls the Apify
apidojo/tweet-scraperactor via REST API - Polls until the run completes, then fetches the dataset
- Deduplicates by tweet ID/URL
- Applies optional keyword filtering (client-side)
- Sorts by likes (descending) and outputs JSON or summary
CLI Reference
| Flag | Default | Description |
|---|---|---|
--query |
required | Search query (quoted in Twitter search) |
--since |
none | Start date YYYY-MM-DD (inclusive) |
--until |
none | End date YYYY-MM-DD (exclusive) |
--max-tweets |
50 | Max tweets to scrape |
--keywords |
none | Additional filter keywords (comma-separated, OR logic) |
--output |
json | Output format: json or summary |
--token |
env var | Apify token (prefer APIFY_API_TOKEN env var) |
--timeout |
300 | Max seconds to wait for the Apify run |
Direct API Usage
{
"searchTerms": ["\"YourCompany\" since:2026-02-15 until:2026-02-22"],
"maxTweets": 50,
"searchMode": "live"
}
What ships with it
2 files 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.
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.
- 9d ago First seen · 102 lines · 53 tokens per session scan A 31a504474991
twitter-mention-tracker is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 53 tokens to every session and 870 once invoked, about $0.0003 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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