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 gabrielmoreira/agent-skills-mirror --skill content-angle-rankergit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/content-angle-ranker)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/content-angle-ranker"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/content-angle-ranker/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/gabrielmoreira/agent-skills-mirror/content-angle-ranker"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/content-angle-ranker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00175 | $0.04986 |
| Opus 5 | $0.00088 | $0.02493 |
| Sonnet 5 | $0.00035 | $0.00997 |
| Haiku 4.5 | $0.00017 | $0.00499 |
Grade A, and why
content-angle-ranker 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.
This is a copy
100% identical to content-angle-ranker — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Angle Ranker
You have a keyword. You know the niche. But what specific content should you create? Which angle, format, and hook will actually perform? This skill answers that question with data — not gut feeling.
It takes engagement data (from trending-content-scout or live research) and ranks
8-12 content angle candidates by a weighted score combining platform fit, competition
level, engagement prediction, and creator fit. The output is a prioritized list with
a clear recommendation and direct handoff to content creation skills.
Think of it as /plan-ceo-review from gstack, but for content strategy: "What is
the 10-star version of this content?" — except the answer is backed by engagement data.
Stage
This skill belongs to Stage S1: Research — but it bridges directly into S2: Content Creation.
When to Use
- After
trending-content-scoutran — use its data to pick the best angle - User has a product/keyword but doesn't know what content to create
- User has multiple content ideas and wants to prioritize by data
- User wants to know: "If I only have time for ONE piece of content, what should it be?"
- Before running any S2 content skill (viral-post-writer, tiktok-script-writer, etc.)
Input Schema
keyword: string # (required if no scout_data) "AI video tools"
product: object # (optional) Affiliate product being promoted
name: string # "HeyGen"
description: string # What it does
url: string # Product URL or affiliate link
reward_value: string # Commission info — never shown in content
platform: string # (required) Target platform for content creation
# "youtube" | "tiktok" | "linkedin" | "x" | "reddit" | "blog"
creator_strengths: string[] # (optional) What the user is good at
# "storytelling" | "technical" | "humor" | "authority" |
# "visual" | "data" | "personal_experience"
audience: string # (optional) Target audience — "beginners", "developers", "small business owners"
time_budget: string # (optional) "30min" | "2hours" | "1day" — affects difficulty filter
custom_angles: string[] # (optional) User's own angle ideas to include in ranking
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.
- 8d ago First seen · 456 lines · 175 tokens per session scan A c036e50672c3
content-angle-ranker is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 175 tokens to every session and 4,986 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to content-angle-ranker, differing in 0 lines, and is treated as a copy.
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