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-research-briefgit 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-research-brief)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/content-research-brief"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/content-research-brief/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-research-brief"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/content-research-brief.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 | $0.00164 | $0.04106 |
| Opus 5 | $0.00082 | $0.02053 |
| Sonnet 5 | $0.00033 | $0.00821 |
| Haiku 4.5 | $0.00016 | $0.00411 |
Grade A, and why
content-research-brief 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.
This is a copy
100% identical to content-research-brief — 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 — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Research Brief
Research a topic by collecting 5-10 real source articles, auto-tagging them by theme, extracting key data points, and synthesizing unique content angles. The output is a structured research brief that any downstream content skill can consume.
The problem this solves: Most AI-written affiliate content is generic because it's written from the model's training data — not from real, current sources. This skill forces research-first content creation: find real articles, extract real data, then write from those sources. The result is content with specific stats, real quotes, and current information that readers (and Google) actually value.
Inspired by the content-pipeline approach: Topic → Search → Select sources → Synthesize → Write with context.
Stage
This skill belongs to Stage S2: Content — but acts as the research foundation for all content skills.
When to Use
- Before writing any article, blog post, or long-form content
- When you need current data and stats about a topic (not just AI-generated claims)
- When creating comparison content (need real feature/pricing data from sources)
- When writing about a product launch, funding round, or industry trend
- After
trending-content-scoutidentifies a topic — research it deeper - When you want unique angles: N sources → N different content pieces
Input Schema
topic: string # (required) "HeyGen AI video tool", "email marketing trends 2024"
source_count: number # (optional, default: 7) How many sources to collect (3-10)
source_types: string[] # (optional, default: ["news", "blog"])
# Options: "news" | "blog" | "linkedin" | "youtube" | "reddit" | "academic"
freshness: string # (optional, default: "month") "day" | "week" | "month" | "year" | "any"
product: object # (optional) Focus research on a specific product
name: string # "HeyGen"
url: string # "https://heygen.com"
language: string # (optional, default: "en") "en" | "vi" | any ISO 639-1 code
angle_count: number # (optional, default: 3) How many unique content angles to generate
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 · 391 lines · 164 tokens per session scan A c7e41b3abe6f
content-research-brief is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 164 tokens to every session and 4,106 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to content-research-brief, differing in 0 lines, and is treated as a copy.
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