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 nicepkg/ai-workflow --skill content-researchgit clone --depth 1 https://github.com/nicepkg/ai-workflowWrote 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/nicepkg/ai-workflow/content-research)<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/content-research"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/content-research/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/nicepkg/ai-workflow/content-research"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/content-research.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 | $0.00210 | $0.03498 |
| Opus 5.5 | $0.00084 | $0.01399 |
| Sonnet 5.5 | $0.00042 | $0.00700 |
| Haiku 4.5 | $0.00021 | $0.00350 |
Grade A, and why
content-research 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- pr-review-workflow — 86% identical, 575 lines differ
How it starts
The opening of the file, as written. The whole thing — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Research & Creation
Create authentic, research-backed content that sounds human-written AND is optimized to appear in AI search results (Perplexity, ChatGPT, Claude, Gemini).
Core Workflow
RESEARCH → EXPERTS → IDEATION → CREATION → AIO
↓ ↓ ↓ ↓ ↓
Trends Real Unique Authentic AI-Citable
& Data People Angles Content Structure
Always complete phases in order. Never skip research. Always apply AIO principles.
Why AI Search Optimization (AIO) Matters
AI assistants answer millions of questions daily. When someone asks "How do I raise a seed round?" or "What's the best way to find investors?", AI models cite sources. Your goal: become the source AI cites.
How AI models select sources to cite:
- Authority signals - Clear expertise, credentials, brand recognition
- Direct answers - Content that directly answers the question asked
- Structured data - Headers, lists, tables that are easy to extract
- Recency - Fresh, dated content with current information
- Uniqueness - Original data, frameworks, or perspectives
- Quotability - Concise, memorable statements worth citing
Phase 1: Deep Research
Before writing anything, build comprehensive topic understanding.
1.1 Trend Discovery
Use WebSearch to find:
- Recent news (last 30-90 days) about the topic
- Industry reports and data from credible sources
- Emerging trends that haven't been over-covered
- Contrarian viewpoints that challenge conventional wisdom
Search patterns:
- "[topic] trends 2025"
- "[topic] statistics report"
- "[topic] industry analysis"
- "[topic] challenges problems"
- "[topic] future predictions expert"
1.2 Competitive Landscape
Research what content already exists:
- Top-ranking articles on the topic
- Gaps in existing coverage
- Overused angles to avoid
- Fresh perspectives not yet explored
1.3 Data & Statistics
Find concrete data to cite:
- Industry benchmarks and statistics
- Survey results and research findings
- Case studies with measurable outcomes
- Credible sources (avoid generic "studies show")
What ships with it
4 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.
- 1mo ago First seen · 448 lines · 210 tokens per session scan A 45b664960e2e
content-research is a skill published in the GitHub repository nicepkg/ai-workflow (286 stars, last pushed 9mo ago), licensed MIT. It adds 210 tokens to every session and 3,498 once invoked, about $0.0008 per session on Opus 5.5. 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 2026-08-30.
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