content-research

A research-led writing workflow for producing content that is useful to people and easy for AI answer tools such as ChatGPT, Claude, Gemini, and Perplexity to cite.

In plain words
What is it for?
Use it to create articles, guides, and other research-backed content that may be found through web search or quoted by AI assistants.
Why use it?
It helps avoid publishing content based only on assumptions or scattered notes. It organizes research, expert input, original ideas, and clear answers into one process.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/nicepkg/ai-workflow/content-research
Any agent
npx skills add nicepkg/ai-workflow --skill content-research
Clone the repo
git clone --depth 1 https://github.com/nicepkg/ai-workflow

Made for: Claude Code, Codex.

Per session 210 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,498 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00210 $0.03498
Opus 5 $0.00105 $0.01749
Sonnet 5 $0.00042 $0.00700
Haiku 4.5 $0.00021 $0.00350

Measured 2d ago against content hash 45b664960e2e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

workflows/content-creator-workflow/.claude/skills/content-research/SKILL.md · 448 lines

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:

  1. Authority signals - Clear expertise, credentials, brand recognition
  2. Direct answers - Content that directly answers the question asked
  3. Structured data - Headers, lists, tables that are easy to extract
  4. Recency - Fresh, dated content with current information
  5. Uniqueness - Original data, frameworks, or perspectives
  6. 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")

Read the full file on GitHub · 448 lines

Files

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.

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. 2d ago First seen · 448 lines · 210 tokens per session scan A 45b664960e2e

Subscribe to this mod's changes

content-research is a skill published in the GitHub repository nicepkg/ai-workflow (283 stars, last pushed 7mo ago), licensed MIT. It adds 210 tokens to every session and 3,498 once invoked, about $0.0011 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 2026-08-30.

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