content-workflow

content-workflow is a cursor rule for coding agents from thatrebeccarae/claude-marketing. It costs 47 tokens per session (1,908 once invoked), scanned A, original, MIT.

A workflow for taking content from research to a reviewed draft and then adapting it for social channels. It covers blog posts, LinkedIn posts, Twitter/X threads, newsletters, and essays.

In plain words
What is it for?
Researching topics, assessing sources, drafting and revising content, checking claims, and preparing versions for different publishing platforms.
Why use it?
It organizes research, fact checking, editing, and distribution so content does not have to be managed as disconnected tasks.

Cursor rule

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 rules/thatrebeccarae/claude-marketing/content-workflow
Clone the repo
git clone --depth 1 https://github.com/thatrebeccarae/claude-marketing

Wrote 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.

agentmods badge for content-workflow

README.md
[![agentmods](https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/content-workflow.svg)](https://agentmods.dev/rules/thatrebeccarae/claude-marketing/content-workflow)
Your own site
<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/content-workflow"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/content-workflow.svg" alt="Measured on agentmods" height="20"></a>
Per session 47 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,908 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 $0.00047 $0.01908
Opus 5 $0.00023 $0.00954
Sonnet 5 $0.00009 $0.00382
Haiku 4.5 $0.00005 $0.00191

Measured 5d ago against content hash 82301366c93e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

content-workflow 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 5d 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.

integrations/cursor/content-workflow.mdc · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content Workflow

End-to-end content creation pipeline: research, draft, review, distribute.

Pipeline Stages

Stage 1: Research

Gather raw material for the content piece.

  1. Topic research — web search, RSS feeds, industry sources
  2. Source assessment — credibility, recency, relevance
  3. Data extraction — key statistics, quotes, data points
  4. Counter-arguments — opposing views, nuance, caveats
  5. Angle development — what makes this piece unique

Output: Research brief with sources, data points, and recommended angle.

Stage 2: Draft & Edit

Create and refine the content.

  1. Draft — write following the content type framework and brand voice
  2. Editorial review against quality criteria:
    • Voice consistency
    • Structural quality (hook → body → CTA)
    • SEO optimization (if applicable)
    • Factual accuracy (all claims sourced)
    • Readability (appropriate level for audience)
  3. Revision — apply specific feedback
  4. Final review — verify all issues addressed

Output: Polished draft ready for approval.

Stage 3: Distribute

Adapt the finished piece for cross-platform distribution.

  1. LinkedIn post — hook, body, CTA, hashtags
  2. Twitter/X thread — numbered tweets, hooks per tweet
  3. Email subject lines — 3 options with preview text
  4. Pull quotes — 3-5 shareable excerpts for social

Output: Distribution pack with platform-specific adaptations.

Usage Modes

Full Pipeline

/content-workflow [topic]

Runs all 3 stages: research → draft → review → distribute.

Start from Draft

/content-workflow --from-draft [file path]

Skips research. Takes an existing draft through editorial review and distribution.

Distribution Only

/content-workflow --distribute [file path]

Takes a finished, approved piece and creates the social distribution pack.

Content Types

Type Research Depth Draft Length Distribution
Blog post Deep (5+ sources) 1,500-2,500 words LinkedIn + Twitter + Email
LinkedIn post Light (2-3 sources) 150-300 words Twitter adaptation only
Twitter thread Light (1-2 sources) 5-12 tweets LinkedIn adaptation only
Newsletter Medium (3-5 sources) 500-1,000 words Twitter + LinkedIn teasers
Essay Deep (8+ sources) 2,000-4,000 words Full distribution pack

Read the full file on GitHub · 263 lines

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. 5d ago First seen · 263 lines · 47 tokens per session scan A 82301366c93e

Subscribe to this mod's changes

content-workflow is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (130 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 1,908 once invoked, about $0.0002 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.