content-waterfall

A content workflow that turns one main draft into versions for many publishing platforms. It adapts the same source material into items such as short video scripts, social posts, email content, and comment replies.

In plain words
What is it for?
Use it to create a coordinated bundle for Instagram, TikTok, LinkedIn, X, email, YouTube, and other supported channels, including alternative hooks and calls to action.
Why use it?
It removes the need to rewrite the same idea separately for every platform. It keeps the derivatives connected to the original draft, research, offer, and publishing schedule.

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/maxtechera/ship/waterfall
Any agent
npx skills add maxtechera/ship --skill waterfall
Clone the repo
git clone --depth 1 https://github.com/maxtechera/ship

Made for: Claude Code, Codex.

Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 634 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 $0.00024 $0.00634
Opus 5 $0.00012 $0.00317
Sonnet 5 $0.00005 $0.00127
Haiku 4.5 $0.00002 $0.00063

Measured yesterday against content hash 1d00a3bdf529, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

content-waterfall 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 yesterday.

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.

content/skills/waterfall/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content Waterfall

Generate a multi-platform bundle from a single pillar asset.

Inputs Required

  • Pillar draft asset (from content-compose or existing piece)
  • Research refs used for grounding
  • Offer/CTA rules for the run
  • Channel cadence constraints (which platforms, which days)

Derivative Matrix

From 1 pillar, produce all applicable derivatives:

Derivative Platform Notes
Reel script Instagram / TikTok Hook + problem + solution + CTA, ≤60s
IG caption Instagram 150-300 words, 5-10 hashtags
IG carousel Instagram 8-10 slides, hook slide + value slides + CTA slide
X/Twitter thread Twitter/X 6-10 tweets, hook tweet first
LinkedIn post LinkedIn Professional angle, metrics-led, longer form
Newsletter block Email Personal angle, conversational
YouTube description YouTube SEO-structured, chapters if long-form
TikTok caption TikTok 100-150 words, 3-5 hashtags
Avatar script HeyGen / Synthesia Script + visual cue markers
CTA variants (3) All Cold / warm / hot temperatures
Comment reply pack (5) All Seed replies for engagement
Hook variants (5) All Per-platform alternatives for testing

Hook Engine

Before finalizing the bundle, generate per-platform hook variants:

  • 5 hook options per primary platform
  • Select 1 primary + 2 alternates per platform for A/B testing
  • Hooks must use VoC language — never generic claims

Draft Schedule Plan

Suggest a posting schedule that fills calendar gaps:

Day 0: Primary reel (anchor piece)
Day 1: Carousel (expands on key point from reel)
Day 2: Thread (data/proof angle)
Day 4: LinkedIn (professional framing)
Day 7: Newsletter block
Day 10: Republish variant (short format)

Verification

  • Per-platform formatting is correct (character limits, aspect ratios, hashtag counts)
  • CTAs align with run offer and do not contradict strategy
  • No generic AI filler; hooks are specific and grounded in evidence
  • Lineage is documented: which claim in each derivative came from the pillar

Read the full file on GitHub · 79 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. yesterday First seen · 79 lines · 24 tokens per session scan A 1d00a3bdf529

Subscribe to this mod's changes

content-waterfall is a skill published in the GitHub repository maxtechera/ship (2 stars, last pushed 4mo ago), licensed MIT. It adds 24 tokens to every session and 634 once invoked, about $0.0001 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens