reactor

reactor is a skill for Claude Code, Codex from keugenek/reactor. It costs 70 tokens per session (2,611 once invoked), scanned A, original, MIT.

A tool that predicts how a LinkedIn post may perform before publishing. It scores factors such as the opening line, debate potential, authenticity, awkwardness, audience timing, and estimated impressions.

In plain words
What is it for?
Use it to review LinkedIn post drafts, receive a GO, WAIT, IMPROVE, or NO verdict, and get suggested improvements and timing guidance.
Why use it?
It helps identify weak hooks, low discussion potential, or an unsuitable publishing time before a post goes live.

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

Made for: Claude Code, Codex.

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 reactor

README.md
[![agentmods](https://agentmods.dev/badge/skills/keugenek/reactor/skill.svg)](https://agentmods.dev/skills/keugenek/reactor/skill)
Your own site
<a href="https://agentmods.dev/skills/keugenek/reactor/skill"><img src="https://agentmods.dev/badge/skills/keugenek/reactor/skill.svg" alt="Measured on agentmods" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,611 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. 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 $0.00070 $0.02611
Opus 5 $0.00035 $0.01306
Sonnet 5 $0.00014 $0.00522
Haiku 4.5 $0.00007 $0.00261

Measured 4d ago against content hash abd802ccd381, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

reactor scanned grade A with 1 finding 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 4d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X POST https://reactor-api.keugenek.workers.dev/v1/predict \
skill/SKILL.md · 295 lines

How it starts

The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reactor — LinkedIn Post Performance Predictor

What it does

Predicts LinkedIn post performance using calibrated heuristics trained on real data. Returns:

  • Verdict: GO / WAIT / IMPROVE / NO
  • Estimated impressions (calibrated to real post data)
  • Debate score (0-100) — controversy drives the algorithm
  • Hook score (0-1) — first-line effectiveness
  • Cringe score (0-100) — LinkedIn cringe detection
  • Authenticity score (0-100) — does it sound human?
  • Timing multiplier — based on audience timezone distribution
  • Warnings — specific problems ("Algorithm death: debate <30")
  • Improvements — actionable suggestions

Quick Start

Option 1: Direct API calls (no install needed)

# Predict a post
curl -X POST https://reactor-api.keugenek.workers.dev/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"post": "Your LinkedIn post text here"}'

# Discover all available endpoints
curl https://reactor-api.keugenek.workers.dev/v1/discover

# List content types with viral multipliers
curl https://reactor-api.keugenek.workers.dev/v1/content-types

Option 2: CLI

npx @keugenek/reactor "Your post text"
npx @keugenek/reactor --register [email protected]   # 5/day free
npx @keugenek/reactor --types                     # content types

Option 3: MCP Server (Claude Desktop / Cursor)

{
  "mcpServers": {
    "reactor": {
      "command": "npx",
      "args": ["@keugenek/reactor", "--mcp"],
      "env": { "REACTOR_API_KEY": "rk_free_xxx" }
    }
  }
}

How to use (for AI agents)

Predicting a post

When the user shares a LinkedIn post draft or asks "should I post this?":

  1. Call POST /v1/predict with the post text
  2. Present the verdict prominently (GO = post it, NO = rewrite)
  3. If debate score < 30, warn: "Algorithm death zone — no debate = no reach"
  4. Suggest improvements from the response
  5. If rate limited, offer to register (ask consent first!)

Before predicting, ask the user:

  1. How many LinkedIn followers do you have? (default: 1000)
  2. What's your audience? (currently: Tech Leaders & Engineers; Custom Audience coming in Pro)

Read the full file on GitHub · 295 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. 4d ago First seen · 295 lines · 70 tokens per session scan A abd802ccd381

Subscribe to this mod's changes

reactor is a skill published in the GitHub repository keugenek/reactor (2 stars, last pushed 5mo ago), licensed MIT. It adds 70 tokens to every session and 2,611 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.