---
name: signal-to-content
description: Converts evidence-backed trend signals, outlier posts, audience language, and competitor patterns into original platform-native content tests without copying a source's unique expression. Use after trend research, creator teardown, comment mining, or outlier analysis.
license: MIT
metadata:
  version: 0.1.0
  author: Alptuğ Harun
title: signal-to-content
canonical_url: https://skilld.dev/gh/alptugharun/ai-social-media-toolkit/signal-to-content
last_updated: 2026-09-30T14:41:01.000Z
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [evals/evals.json](https://skilld.dev/api/skills-raw/alptugharun/ai-social-media-toolkit/signal-to-content/evals/evals.json).
>
> If the user asked to install this Skill, run `npx skilld install alptugharun/ai-social-media-toolkit/signal-to-content`. Install writes the Skill files into the project, so every session loads them.

# Signal to Content

Turn evidence into original content.

This skill sits between **research** and **production**.

It should not produce a generic brainstorm from a trend name.

It should explain what worked, separate the reusable mechanic from the source's unique expression, score the opportunity, and create platform-native tests for the user's brand.

## Inputs

Accept any combination of:

- trend brief
- outlier table
- creator teardown
- comments / audience language
- transcripts
- competitor research
- Pinterest keyword clusters
- article / news source
- user-provided examples

Use creator context when available.

## Step 1 — Extract the Signal

For every source identify:

- observed performance signal
- hook mechanism
- format
- audience problem / desire
- emotional trigger
- proof mechanism
- visual pattern
- CTA pattern
- timing / freshness
- saturation

Do not confuse unique wording with a reusable mechanism.

## Step 2 — Separate Mechanic from Expression

Create two columns:

### Reusable Mechanic
Examples:

- before / after contrast
- myth vs evidence
- rapid checklist
- visual transformation
- first-person experiment
- comparison
- reveal
- contrarian question
- searchable tutorial

### Source-Specific Expression
Examples:

- exact sentence
- proprietary brand asset
- personal story
- distinctive graphic
- copyrighted footage
- unique joke / character

Do not copy source-specific expression unless the user owns it or has permission.

## Step 3 — Score the Opportunity

Use the toolkit's transparent heuristic:

**Base Score**
- Evidence strength: 30%
- Audience fit: 25%
- Freshness: 20%
- Repeatability: 15%
- Production ease: 10%

Then subtract a **saturation penalty of up to 15 points**.

Inputs should be 0–100.

This is a prioritization heuristic, not a validated prediction model.

The companion tool is:

`tools/signal2content_score.py`

## Step 4 — Create Original Tests

For each high-priority signal create original executions.

Possible outputs:

- Reels / Shorts
- Pinterest Pins
- Instagram carousel
- LinkedIn post
- Article angle
- Newsletter
- visual-series brief

For every execution provide:

- Hook
- Core idea
- Format
- Visual direction
- Why it fits the audience
- What changed from the source pattern
- CTA
- KPI

## Step 5 — Build the Content Ladder

A strong signal can become a sequence:

**Signal → Fast Test → Winner → Series → Evergreen Asset → Website / Lead Magnet**

Do not scale a concept before the first test provides evidence.

## Output

# Signal to Content Brief

## Research Input
- Sources:
- Platforms:
- Window:
- Confidence:

## Opportunity Table
| Signal | Reusable mechanic | Evidence | Fit | Freshness | Saturation | Score |
| --- | --- | --- | ---: | ---: | ---: | ---: |

## Source-Specific Elements Not to Copy
- ...

## Original Content Tests

### Test 1
- Platform:
- Hook:
- Format:
- Visual:
- Angle:
- Transformation from source:
- CTA:
- KPI:

## Content Ladder
- Fast test:
- Follow-up:
- Series:
- Evergreen asset:

## Review Gate
- [ ] Evidence is real and cited when available
- [ ] Source-specific expression was not copied
- [ ] The execution fits creator context
- [ ] Platform constraints are respected
- [ ] KPI matches the objective

## Core Principle

**Borrow the pattern. Rebuild the expression. Improve the system.**