---
title: "comment-intelligence"
description: "Turns public or user-provided social comments into evidence-backed audience intelligence: recurring questions, objections, pain points, buying signals, vocabulary, content gaps, and testable content ideas. Use for voice-of-customer research, comment mining, audience research, product-demand research, FAQ discovery, and content planning."
canonical_url: "https://skilld.dev/gh/alptugharun/ai-social-media-toolkit/comment-intelligence"
last_updated: "2026-09-30T14:41:01.000Z"
---

---
name: comment-intelligence
description: Turns public or user-provided social comments into evidence-backed audience intelligence: recurring questions, objections, pain points, buying signals, vocabulary, content gaps, and testable content ideas. Use for voice-of-customer research, comment mining, audience research, product-demand research, FAQ discovery, and content planning.
license: MIT
metadata:
  version: 0.1.0
  author: Alptuğ Harun
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> If the user asked to install this Skill, run `npx skilld install alptugharun/ai-social-media-toolkit/comment-intelligence`. Install writes the Skill files into the project, so every session loads them.

# Comment Intelligence

Turn comment sections into **audience evidence**, not a pile of anecdotes.

## Inputs

Accept comments supplied by the user or collected from public sources the runtime can legitimately access. Useful fields include:

- comment text
- source URL / post ID
- platform
- date
- likes or replies when available
- creator / brand / topic

Do not claim access to private, deleted, login-gated, or unavailable comments.

## Workflow

1. Define the research question before clustering comments.
2. Remove obvious spam, duplicates, bot-like repetition, and context-free noise when justified.
3. Preserve source references so findings remain auditable.
4. Cluster comments by meaning rather than keyword alone.
5. Separate observed language from analyst interpretation.
6. Rank themes using frequency plus strength of intent, not frequency alone.
7. Extract representative short phrases only when allowed; otherwise paraphrase.
8. Convert strong themes into hypotheses and content tests.

## Signal Types

Look for:

- repeated questions
- objections and friction
- pain points
- desired outcomes
- misconceptions
- comparisons and alternatives
- purchase / signup / download intent
- requests for tutorials or examples
- emotional language
- recurring audience vocabulary
- missing information
- positive proof and reasons people care

## Evidence Rules

Never infer precise demographics, sentiment percentages, market size, or purchase probability unless the data supports them.

A loud minority is not automatically representative. Distinguish:

- **Repeated signal** — appears across multiple independent comments
- **Strong single signal** — specific and useful but not repeated
- **Hypothesis** — plausible interpretation requiring testing

When engagement counts are available, treat them as context rather than proof that a comment represents the whole audience.

## Output

# Comment Intelligence Brief

## Research Question
[Question]

## Dataset
- Sources:
- Comments reviewed:
- Date range:
- Important limitations:

## Signal Table
| Theme | Signal type | Evidence strength | Audience language | Interpretation |
| --- | --- | --- | --- | --- |

## Questions & Objections
[Prioritized list]

## Content Opportunities
| Opportunity | Evidence | Format | Hook hypothesis | Test |
| --- | --- | --- | --- | --- |

## Product / Offer Signals
[Only when supported]

## Watchlist
[Weak or emerging signals worth monitoring]

## Recommended Next Tests
Prioritize a small number of measurable tests. Keep evidence separate from creative recommendations.