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Comment Reply Manager

ASecurity

Use when monitor TikTok/Instagram comments, classify sentiment, auto-reply

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  • Added September 8, 2026
ai-agentspythongogitperformance

Works with

  • cli

Security analysis

A100/100

Scanned September 8, 2026

npx -y skills add oyi77/1ai-skills --skill comment-reply-manager --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: comment-reply-manager
description: Use when monitor TikTok/Instagram comments, classify sentiment, auto-reply
  with FAQ answers, and DM high-intent commenters with LYNK affiliate links to convert
  engagement into sales. Use when monitoring tiktok/instagram comments, classify sentiment,
  auto-reply with faq answers, and.
domain: content
author: oyi77
license: Apache-2.0
subdomain: content-creation
tags:
- comment
- content-creation
- digital-content
- manager
- media
- reply
version: 1.0.0
category: content
---


# Comment Reply Manager

## When to Use

**Trigger phrases:**
- "comment reply manager"
- "Help me with comment reply manager"

**Use cases:**
- When the task matches this skill's domain expertise

**When NOT to use:**
- For tasks outside this skill's scope


**Purpose:** Convert comment engagement into LYNK affiliate sales. Detect, analyze, and reply to comments on TikTok/Instagram posts. Auto-DM high-intent commenters with LYNK product links.

**Revenue Context:** 196 clicks → 0 sales. This skill creates the missing engagement funnel between viewer and buyer.

---


## When NOT to Use

- When the content requires deep domain expertise you do not have
- For legal, medical, or financial advice content
- When real-time data is required (use live data feeds)


## Overview

Comment Reply Manager enables content production with professional quality and consistency.

## Workflow

```python
# Example: Content generation pipeline
def generate_content(topic: str, format: str = "article"):
    outline = create_outline(topic)
    draft = write_draft(outline, format)
    edited = edit_for_quality(draft)
    optimized = optimize_for_seo(edited)
    return publish(optimized)
```

1. **Define brief** — Set objectives, audience, and style guidelines
2. **Research and gather** — Collect source material and reference content
3. **Create draft** — Generate initial content following the brief
4. **Refine and edit** — Polish for quality, accuracy, and engagement
5. **Publish and distribute** — Deploy to target platforms
6. **Track performance** — Monitor engagement and iterate

## Quality Checklist

- [ ] Content matches the defined brief and audience
- [ ] All facts verified against authoritative sources
- [ ] Formatting consistent with style guidelines
- [ ] SEO/distribution optimization applied
- [ ] Call-to-action clear and compelling

## Tools

- Content management system for publishing
- Analytics platform for performance tracking
- Design tools for visual assets
- Collaboration tools for review cycles

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "Good enough content works" | Quality content drives engagement. Mediocre content gets ignored. |
| "I will optimize later" | SEO and distribution need optimization from the start. |
| "Templates are good enough" | Templates are a starting point. Custom content outperforms generic. |


## Process

1. **Prepare** — Gather requirements, verify prerequisites, set up environment
1. **Execute** — Run comment reply manager workflow with configured parameters
1. **Verify** — Validate output meets requirements, document results

## Verification

- [ ] Content meets quality standards and brief requirements
- [ ] Output is properly formatted for target platform
- [ ] All facts and references verified
- [ ] SEO/distribution optimization applied where applicable
## Verification Checklist

- [ ] Comment classification accuracy > 90%
- [ ] Auto-reply triggers on correct sentiment
- [ ] DM conversion tracking functional
- [ ] Rate limits respected (no account flags)
- [ ] Affiliate links properly attributed

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