An advanced methodology-driven framework skill that structures public opinion events into Duncan's five-stage lifecycle to generate standardized PR and crisis analysis reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install sentiment-analyzer-pro
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install sentiment-analyzer-pro using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Sentiment Analyzer Pro?
Sentiment Analyzer Pro is a professional methodology framework tool designed to structure, track, and analyze public opinion events. Built on the authoritative Duncan five-stage methodology (Inception, Outbreak, Climax, Mitigation, and Cooling), this skill helps PR teams, government relations specialists, and brand managers dissect complex crises and design feedback loops. As one of the most powerful analytical Openclaw Skills, it acts as a structured playbook rather than a simple real-time engine, establishing clear metrics for evaluation across breadth, precision, and depth.
By leveraging this tool within the Openclaw Skills ecosystem, organizations can train teams, design internal responses, draft framework crisis reports, and conduct rigorous post-event reviews. It translates raw event timelines into high-quality, actionable crisis management strategies, ensuring that communication is guided by system-level insights instead of reactive guesswork.
Sentiment Analyzer Pro Use Cases
- Corporate Crisis Post-Mortems: Conduct deep-dive reviews of brand PR crises to extract lessons and optimize response workflows.
- PR & Communication Team Training: Teach and align internal teams on standardized crisis communication models using the Duncan framework.
- Drafting Executive Reports: Quickly build framework-level crisis reports with structured timelines and benchmarking comparisons.
- Risk Prediction and Simulation: Assess early-stage event signals and simulate potential outbreak pathways on social media.
- Policy and Brand Monitoring Refinement: Set up clear internal threshold policies based on benchmarked public feedback dynamics.
How Sentiment Analyzer Pro Works
- Sentiment Nature Assessment: Instantly classify the nature of the public opinion event (Major Emergency, General Event, or Micro-Sentiment) to determine the appropriate depth of the analytical pipeline.
- Multi-Channel Data Gathering: Pull and structure event data from at least three independent sources, spanning mainstream media, social platforms, and vertical community forums.
- Five-Stage Alignment: Map the chronological data points to Duncan's lifecycle stages (Inception, Outbreak, Climax, Mitigation, Cooling) using baseline ecological thresholds.
- Three-Dimensional Evaluation: Verify each stage against strict quality benchmarks for analytical breadth (independent sources), precision (timestamps), and depth (analytical insights over mere summarization).
- Comparative Benchmarking: Contrast the target event's progression, response times, and sentiment spikes against historical benchmarks and cross-industry crises.
- Actionable Report Output: Compile the analysis into a standardized Markdown report complete with core conclusions, timeline breakdowns, and three concrete, tracking-ready recommendations.
Sentiment Analyzer Pro Setup
To integrate and run Sentiment Analyzer Pro locally or in your AI workspace, configure your workspace directory to access the methodology resources:
- Ensure you have the core references and templates placed in your workspace folder:
mkdir -p references assets
touch references/五阶段方法论详解.md references/舆情分析案例库.md assets/五阶段报告模板.md
- Configure your AI agent or Openclaw Skills runner with the following default configuration in your environment file:
export SKILL_SLUG="sentiment-analyzer-pro"
export SKILL_VERSION="1.0.0"
- Initialize the analysis template and begin processing event descriptions by feeding chronological data inputs into the agent's workspace.
Sentiment Analyzer Pro Data Schema & Taxonomy
Sentiment Analyzer Pro relies on structured markdown references and template schemes to enforce high analytical quality. Below is the file structure schema used by the skill:
| Directory | File Name | Role | Description |
|---|---|---|---|
references/ |
五阶段方法论详解.md |
Core Methodology | Comprehensive theory guidelines for Duncan's Five Stages. |
references/ |
舆情分析案例库.md |
Benchmarking Database | Database of historical sentiment events used to compare crisis timelines. |
assets/ |
五阶段报告模板.md |
Output Blueprint | Pre-formatted markdown skeleton for the standardized 5-stage report. |
Report Metadata Schema
Each generated analysis report automatically structures its header with the following metadata taxonomy:
- Event Name: Main identifier of the public opinion event.
- Analysis Time: Time ISO timestamp
YYYY-MM-DD HH:MMof the generated report. - Event Summary: A crisp, single-sentence overview of the incident.
- Current Lifecycle Stage: The current phase (Phase 1 to 5) the event resides in.
Sentiment Analyzer Pro Advanced Features
- Three-Dimensional Quality Audit: Self-checks generated reports against strict breadth, precision, and depth matrices to prevent shallow content generation.
- Cross-Platform Synchronization: Adapts to data inputs across a variety of ecosystems (Weibo, Red/Xiaohongshu, Douyin, WeChat, mainstream news networks, and internal ticketing records).
- 2026 Ecological Threshold Baseline: Employs baseline metrics calibrated to modern social media algorithms, making it highly accurate for identifying potential viral triggers.
- Dual-Incident Co-Evolution Tracking: Supports nesting and analyzing multiple interrelated events cascading simultaneously, evaluating cross-event feedback loops.
- Pre-Check Fallback Mechanisms: Automatically flags insufficient user inputs and requests missing target data (such as actor roles, triggers, or timestamps) before drafting standard reports.
SKILL.md
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