A context-aware recommendation engine that learns user preferences and researches options to provide highly personalized matches.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install recommend
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 recommend 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 Recommend Skill?
The Recommend skill is a sophisticated logic module for Openclaw Skills designed to transform raw data into actionable, personalized suggestions. Unlike static recommendation engines, it uses a deep context-gathering phase to understand the user's current mood, historical preferences, and specific constraints. By synthesizing information from multiple sources, it ensures that every recommendation is backed by research and aligned with specific user values.
This skill is essential for developers and users who need an AI agent capable of anticipating needs rather than just reacting to prompts. By integrating with Openclaw Skills, the Recommend module evolves over time, using adaptive learning to refine its output based on whether a user accepts, modifies, or rejects a proposal.
Recommend Skill Use Cases
- Personalized tool or software library suggestions based on technical history.
- Tailored travel or dining options based on specific dietary constraints and mood.
- Researching and ranking vendor options for project procurement.
- Strategic decision-making support by weighing tradeoffs between multiple candidates.
How Recommend Skill Works
- Context Gathering: The skill scans designated sources to find 3-5 relevant user signals before proceeding.
- Preference Extraction: It identifies core dimensions including values, constraints, history, and current mood to build a profile.
- Breadth-First Research: The agent searches for candidates, prioritizing source quality, recency, and actual availability.
- Match & Rank: Options are scored against the extracted preference profile, disqualifying any that violate hard constraints.
- Formatted Recommendation: The agent presents the top 1-3 options with clear justifications, tradeoffs, and a confidence score.
Recommend Skill Setup
To integrate this module into your Openclaw Skills environment, ensure your local configuration points to the correct context sources.
# Install the recommendation module
openclaw install recommend
# Configure source files for context gathering
touch sources.md categories.md
Recommend Skill Data Schema & Taxonomy
The skill organizes data through a structured pipeline to ensure accuracy and traceability within the Openclaw Skills ecosystem:
| Data Object | Description |
|---|---|
| User Signals | 3-5 bullet preference profile extracted from context sources. |
| Candidate Shortlist | 3-7 viable options with key attributes and source quality scores. |
| Alignment Score | Multi-dimensional ranking based on values, constraints, and mood fit. |
| Feedback Loop | Records of accepted or rejected recommendations stored for future adaptation. |
Recommend Skill Advanced Features
- Adaptive learning system that updates preference profiles based on reinforcement or rejection.
- Tradeoff analysis to provide transparency on why one option was chosen over another.
- Confidence level reporting based on the quality and recency of research data.
- Contextual exception handling to differentiate between routine choices and special scenarios (e.g., anniversary vs. casual lunch).
SKILL.md
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