A low-friction reflection tool that transforms messy brain-dumps into concise, supportive summaries of your day or week.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install soft-landing
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 soft-landing 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 Soft Landing?
Soft Landing is a specialized reflection skill designed for the moments when you are mentally overloaded or simply too tired to write a structured journal entry. It acts as a thoughtful companion, helping you make sense of your efforts by identifying invisible work, sources of friction, and gentle patterns without the pressure of productivity coaching or formal performance tracking. By utilizing Openclaw Skills like this one, users can transition from a scattered state of mind to a more settled perspective in seconds.
This skill is particularly effective at recognizing the emotional and cognitive labor often missed by traditional task managers. Whether you provide a single sentence or a fragmented brain-dump, Soft Landing synthesizes your input into a six-part reflection card that validates your effort and provides a light, actionable path forward for the next day or week.
Soft Landing Use Cases
- Reviewing a busy or scattered day when you have limited mental energy.
- Catching up on a missed reflection from yesterday to clear mental debt.
- Summarizing a complex week to identify recurring themes and sources of drain.
- Processing a messy brain-dump or voice-to-text message into a coherent summary.
- Identifying invisible work like coordination, context switching, and emotional labor.
How Soft Landing Works
- The user triggers the skill with a phrase like review today or help me wrap up the week.
- If the timeframe is unclear, the skill asks exactly one clarifying question to identify the scope.
- The user provides input, which can be anything from a few keywords to a rough paragraph or multiple short messages.
- The skill analyzes the input to identify the shape of the period, tasks handled, and hidden stressors.
- A six-part reflection card is generated, including a gentle observation of a potential pattern and a specific acknowledgment.
- The user can add more details to refine the reflection, which the skill integrates into the existing summary.
Soft Landing Setup
To integrate Soft Landing into your environment, ensure you have the Openclaw Skills framework installed and the skill file added to your agent's configuration directory.
# Example command to add the skill to your local repository
cp soft-landing.md ./skills/soft-landing.md
No complex API keys are required; simply ensure your AI agent has read access to the markdown content to begin processing reflections.
Soft Landing Data Schema & Taxonomy
Soft Landing generates structured reflection cards based on the following taxonomy:
| Section | Description | Data Type |
|---|---|---|
| Timeframe | Today, Yesterday, or Week | String |
| Shape | 1-2 sentence summary of the period's rhythm | String |
| Handled Items | 2-4 representative streams of effort or invisible work | List |
| Drain Source | Primary source of friction or depletion | String |
| Pattern | A gentle observation of a recurring behavior | String |
| Acknowledgment | A specific, non-generic validation of effort | String |
| Note | A low-pressure suggestion for the future | String |
Soft Landing Advanced Features
- Multi-message synthesis: Combines multiple short, rapid-fire messages into a single, cohesive reflection card.
- Invisible effort detection: Automatically identifies cognitive loads like context switching and coordination tasks even if not explicitly named by the user.
- Adaptive follow-up logic: Only prompts for more info if the input is too thin to be useful, adhering to a strict one-question limit to avoid user fatigue.
- Iterative refinement: Allows users to update their reflection with additional details without losing the original context or restarting the process.
- Timeframe context awareness: Switches between daily and weekly structures automatically based on user intent within the Openclaw Skills ecosystem.
SKILL.md
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