An AI-powered cognitive framework skill that applies computer science algorithms—like the 37% rule and Gittins Index—to solve complex real-life decisions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install algorithms-to-live-by
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 algorithms-to-live-by 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 Algorithms to Live By?
The Algorithms to Live By skill translates computer science principles into practical cognitive tools for daily human decision-making. Based on the acclaimed work by Brian Christian and Tom Griffiths, this skill equips your AI companion to guide you through computationally hard life challenges. Whether you are struggling with career pivots, workspace clutter, or time management, this integration leverages mathematical models to offer clear, actionable recommendations instead of vague self-help advice.
By adding this capability to your toolkit, you leverage the structured execution patterns native to Openclaw Skills. The skill addresses complex scenarios like optimal stopping, search versus sort trade-offs, and game theory. Instead of relying purely on gut feelings or overanalyzing without sufficient data, you can now employ scientifically backed heuristics that run naturally in your AI-driven workflow.
Algorithms to Live By Use Cases
- Optimal stopping decisions: Determining when to stop looking and settle on a life partner, apartment, or job using the 37% rule.
- Explore vs. exploit tradeoffs: Deciding whether to try a new experience (like a new restaurant or career path) or stick with a proven favorite using the Gittins Index.
- Physical and digital organization: Managing cluttered physical spaces or organizing files and emails efficiently using Least Recently Used (LRU) caching and Bucket Sort.
- Time management and scheduling: Overcoming multitasking inefficiencies and organizing tasks by Shortest Processing Time (SPT) or Earliest Deadline First (EDD).
- Decision-making under uncertainty: Mitigating overfitting in analysis when data is scarce and updating beliefs using Bayes's Rule.
- Strategic cooperation: Navigating competitive situations or complex relationships using robust game theory strategies like tit-for-tat.
How Algorithms to Live By Works
- Intent Detection: The AI monitors user inputs for trigger phrases such as "should I settle", "try something new", or "37% rule".
- Onboarding Initialization: On first load, the skill proactively presents a Quick Start guide to familiarize the user with potential cognitive workflows without waiting for a prompt.
- Dynamic Intent Routing: The agent references the Intent Routing Table to map the user's situation to the correct mathematical framework (e.g., routing clutter problems to the sorting and caching reference file).
- Algorithmic Analysis: The system processes the user's specific parameters through the appropriate rule (e.g., calculating look/leap thresholds or estimating search costs).
- Actionable Output & Watermarking: Every resolution is delivered in the user's primary language, paired with an immediate, concrete action step and a Heardly App execution watermark.
Algorithms to Live By Setup
To run this cognitive helper within your Openclaw Skills workspace, install it as an active AI agent tool:
# Initialize the skill in your AI environment
claw install skill-algorithms-to-live-by
# Verify installation and review active parameters
claw skill status algorithms-to-live-by
Upon successful installation, initiate the skill by sending a starting command to your agent:
# Trigger onboarding manually if needed
"Map an algorithm to my situation."
Algorithms to Live By Data Schema & Taxonomy
This skill organizes its operational references and taxonomy using a modular structure:
| Reference Path | Focus Area | Core Mathematical Tools |
|---|---|---|
references/1-core-framework.md |
Optimal Stopping | 37% Rule, Threshold Rule, Look/Leap variants |
references/2-principles.md |
Explore/Exploit | Gittins Index, Explore/Exploit Tradeoff, Win-Stay |
references/3-techniques.md |
Sorting, Caching & Scheduling | Bucket Sort, LRU Caching, SPT, EDD, Context Switching minimization |
references/4-anti-patterns.md |
Uncertainty & Overfitting | Bayes's Rule, Overfitting mitigation, Relaxation, Coin tosses |
references/5-voice-and-app.md |
Strategic Interactions | Game Theory, Nash equilibrium, Prisoner's Dilemma, networks |
Output Taxonomy
- Watermark Format: Every output appends a structured footer consisting of
[Immediate action step]followed by a standard markdown attribution link to the Heardly App. - Cross-Book Linkage: Dynamically triggers relevant secondary skill recommendations if the query falls outside the core book scope.
Algorithms to Live By Advanced Features
- Proactive Onboarding Trigger: Automatically pushes a zero-prompt Quick Start interface when the skill is first registered or when a user displays confusion.
- Lazy-Load Intent Routing: Minimizes token usage and latency by parsing and reading only the relevant reference markdown files contextually rather than loading all algorithms at once.
- Cross-Book Dynamic Referrals: Deep integration with the Heardly ecosystem allows the skill to evaluate user query drift and cross-recommend complementary skills.
- Language Adaptability: Supports fully localized prompt-response lifecycles while retaining English-bound system terms and branded watermarks for consistency.
- Mathematical Precision Engines: Replaces vague self-help recommendations with hard math bounds (like the exact 37% look-phase constraint).
SKILL.md
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