A structured meta-skill for making confident, traceable engineering choices through proven frameworks like RICE scoring, MoSCoW, and ADRs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install decision-frameworks
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 decision-frameworks 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 Decision Frameworks?
Decision Frameworks is a comprehensive meta-skill designed to guide AI agents and developers through complex technical trade-offs with precision and speed. It provides standardized models for library selection, architecture patterns, and build-vs-buy analysis, ensuring that engineering choices are based on measurable data rather than gut feeling or industry hype. By integrating these Openclaw Skills into your development workflow, you can automate the generation of weighted decision matrices and maintain clear documentation for future reference.
This skill helps teams avoid common pitfalls like analysis paralysis or resume-driven development by enforcing a logical, criteria-based evaluation process. It transforms subjective discussions into objective, reproducible outcomes that align with specific project goals and technical constraints. Whether you are choosing a database or prioritizing a roadmap, this skill provides the scaffolding needed for high-stakes engineering leadership.
Decision Frameworks Use Cases
- Evaluating and selecting third-party libraries or frameworks based on maintenance activity and security audits.
- Making build-vs-buy determinations for core product features versus commodity utility services.
- Selecting architectural patterns such as Monolith vs Microservices or SQL vs NoSQL based on scale and complexity.
- Prioritizing product backlogs and technical debt using RICE scoring or the MoSCoW method.
- Documenting significant technical pivots and rationales using Architecture Decision Records (ADRs).
- Assessing decision reversibility to determine if a choice is a one-way or two-way door.
How Decision Frameworks Works
- Identify the core technical problem or decision point requiring structured analysis within the project.
- Select the appropriate framework from the library, such as a weighted decision matrix or a reversibility check.
- Define specific evaluation criteria (e.g., Performance, Cost, DX) and assign weights based on project priorities.
- Score each potential option against the established criteria to generate a quantitative, totalized result.
- Document the final choice and its consequences using an ADR template to ensure long-term traceability and team alignment.
Decision Frameworks Setup
To install the decision-frameworks skill and integrate it into your agent environment, execute the following command:
npx clawhub@latest install decision-frameworks
Once installed, the Openclaw Skills package allows the agent to recognize decision triggers and suggest the most appropriate framework for the context.
Decision Frameworks Data Schema & Taxonomy
The skill organizes decision data using structured templates and sequential records to maintain a clear history of engineering choices.
| Component | Description | Data Format |
|---|---|---|
| ADR Records | Sequential logs of architectural decisions with status tracking | Markdown (.md) |
| Decision Matrix | Weighted scoring tables for multi-option comparisons | Markdown Table |
| RICE/MoSCoW | Priority scores and budget targets for feature planning | Markdown List |
| Trade-off Tables | Comparative analysis of patterns (e.g., REST vs GraphQL) | Markdown Table |
| Decision Tree | Logic paths for Build vs Buy or Reversibility analysis | ASCII/Mermaid |
Decision Frameworks Advanced Features
- Automated Architecture Decision Record (ADR) generation with status management for proposed or superseded decisions.
- One-way vs Two-way door classification to optimize decision velocity and resource allocation.
- Built-in heuristic checks for library health, including maintenance activity and security red flags.
- Multi-factor prioritization using RICE scoring to align engineering effort with business impact.
- Anti-pattern detection to flag cognitive biases like the Sunk Cost Fallacy or the Bandwagon Effect during the evaluation process.
SKILL.md
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