What is the Technical Research Skill

A structured approach to technical spikes, proofs of concept, and technology evaluations to inform decisions.

How long should a timebox be

It is defined in the objective and kept fixed to ensure focused exploration within a known window.

How is the final recommendation justified

Based on a weighted criteria matrix, PoC results, and clearly documented trade offs.

technical-research

npx machina-cli add skill aiskillstore/marketplace/technical-research --openclaw

Files (1)

SKILL.md

1.4 KB

Technical Research Skill

Structured approach to technical spikes, proof of concepts, and technology evaluations.

What This Skill Does

Conducts timeboxed technical investigations
Creates proof of concept implementations
Compares technical options objectively
Documents findings and recommendations
Identifies risks and dependencies
Provides implementation paths

When to Use

Technology selection decisions
Architecture exploration
Feasibility studies
Performance investigations
Library/framework evaluation

Reference Files

references/TECHNICAL_SPIKE.template.md - Structured spike investigation format

Spike Structure

Objective - Clear questions to answer
Timebox - Fixed investigation period
Options - Multiple approaches explored
POC - Working code for each option
Comparison - Weighted criteria matrix
Recommendation - Justified decision

Best Practices

Define success criteria upfront
Explore at least 2-3 options
Create runnable POC code
Document trade-offs honestly
Track unanswered questions
Stay within timebox

Source

git clone https://github.com/aiskillstore/marketplace/blob/main/skills/89jobrien/technical-research/SKILL.mdView on GitHub

Overview

The Technical Research Skill provides a structured approach to timeboxed technical spikes, PoCs, and technology evaluations. It helps teams compare options objectively, document findings, identify risks, and outline concrete implementation paths.

How This Skill Works

It starts with an Objective and a Timebox, then explores multiple Options, develops a PoC for each option, compiles a Comparison (criteria matrix), and ends with a Recommendation. This Spike Structure ensures repeatable, transparent evaluations and traceable decision rationale.

When to Use It

Technology selection decisions
Architecture exploration
Feasibility studies
Performance investigations
Library/framework evaluation

Quick Start

Step 1: Define objective and timebox
Step 2: Build PoCs for at least 2 options
Step 3: Create a comparison and produce a recommendation

Best Practices

Define success criteria upfront
Explore at least 2-3 options
Create runnable POC code
Document trade-offs honestly
Stay within timebox

Example Use Cases

Choosing a database technology for a new service
Evaluating frontend UI frameworks for a high traffic app
Comparing Kafka vs RabbitMQ for a streaming workload
Prototyping a caching layer to speed up a critical path
Assessing cloud storage options for archival data

Frequently Asked Questions

Add this skill to your agents