fiware-knowledge-api

Perform semantic search across official FIWARE GitHub repositories for component information.

Updated Mar 27, 2026
One-click install
npx skills add https://github.com/BenjaminLTakaki/fiware-deployment --skill fiware-knowledge-api
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: fiware-knowledge-api
Source: https://github.com/BenjaminLTakaki/fiware-deployment/tree/main/.claude
Command: npx skills add https://github.com/BenjaminLTakaki/fiware-deployment --skill fiware-knowledge-api

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the need to manually sift through outdated FIWARE documentation or unvetted community content by providing direct access to real commit history, diffs, and deployment context from official FIWARE GitHub repositories, ensuring answers are accurate and traceable.

Core Features & Use Cases

  • Semantic Search: Query natural language questions about FIWARE components to retrieve relevant, pre-formatted context from official repositories.
  • Cited Sources: All results include verifiable source metadata including repository name, commit hash, date, and commit message for full traceability.
  • Use Case: A researcher implementing a FIWARE data space connector can query this API to get exact Helm chart configuration values and recent bug fixes directly from the official helm-charts repository, avoiding errors from outdated tutorials.

Quick Start

Use the fiware-knowledge-api skill to ask a natural language question about any FIWARE component deployment or configuration and receive a cited, accurate answer sourced from official GitHub repositories.

Frequently Asked Questions about fiware-knowledge-api

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find accurate FIWARE deployment configurations directly from source repositories?

To find accurate FIWARE deployment configurations, you can use semantic search across official GitHub repositories to retrieve real commit history, diffs, and pre-formatted context blocks. This ensures deployment answers are accurate and fully traceable to the source.

Why does my FIWARE component setup fail when using outdated tutorials?

FIWARE component setup fails with outdated tutorials because they lack recent bug fixes and exact Helm chart values. Sourcing configuration directly from official GitHub repositories via semantic search avoids these errors by providing current, verified deployment context.

Can I get cited source metadata for FIWARE troubleshooting answers to use in my LLM?

You can get cited source metadata for FIWARE troubleshooting to use in LLM systems by retrieving pre-formatted context blocks. All results include verifiable metadata like repository name, commit hash, date, and commit message for full traceability.

What is the best way to query natural language questions about FIWARE software components?

The best way to query natural language questions about FIWARE software components is using an API that performs semantic search across official repositories. This provides developers and researchers pre-formatted context blocks for integration with LLM systems.

Does this semantic search approach work for finding recent bug fixes in FIWARE Helm charts?

This semantic search approach works for finding recent bug fixes in FIWARE Helm charts by querying the official helm-charts repository. It retrieves exact configuration values and recent diffs directly, eliminating manual sifting through unvetted community content.

What limitations exist when relying on unvetted community content for FIWARE data space connector implementation?

Relying on unvetted community content for FIWARE data space connectors risks outdated configuration values and missing bug fixes. Semantic search of official GitHub repos eliminates this by providing exact, cited source metadata including commit hashes and dates.