mikefarah/yq — Yq is a portable command-line YAML, JSON, XML, CSV, TOML, HCL and properties processor

Description and use

**Brief Summary (2‑3 sentences)**
mikefarah/yq is a fast, portable command‑line tool written in Go that lets you query, transform, and validate data across YAML, JSON, XML, CSV, TOML, HCL and Java properties formats. With a syntax reminiscent of jq, it enables developers and DevOps engineers to script complex data‑processing pipelines without writing custom parsers. Its high star count, active maintenance, and broad format support make it a solid OSS candidate for production use.

**Value**

• **Unified data handling** – One binary can manipulate every major configuration and data serialization format, eliminating the need for multiple specialized tools.
• **AI‑friendly workflow glue** – yq can preprocess prompts, context files, or model outputs (e.g., extracting snippets from YAML‑based knowledge bases) before feeding them into RAG or agent pipelines, accelerating prototype development.
• **Low‑learning curve** – The jq‑style expression language is familiar to many engineers, shortening onboarding time for teams that need quick data wrangling in CI/CD or model‑serving scripts.

**Practical Adoption Path**

1. **Evaluation** – Install the single‑binary via a package manager (brew install yq, apt-get, or download the Go binary) and run the built‑in test suite on a sample data set to confirm compatibility with your formats.
2. **Integration** – Wrap yq calls in shell scripts, Makefiles, or CI pipelines (GitHub Actions, GitLab CI) to perform validation, templating, or conversion steps before invoking AI services.
3. **SDK/CLI Extension** – If deeper integration is needed, invoke the binary from Go, Python (subprocess), or any language that can spawn a process, or embed the Go library directly (the repo provides a public API).
4. **Production rollout** – Pin the version in your infrastructure as a Docker image or binary checksum, add monitoring for exit codes and output size, and incorporate it into your observability stack.

**Production Readiness**

• **Activity & Community** – 15 k+ stars, 772 forks, frequent commits (last update 2026‑05‑14) and a vibrant issue/PR community indicate strong ongoing support.
• **Stability** – Written in Go, compiled to a single static binary, it has minimal runtime dependencies, making it easy to deploy in containers, VMs, or edge devices.
• **Security & Licensing** – Uses an MIT‑compatible license; no critical vulnerabilities reported in recent scans, though a formal security audit and verification of maintainer activity are recommended before a large‑scale rollout.
• **Ecosystem Fit** – Already adopted in many DevOps toolchains (Kubernetes manifests, Helm charts, Terraform HCL), so integrating it with AI pipelines aligns with existing best‑practice patterns.

Overall, yq offers a high‑impact, low‑friction way to add robust data‑processing capabilities to AI‑augmented workflows, and its maturity makes it ready for production pilots after a brief security and licensing review.

Problem

mikefarah/yq helps add AI capability without starting from a blank model stack.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

yq is a portable command-line YAML, JSON, XML, CSV, TOML, HCL and properties processor

Source