TomWright/dasel — Select, put and delete data from JSON, TOML, YAML, XML, INI, HCL and CSV files with a single tool.

Description and use

**Brief Summary (2‑3 sentences)**
TomWright/dasel is a versatile command‑line utility (and Go module) that lets you query, modify, and delete data across a wide range of formats—JSON, TOML, YAML, XML, INI, HCL and CSV—with a single, consistent syntax. Its rich feature set and Go‑native SDK make it easy to embed into scripts, CI pipelines, or larger Go applications, and the project enjoys strong community traction (7.9k ★, 167 forks).

**Value**
Dasel eliminates the need to juggle multiple format‑specific parsers or write custom conversion code, enabling rapid prototyping of AI‑driven workflows (e.g., RAG pipelines, prompt‑engineering agents) that must ingest or emit data in heterogeneous formats. By providing a uniform query language and a Go library, it accelerates feature development and reduces bugs associated with manual data handling.

**Practical Adoption Path**

1. **Exploratory testing** – Use the CLI to run one‑off queries or transformations on sample files, verifying syntax and performance.
2. **Integration** – Add the github.com/tomwright/dasel module to Go services or wrap the CLI in shell scripts for CI/CD, data‑ingestion jobs, or model‑output post‑processing.
3. **Automation** – Combine dasel with orchestration tools (e.g., GitHub Actions, Airflow) to standardize data preparation steps across AI pipelines.

**Production Readiness**
The project shows high production readiness: recent commits (as of 2026‑05‑13), active issue handling, and a sizable star/fork count indicate a healthy community. Its Go implementation is performant and statically compiled, suitable for containerized environments. While a final legal/security audit (license compliance, vulnerability scanning) is still required, the overall signal suggests dasel is ready for serious pilot deployments and can be trusted as a core data‑manipulation component in production AI systems.

Problem

TomWright/dasel 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

Select, put and delete data from JSON, TOML, YAML, XML, INI, HCL and CSV files with a single tool. Also available as a go mod.

Source