Orchestrate an end-to-end sprint to add a feature to an existing codebase.
npx skills add https://github.com/garagon/nanostack --skill feature-garagon
Please help me install this Agent Skill. Skill: feature Source: https://github.com/garagon/nanostack/tree/main/feature Command: npx skills add https://github.com/garagon/nanostack --skill feature-garagon
Enables rapid delivery of a known feature by running a complete sprint (plan → build → review → security → qa → ship) automatically without pausing for user confirmations. It skips the diagnostic /think step and assumes the project already exists and the requester knows the desired outcome.
Run /feature Add import from JSON/CSV to restore backups to execute a fast sprint that plans, builds, reviews, audits, and ships the feature.
High-intent search queries and answers about installing and using this skill.
Automated sprint orchestration runs plan, build, review, security, QA, and ship phases continuously. You provide the desired feature and existing project, and the flow executes end-to-end without pausing for manual confirmations.
Yes, commit-time enforcement blocks git commits until plan, review, security, and QA artifacts are present and current. A PreToolUse hook validates artifact presence before allowing any commit to proceed.
Parallel validations execute review, security, and QA phases simultaneously when possible. If blocking issues are found, only the failing phases re-run automatically, ensuring fast feedback without redundant processing.
Yes, jq is a required dependency to run the end-to-end sprint orchestration. You must have this command-line JSON processor installed in your environment before initiating the automated workflow.
Running a fast automated sprint plans, builds, reviews, audits, and ships incremental features in one continuous flow. It skips diagnostic steps and assumes you already know the desired outcome and have an existing project.
Avoid automated sprint orchestration when the project does not exist yet or the desired feature is undefined. It skips diagnostic reasoning and requires a clear outcome and existing codebase to function correctly.
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