OpenSpec vs Superpowers vs Spec Kit: SDD Patterns
Compare OpenSpec vs Superpowers vs GitHub Spec Kit through practical SDD patterns: specs, plans, tasks, tests, review gates, and evidence.
A focused archive of AI Coding articles for spec-first teams.
Compare OpenSpec vs Superpowers vs GitHub Spec Kit through practical SDD patterns: specs, plans, tasks, tests, review gates, and evidence.
A copy-ready packet for giving AI coding tools a bounded task, acceptance criteria, file ownership, tests, and review evidence before code generation starts.
Superpowers enforces spec-first discipline for AI coding agents through brainstorming, specs, plans, TDD, verification, and repeatable review evidence.
AI coding tools drift without constraints, adding fields, renaming functions, expanding scope, and inventing tests. Spec-first prompts keep changes reviewable.
Govern AI-assisted coding with spec-driven prompts: define scope, boundaries, evidence, and audit trails before generated code reaches review.
Review AI-generated pull requests against acceptance criteria: inspect the diff, run evidence checks, and catch failures a quick skim misses.
Use a pre-merge risk register for AI-generated code: flag auth, data, contract, migration, rollback, and observability risks.
Use test-evidence gates for AI-generated code: require meaningful tests before merge and catch hallucinated implementations before release.
Manage API changes for AI-generated clients with structured changelogs, announcement channels, compatibility rules, and CI gates.
Design API error taxonomies AI-generated clients can use, with stable codes, retry categories, and machine-readable details.
Design API specs for LLM-powered agentic clients with discoverable fields, idempotency, dry-runs, semantic descriptions, and safe destructive actions.
Follow a vague support ticket as it becomes a shippable technical spec using Spec Skills, guided questions, and review-ready output.
See how Spec Skills fits spec-first delivery through constrained prompts, spec injection, boundary enforcement, and reviewable AI output.
Quality gates for AI-assisted code: pre-prompt spec checks, diff review, test evidence, and human sign-off before generated code ships.