reclaimit

Create Gentle AI pull requests with issue-first checks. Trigger: creating, opening, or preparing PRs for review.

Trigger: PRs over 400 lines, stacked PRs, review slices. Split oversized changes into chained PRs that protect review focus.

Design docs that reduce cognitive load. Trigger: writing guides, READMEs, RFCs, onboarding, architecture, or review-facing docs.

Write warm, direct collaboration comments. Trigger: PR feedback, issue replies, reviews, Slack messages, or GitHub comments.

Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.

Create Gentle AI issues with issue-first checks. Trigger: creating GitHub issues, bug reports, or feature requests.

Trigger: judgment day, dual review, adversarial review, juzgar. Run blind dual review, fix confirmed issues, then re-judge.

Implement SDD tasks from specs and design. Trigger: orchestrator launches apply for one or more change tasks.

Archive a completed SDD change by syncing delta specs. Trigger: orchestrator launches archive after implementation and verification.

Create the SDD technical design and architecture approach. Trigger: orchestrator launches design for a change.

Explore SDD ideas before committing to a change. Trigger: orchestrator launches exploration or requirement clarification.

Trigger: sdd init, iniciar sdd, openspec init. Initialize SDD context, testing capabilities, registry, and persistence.

Walk users through the SDD workflow on the real codebase. Trigger: orchestrator launches onboarding for the full SDD cycle.

Create an SDD change proposal with intent, scope, and approach. Trigger: orchestrator launches proposal work for a change.

Write SDD delta specs with requirements and scenarios. Trigger: orchestrator launches spec work for a change.

Break an SDD change into implementation tasks. Trigger: orchestrator launches task planning for a change.

Trigger: SDD verification phase, verify change. Execute tests and prove implementation matches specs, design, and tasks.

Trigger: new skills, agent instructions, documenting AI usage patterns. Create LLM-first skills with valid frontmatter.

Trigger: improve skills, audit skills, refactor skills, skill quality. Audit and upgrade existing LLM-first skills.

Trigger: update skills, skill registry, actualizar skills, after skill changes. Index available skills by trigger and path.

Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.