tony/ai-workflow-plugins

Take tickets to branches in isolated git worktrees — resolve Linear/GitHub issues strictly read-only, name branches by the team's own conventions, land gated commits, and fan out multiple tickets in parallel

Measure and report the business value of AI skills and agentic workflows — provenance-tagged data collection and audience-tiered reports in engineer-hours and cycle time, never currency

Generate categorized changelog entries from branch commits and PR context; refresh or recut a branch's own entries as it evolves

Create git commits following project conventions with format enforcement and safety checks

Make verification requests return the re-derived answer instead of a diff against the agent's prior turn — an ambient skill for 'double check' / 'are you sure' moments plus an /align command to repair a chat where the diff already happened

Update GitHub Actions pins across one repo or a whole fleet — verify every target tag exists before writing it, research each upgrade against real release notes, land one commit per action on trunk, then close dependabot's PRs by citing the commit that superseded them

Write tight, slop-free prose and code, and tighten existing files in place with no commits — a model-invocable writing discipline plus a working-tree cleanup command

Merge PRs via gh with merge commits matching the repo's own git history — readiness checks and CI watch before every merge, plus multi-PR runs with stack detection, rebase, and conflict resolution between merges

Run prompts through individual AI CLIs — Antigravity/agy (Gemini), codex/GPT, and cursor/agent with fallback support

Generate, refresh, recut, and review gold-standard PR descriptions; detect AI slop, brittle counts, and verbose commit messages on branch commits and resolve via fixup commits and autosquash with quality-gate checks

Profile a pytest suite, rank safe speedups with a safety-first rubric, and apply each as its own verified commit

Automated rebase onto trunk with conflict prediction, resolution, and quality gate verification

Cut and bump releases with safe defaults — no push, no tag, no tag push without an explicit flag — and roll new releases out to every downstream consumer repo with CI verification

Clone and study your project's dependencies at the exact versions you use — source repos with version-pinned git worktrees

Address code-review findings on the current branch — provenance-gated to changes the branch introduced, one finding per commit, simplest pragmatic fix, with quality gates before every commit and prompted history rewrites

Move one repo or a whole fleet onto a new ruff release — work out which rules the release can actually fire against each repo's own select list, gate on the resolver being able to see the version at all, then land one reviewed commit per rule with the upstream rule doc cited

Gain situational awareness before touching a repository — read the branch against trunk, its diff, its pull request and review threads, its linked tickets, and the project's own conventions, then report where the work stands; optionally search prior AI conversations for decisions the repository never recorded

Scan repo tracked files for AI slop, brittle counts, and verbose noise; resolve each finding as an atomic forward-going commit with quality-gate verification

Prove a path fast in a no-commit spike — a single probe or a bakeoff of competing strategies in git worktrees — exit through the project's quality gates into stashes, and hand back a commit-by-commit implementation plan

Detect and fix inconsistent spacing in Tailwind CSS layouts — container fragmentation, margin/gap mixing, padding asymmetry, and more

TDD bug-fix workflow — reproduce bugs as failing tests, find root cause, fix, and verify

Move Terraform and OpenTofu versions across any repository layout — discover every root module instead of assuming one, move every declaration of a provider together because constraints combine across modules, and refresh each lock file without narrowing its platform coverage

Run independent adversarial participants through host-native sub-agents by default or separate model CLIs by choice — brainstorm, refine, plan, execute, architect, review, and synthesize