Claude Skills by tony

Use when taking one ticket or a related ticket group into its own branch and git worktree for implementation.

Use when fanning several tickets into parallel branches and git worktrees, with related tickets grouped together.

Use when sending a message to another Codex/Claude session or telling an agent in another tmux pane a status, finding, instruction, or schema change.

Write the tier-2 internal case study — situation, what was built, tagged outcomes with denominators, lessons, replication guide for other teams

Write the tier-3 public case study — hard sanitization, every headline claim triangulated against external evidence, candid limitations mandatory

Write the tier-3 one-page announcement — figures drawn only from the public case study, denominators attached, limitations one-liner included

Use when rendering a tier-0 leadership report from a completed business-value run, with SCQA and a conservative value case.

Render the tier-1 org-wide projection — explicit adoption and realization inputs, scenario spread, sensitivity ranking, plain-language company close

Use when collecting evidence for the business value of an AI skill or workflow into a provenance-tagged research package.

Update the branch's own changelog entries to match its current net change; commits only with --commit

Rebase out the branch's earlier changelog commits and regenerate its entries fresh; commits only with --commit

Generate CHANGES entries from branch commits and PR context

Create a git commit following project conventions

Reclaim disk space through a proof-gated plan — clears regenerable caches, merges proved-redundant copies, and protects agent history

Survey disk usage across every filesystem layer and classify what is reclaimable, without deleting anything

Use when disk space is low; find large directories, free space, decide if Claude sessions or npm cache are safe to remove, or shrink WSL.

Use when prior verification was framed as a revision log and the user wants a clean, standalone restatement.

Use when asked “are you sure/certain” or “I don't trust that”; recheck code/files from source and repeat prior analysis in full, not just changes.

Use when filing a GitHub issue for a bug, feature, audit, review finding, or investigated piece of work.

Use when Markdown must render: never hard-wrap issue-body paragraphs; fix GitHub code fences, fold logs into details, and pin source links to tags.

Use when a branch or proof of concept should be redone from scratch: throw away the approach, start implementation over, and treat tests as the spec.

Use when commit history must be rebuilt into atomic reviewable commits while preserving the branch's final tree and authorship.

Use when workflow actions are out of date across repos, Dependabot opens action-bump PRs, or pinned actions/checkout needs its latest release.

Update one named GitHub Action to its current version — verify the tag exists, research the upgrade, and commit it with release links

Use when updating every outdated GitHub Action across one repository or a fleet, one researched change at a time.

Use when tightening named files or pasted text in place by removing verbose prose, brittle references, and low-value noise.

Use when existing source comments or docstrings are bloated, dense, repetitive, AI slop, or should be trimmed, debloated, and kept light.

Use when commit messages or prose must stay tight: cut filler, padding, preamble, AI fluff, and stories of attempts; lead with current results.

Merge a set of PRs one at a time — detect stack vs independent set, rebase and resolve conflicts between merges, watch CI, merge each via gh

Merge one PR via gh with a merge commit matching the repo's git history — readiness-gated, CI-watched, trunk synced after

Use when delegating to Google Gemini or Antigravity for a second opinion from Google's model; falls back through Gemini and agent CLIs.

Use when asked to use Codex, GPT, OpenAI's model, or the Codex CLI to delegate a task; falls back to the GPT agent when needed.

Use when asked to use Cursor, Cursor's CLI, or the agent binary to delegate a prompt or task; no fallback is available.

Use when invoking Google's Gemini directly through the shared Antigravity, Gemini CLI, or agent fallback chain.

Use when invoking OpenAI GPT directly through the same Codex CLI or agent fallback used by the codex skill.

Use when updating one named dependency to a target version everywhere it is pinned across one repository or a fleet.

Use when updating one runtime or toolchain pin across version files, package metadata, CI, and other repositories.

Use when bringing every outdated dependency and toolchain pin current across one repository or a fleet.

Use when packages are out of date across repos; bump dependencies, refresh uv.lock, update .tool-versions/packageManager pins, and commit each.

Use when cleaning AI slop, verbose commit messages, brittle references, or low-value changes from a branch before review.

Generate a gold-standard merge commit message from branch diff

Refresh an existing PR description to match the branch's current net change, preserving structure and customizations

Review a PR description against gold-standard patterns

Rewrite an existing PR description from scratch against the branch's current net change, carrying forward context that still matters

Generate a gold-standard pull request description from branch diff

Use when starting or re-baselining a pytest optimization pass by profiling tests and fixtures without editing the suite.

Use when benchmarking pytest optimization hypotheses from 00-scan in isolation to identify speedups that beat measured noise.

Use when ranking validated pytest speedups from 01-benchmark into an approved, ordered, commit-by-commit plan.

Use when applying an approved pytest optimization plan as separate verified commits with resumable progress.

Use when a pytest suite or its fixtures are slow and need measured, safety-gated optimization across the full pipeline.