Pytest optimizer 02 plan

Skill tony/ai-workflow-plugins/.agents/skills/pytest-optimizer-02-plan

Claude Code Plugins, Commands, and Skills

Install

npx -y skills add tony/ai-workflow-plugins --skill pytest-optimizer-02-plan

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Phase 3 of the pytest-optimizer pipeline. Rank the validated speedups from 01-benchmark into an ordered commit plan. Scores each candidate with the weighted rubric (safety, impact, effort, confidence, reversibility), drops anything below the hard safety gate, orders the survivors (safety-gate fixes and typings first; scope and consolidation before parallelism), and drafts one why/what commit per speedup with its verify command. Runs inside plan mode and presents the plan for approval. Writes plan.json. Use after 01-benchmark to decide what to apply and in what order.

SKILL.md

3.6 KB, as published. Nobody here has run it

02-plan

Turn measured candidates into a reviewable commit plan. This phase only reads the suite and writes plan.json; it makes no code changes.

$ARGUMENTS may pass --max-commits=N to cap the plan, --min-score to raise the inclusion threshold, and --force to recompute.

Step 1: Load and score

Read benchmarks.json and baseline.json. Score every validated candidate with references/scoring-rubric.md:

total = 0.35*safety + 0.30*impact + 0.15*effort
      + 0.12*confidence + 0.08*reversibility

Apply the hard gates first: drop any candidate with safety < 0.4 or impact == 0. For each dropped item, capture the prerequisite refactor (if any) as a separate, clearly-labeled follow-up — not as an auto-applied commit.

Step 2: Order

Score sets priority; these constraints override it where they apply:

Safety-gate fixes first (make collection deterministic, prove order independence) before any parallel/reorder speedup.
Typing and parametrize migrations early (low-risk, readable diffs).
Scope and consolidation before parallelism.
One speedup per commit — never bundle.

Honor --max-commits / --min-score.

Step 3: Draft commits

For each planned item, draft a commit from templates/commit-message.tmpl, adapting the type(scope) prefix to the target project's convention (read from its AGENTS.md/CLAUDE.md). Each entry records: order, heuristic id, the score breakdown, target files, the draft subject/body, the verify command (the project test + quality checks), and any depends_on. Write plan.json and update state.json (phase=plan, plan hash).

Step 4: Present for approval (plan mode)

This phase is a decision point. Enter plan mode (Claude Code: EnterPlanMode; others: /plan or Shift+Tab). Present the 02-plan sections from references/output-contract.md: ## Ranked plan, ## Dropped at the gate, ## Ordering rationale. Let the user reorder, drop items, or cap the count. Because plan mode is the decision point, omit the ask-user-choice panel here. Exit plan mode once approved; plan.json is the contract the pytest-optimizer-03-execute skill consumes.

Portability notes

ask-user-choice — present the listed options and wait for the user to pick one. Hosts with a structured multiple-choice tool (Claude Code's AskUserQuestion) should use it; otherwise print a numbered list and wait for a numbered reply. Never proceed on an assumed answer.
$ARGUMENTS — the text the user passed when invoking this skill. If your host does not substitute it, read it as the user's request in the current turn, and ask when there is none.
Bundled files — every relative path in this skill points at a file shipped inside this skill directory. Read them from here, not from the host's plugin tree.