Pytest optimizer 00 scan

Skill tony/ai-workflow-plugins/.agents/skills/pytest-optimizer-00-scan

Claude Code Plugins, Commands, and Skills

Install

npx -y skills add tony/ai-workflow-plugins --skill pytest-optimizer-00-scan

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What its author says it does

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Phase 1 of the pytest-optimizer pipeline. Profile the suite and emit hypotheses without editing any test. Detects pytest + plugin versions and gates capabilities, establishes a reproducible timing baseline with a measured noise band, ranks the slowest test bodies and the slowest fixture setup/teardown separately, and runs the static detectors (unused/mis-scoped/ duplicate fixtures, mark-on-fixture, typing and parametrize gaps). Writes baseline.json, capabilities.json, and hypotheses.json to the memory directory. Read-only on the test suite. Use when starting a pytest optimization pass or re-baselining after changes.

SKILL.md

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00-scan

Profile, detect, hypothesize. This phase only reads the suite and only writes JSON to the memory directory. No test is edited here.

$ARGUMENTS may scope the run to a path or marker, set --runs=N (baseline sample count, default 5), --force to ignore the idempotency token, and --memory-dir to override the memory location.

Step 1: Preflight

Detect preferred tools (rg/ag/fd/jq/uv/uvx) and read the project's own test command from AGENTS.md, CLAUDE.md, justfile, tox.ini, or pyproject.toml. Do not hardcode a runner. Record the resolved command; every later phase reuses it. Substitute it wherever this file writes pytest.

Step 2: Resolve memory and check idempotency

Resolve the memory directory per references/memory-schema.md (repo-root .pytest-optimizer/, gitignored; XDG fallback). Compute the idempotency token (git HEAD + collection node-ids + pytest/plugin versions + environment fingerprint). If state.json shows scan completed with an unchanged token and hypotheses.json exists, report "already scanned" and stop — unless --force.

Step 3: Detect capabilities

Probe the installed pytest and plugins against references/version-matrix.md. Write capabilities.json. Use only available features below; fall back as the matrix directs (e.g. no --durations-min pre-6.1 → post-filter rows).

Step 4: Establish the baseline and noise band

Run the suite serially, cache disabled, --runs times, and record total wall-time each run:

pytest -p no:cacheprovider -p no:randomly -q

Compute the median and MAD of the per-run totals; set the noise band (median + k·MAD, default k = 3) and the ~50ms trust floor. Write the env fingerprint, versions, capabilities, and the band to baseline.json. See references/durations-parsing.md.

Step 5: Profile slowest tests and fixtures

Capture per-phase timings and bucket rows by the when token (call vs setup/teardown), de-duplicating by (nodeid, when):

pytest --durations=0 --durations-min=0.005 -p no:cacheprovider -p no:randomly

Rank slowest test bodies (call, H01) and slowest fixture setup/teardown (setup/teardown, H02). For shared higher-scope fixtures whose cost is billed to one nodeid (H03), note the attribution; offer the opt-in timing plugin if precise per-fixture cost is needed. Persist the timings to baseline.json.

Step 6: Run the static detectors

Work through references/heuristic-catalog.md, using references/fixture-analysis.md for the fixture recipes and references/parametrize-convention.md for the typing/ parametrize gaps:

Fixtures: unused (H08/H09/H41), mis-scoped (H10/H13), duplicate (H11), autouse (H12/H15), mark-on-fixture (H43), empty conftest (H42).
Collection/capability errors (H14/H22/H44).
Typing and parametrize gaps (H26–H32).
I/O, sleep, markers, timeouts (H15/H33/H37/H39/H40).
Recomputation and duplication (H24/H25/H34/H35/H36/H38).

Apply the getfixturevalue guard (H09) before proposing any fixture deletion.

Step 7: Emit hypotheses

Write hypotheses.json: one entry per firing detector, each tagged with its heuristic id, the goal(s) it addresses, the raw evidence, and prior risk/effort. Do not estimate impact here — 01-benchmark measures it. Update state.json (phase=scan, completed.scan=true, token).

Step 8: Report

Emit the 00-scan sections from references/output-contract.md: a hero block, then ## Environment, ## Baseline, ## Slowest tests, ## Slowest fixtures, ## Hypotheses. Close with an ask-user-choice panel offering to benchmark the hypotheses, narrow scope, or stop.

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.