phpunit-test-team-reviewing

name	phpunit-test-team-reviewing
version	4.2.2
description	Use this skill when the user asks for a team-based, consensus, multi-reviewer, or red-team review of Shopware PHPUnit tests — trigger phrases like "team review these tests", "consensus review the tests in PR
allowed-tools	Bash, Read, Glob, Grep, AskUserQuestion, Workflow, mcp__plugin_test-writing_test-rules__build_rule_package

Team-Based PHPUnit Test Review

Resolve the input into a mixed test-file manifest, project the agent cost, partition the manifest into shards that each fit one workflow run, and drive the review as a campaign of sequential workflow launches with every stage result persisted to disk: a signals run (cross-file consistency + adoption), the review shards (consensus), a deterministic merge, and a gated adversarial run (red team + defense + arbitration). All stages launch the same committed workflow script, switched by a manifest mode. The review is strictly read-only — it never mutates the tests under review.

digraph team_review {
  "Team review requested" [shape=doublecircle];
  "Confirm scope + cost" [shape=diamond];
  "Offer single-reviewer, stop" [shape=octagon, style=filled, fillcolor=red];
  "Resolve input + classify each path (test_type)" [shape=box];
  "File list empty?" [shape=diamond];
  "Abort: no valid test files" [shape=octagon, style=filled, fillcolor=red];
  "Fan out per-file extraction (parallel haiku subagents)" [shape=box];
  "Resolve ambiguous entries (AskUserQuestion)" [shape=box];
  "Project agent cost (dry-run workflow) + select preset/models" [shape=box];
  "Build shard plan (per-file weights, S_max)" [shape=box];
  "Assemble campaign dir (campaign.json, args per shard + signals)" [shape=box];
  "Launch signals run (mode=signals, background)" [shape=box];
  "Launch next review shard (mode=review); persist result" [shape=box];
  "Shard partial or failed?" [shape=diamond];
  "Stop campaign: report completed shards + resume policy" [shape=octagon, style=filled, fillcolor=red];
  "More shards?" [shape=diamond];
  "Merge: verdicts + coverage map + placement flags" [shape=box];
  "Adversarial gate: run red team?" [shape=diamond];
  "Launch adversarial run (mode=adversarial); persist result" [shape=box];
  "Render combined report" [shape=doublecircle];

  "Team review requested" -> "Confirm scope + cost";
  "Confirm scope + cost" -> "Offer single-reviewer, stop" [label="declined"];
  "Confirm scope + cost" -> "Resolve input + classify each path (test_type)" [label="proceed"];
  "Resolve input + classify each path (test_type)" -> "File list empty?";
  "File list empty?" -> "Abort: no valid test files" [label="yes"];
  "File list empty?" -> "Fan out per-file extraction (parallel haiku subagents)" [label="no"];
  "Fan out per-file extraction (parallel haiku subagents)" -> "Resolve ambiguous entries (AskUserQuestion)";
  "Resolve ambiguous entries (AskUserQuestion)" -> "Project agent cost (dry-run workflow) + select preset/models";
  "Project agent cost (dry-run workflow) + select preset/models" -> "Build shard plan (per-file weights, S_max)";
  "Build shard plan (per-file weights, S_max)" -> "Assemble campaign dir (campaign.json, args per shard + signals)";
  "Assemble campaign dir (campaign.json, args per shard + signals)" -> "Launch signals run (mode=signals, background)";
  "Launch signals run (mode=signals, background)" -> "Launch next review shard (mode=review); persist result";
  "Launch next review shard (mode=review); persist result" -> "Shard partial or failed?";
  "Shard partial or failed?" -> "Stop campaign: report completed shards + resume policy" [label="yes"];
  "Shard partial or failed?" -> "More shards?" [label="no"];
  "More shards?" -> "Launch next review shard (mode=review); persist result" [label="yes"];
  "More shards?" -> "Merge: verdicts + coverage map + placement flags" [label="no"];
  "Merge: verdicts + coverage map + placement flags" -> "Adversarial gate: run red team?";
  "Adversarial gate: run red team?" -> "Launch adversarial run (mode=adversarial); persist result" [label="run"];
  "Adversarial gate: run red team?" -> "Render combined report" [label="skip"];
  "Launch adversarial run (mode=adversarial); persist result" -> "Render combined report";
}

Phase 0: Confirm Scope & Cost

This review spawns many parallel agents and consumes substantially more tokens than a single-reviewer pass. Ask via AskUserQuestion whether to proceed with the team review or run a single-reviewer pass with the matching per-type reviewing skill (phpunit-unit-test-reviewing for unit, phpunit-integration-test-reviewing for integration, phpunit-migration-test-reviewing for migration) instead. Proceed only on confirmation. The preset and model combo are chosen later (Phase 2), informed by the projected agent count.

Phase 1: Resolve Input to a Manifest

Read references/input-resolution.md. Resolve the input to a file list and classify each path by its root — tests/unit/ → test_type=unit, tests/integration/ → integration, tests/migration/ → migration. Resolve interactive ambiguity that blocks resolution — base branch for a branch diff, unclear scope — with AskUserQuestion; the review cannot ask once it is running. If the file list is empty, abort per references/error-handling.md.

Then build each file's entry in parallel: spawn one general-purpose subagent per file, pinned to model: haiku, each running references/input-resolution.md §Per-File Extraction. Inline that contract verbatim into every spawn — a spawned agent never reads the reference. Each subagent measures its file with wc/grep (never estimates), enumerates every test method, resolves the #[CoversClass] source, computes the cross-file fingerprint and (when the file's combined lines exceed the digest threshold) the body-free digest — or, when the source cannot be resolved to a src/ file, returns ambiguous: true with a reason instead of guessing.

Aggregate the returned entries. For every entry flagged ambiguous, resolve it with AskUserQuestion and refill its fields from the answer — a guessed source size silently flips the track decision, so nothing ambiguous may reach the run. Let N = number of files.

Output: a manifest of validated entries, each with test_type, method scope (methods, plus the diff-touched changed_methods on diff runs), the full test_methods list, resolved source_path/source_paths, decomposition measurements (test_lines, source_lines, method_count), fingerprint, and a digest when combined lines exceed the threshold (references/input-resolution.md).

Phase 2: Project the Cost, Select the Preset, Build the Shard Plan

Run the workflow in projection-only mode, let the user choose preset and model combo, then partition the manifest into shards that each fit one workflow run. This step spawns no review agents.

1. Write a dry-run input — { "files": [<the Phase-1 entries>], "dry_run": true } — to dry-args.json. A dry run needs no rule catalogs.
2. Build and launch it the same way Phase 4 launches a real run, but against dry-args.json: run ${CLAUDE_SKILL_DIR}/workflow/build-run-script.sh dry-args.json "$DRY_OUT" (with DRY_OUT a mktemp path outside the repo), then the Workflow tool with scriptPath: "$DRY_OUT" and no args. It returns immediately with the projection result ({ dry_run: true, files, slots, model_combos, projections } — references/report-format.md §Dry-Run Projection). Each preset's projection carries review_agents_bound, adversarial_agents_bound, and per_file shard weights.
3. Render projections as a compact table (per preset: units, wave0_agents, review_agents_bound, adversarial_agents_bound), noting the bounds are upper bounds — conditional waves and capped arbitration run fewer.
4. Present preset + model combo as an AskUserQuestion, defaulting to standard / sonnet-opus.
   • preset — deep / standard / lean: cost/quality operating point (whole-class coverage threshold, shard granularity, adversary lens count, arbitration caps). Per-preset values: references/reviewer-allocation.md.
   • models — sonnet-opus / haiku-opus / haiku-sonnet: body and adversary model tiers. Lower body tiers cut cost but reduce rule-application precision; keep the adversary tier no lower than sonnet.
5. Build the shard plan from the chosen preset's per_file weights (references/reviewer-allocation.md §Shard Budget): if the preset's review_agents_bound ≤ S_max (250), the plan is one shard; otherwise partition round-robin by descending weight into the fewest shards whose per-shard weight sums stay ≤ S_max. A file never straddles shards. Render the plan (shard count, files and projected agents per shard) with the projection table.

Phase 3: Assemble the Campaign on Disk

Each workflow run is sandboxed — no filesystem, no MCP — and reads its manifest from one inlined value. Assemble every stage's manifest as a JSON file inside one campaign directory; the catalogs are large (tens of KB each) by design, so splice them in by path and never load them into context.

1. Campaign directory. Create it outside the repository — CAMPAIGN="$(mktemp -d)". Every args file, run-script, and stage result lives here; a later session resumes the campaign from this directory alone.

2. Per-type rule catalogs. For each test type present in the manifest, call build_rule_package and keep the returned path (do not Read it):

   • unit → build_rule_package() (no arguments)
   • integration → build_rule_package(group=integration, test_type=integration)
   • migration → build_rule_package(group=migration, test_type=migration)

   If a needed build fails or reports zero rules, abort (references/error-handling.md).

3. Per-shard args. For each shard k, Write its Phase-1 entries (plus any base ref and the Phase-2 preset / models names) to $CAMPAIGN/manifest-core-k.json, then merge the catalogs in by path with jq --rawfile — include only the rule_packages keys for types present in that shard:

   jq -n \
     --slurpfile core "$CAMPAIGN/manifest-core-k.json" \
     --rawfile unit  <unit catalog path> \
     --rawfile integ <integration catalog path> \
     '{ mode: "review", files: $core[0].files, base: $core[0].base,
        preset: $core[0].preset, models: $core[0].models,
        rule_packages: { unit: $unit, integration: $integ } }' \
     > "$CAMPAIGN/args-shard-k.json"
   

4. Signals args. Write { "mode": "signals", "files": [<ALL Phase-1 entries>], "base": <base> } to $CAMPAIGN/args-signals.json — the signals run needs no rule catalogs.

5. Campaign state. Write $CAMPAIGN/campaign.json: the base ref, preset/models, and one entry per stage (signals, shard-1..N, adversarial) with its args path and status: "pending".

The manifests are fixed here, before any launch — nothing ambiguous may reach a run.

Phase 4: Execute the Campaign

Build each stage's run-script with ${CLAUDE_SKILL_DIR}/workflow/build-run-script.sh <args file> <out.mjs in $CAMPAIGN> and launch it with the Workflow tool (scriptPath, no args). The committed workflow/team-review.workflow.mjs is the sole orchestration for every stage — do not compose, write, or search for an alternative, and treat any leftover run-script from a prior run as stale. Launch directly: do not consult the advisor before launching.

1. Launch the signals run first (it depends on nothing and runs concurrently with the first shard).
2. Launch review shards strictly one at a time. Launch shard k; when its completion notification arrives, Write the raw result to $CAMPAIGN/shard-k.result.json and set its campaign.json status before launching shard k+1.
3. Check every stage result before continuing. A result with partial: true, a failed run, or a launch error stops the campaign: update campaign.json, report which stages completed and which did not, and apply the resume policy in references/error-handling.md — a storm-failed stage is relaunched clean; a circuit-breaker-halted stage may resume via resumeFromRunId. Do not launch the next stage into a known-dead quota window.
4. When the signals result arrives, Write it to $CAMPAIGN/signals.result.json.

Phase 5: Merge the Consensus & Compute the Deterministic Signals

When all shards completed, merge on disk — no agents:

1. Combined verdicts. Concatenate the shard results' files arrays; aggregate kept_findings, contested_findings, and concession_rate (weighted by each shard's wave0 finding keys) from the shard summaries.
2. SUT-coverage map. Join every manifest entry's source_paths to its test path; report each SUT covered by ≥ 2 test files as { sut, covered_by: [{path, test_type}], note }, noting integration test redundant with existing unit coverage of this SUT when the covering set mixes unit and integration (references/report-format.md §Coverage Map).
3. Placement flags. Flag an integration file when (a) its merged result carries an INTEGRATION-008 informational finding, and/or (b) the coverage map shows it redundant with unit coverage. Each flag points at phpunit-integration-to-unit-migrating and never raises status.

Render the consensus-stage report section now (report-format.md) — it survives even if the adversarial stage never runs.

Phase 6: Adversarial Gate & Run

The adversarial stage (red team + defense + arbitration) is the opus-priced part and the only stage that consumes consensus. Gate it explicitly:

1. Aggregate the shard summaries' adversarial_gate signals. If every shard recommends skip (zero kept findings, or concession ≥ 50%), recommend skipping.
2. Present the gate as an AskUserQuestion: kept/contested totals, the skip signals, and the chosen preset's adversarial_agents_bound from the Phase-2 projection. Default to run when findings exist and no skip signal fired.
3. On skip: proceed to Phase 7 with the consensus-stage results as final.
4. On run: assemble $CAMPAIGN/args-adversarial.json — mode: "adversarial", ALL files, the same rule_packages / preset / models / base, plus consensus: the array of every file's adversarial_input object extracted from the shard results (jq, by path). Build, launch, persist to $CAMPAIGN/adversarial.result.json with the same stop-on-partial policy as Phase 4.

Phase 7: Render the Report

Read references/report-format.md and render the combined report: per-file verdicts (the adversarial result's files supersede the consensus-stage entries for files it processed), the signals result's consistency and adoption_opportunities, the Phase-5 coverage map and placement flags, and the per-stage cost lines (each stage result's agents_spawned and output_tokens).

Error Handling

For input-resolution failures, stage start-up or run failures, partial results (partial: true + halted_at), the campaign stop/resume policy, and consensus edge cases, Read references/error-handling.md.