gstack/review/sections/review-army.md

Step 4.5: Review Army — Specialist Dispatch

Detect stack and scope

source <(~/.claude/skills/gstack/bin/gstack-diff-scope <base> 2>/dev/null) || true
# Detect stack for specialist context
STACK=""
[ -f Gemfile ] && STACK="${STACK}ruby "
[ -f package.json ] && STACK="${STACK}node "
[ -f requirements.txt ] || [ -f pyproject.toml ] && STACK="${STACK}python "
[ -f go.mod ] && STACK="${STACK}go "
[ -f Cargo.toml ] && STACK="${STACK}rust "
echo "STACK: ${STACK:-unknown}"
DIFF_BASE=$(git merge-base origin/<base> HEAD)
DIFF_INS=$(git diff "$DIFF_BASE" --stat | tail -1 | grep -oE '[0-9]+ insertion' | grep -oE '[0-9]+' || echo "0")
DIFF_DEL=$(git diff "$DIFF_BASE" --stat | tail -1 | grep -oE '[0-9]+ deletion' | grep -oE '[0-9]+' || echo "0")
DIFF_LINES=$((DIFF_INS + DIFF_DEL))
echo "DIFF_LINES: $DIFF_LINES"
# Detect test framework for specialist test stub generation
TEST_FW=""
{ [ -f jest.config.ts ] || [ -f jest.config.js ]; } && TEST_FW="jest"
[ -f vitest.config.ts ] && TEST_FW="vitest"
{ [ -f spec/spec_helper.rb ] || [ -f .rspec ]; } && TEST_FW="rspec"
{ [ -f pytest.ini ] || [ -f conftest.py ]; } && TEST_FW="pytest"
[ -f go.mod ] && TEST_FW="go-test"
echo "TEST_FW: ${TEST_FW:-unknown}"

Read specialist hit rates (adaptive gating)

~/.claude/skills/gstack/bin/gstack-specialist-stats 2>/dev/null || true

Select specialists

Based on the scope signals above, select which specialists to dispatch.

Always-on (dispatch on every review with 50+ changed lines):

1. Testing — read ~/.claude/skills/gstack/review/specialists/testing.md
2. Maintainability — read ~/.claude/skills/gstack/review/specialists/maintainability.md

If DIFF_LINES < 50: Skip all specialists. Print: "Small diff ($DIFF_LINES lines) — specialists skipped." Continue to Step 4.6 with the core findings and an empty specialist list, then the parent's Exploratory QA step and Step 4.8 (adversarial review), then Step 5. Small diffs skip fan-out, never the parent-owned smoke probes. Core shared-code checks also remain required.

Conditional (dispatch if the matching scope signal is true): 3. Security — if SCOPE_AUTH=true, OR if SCOPE_BACKEND=true AND DIFF_LINES > 100. Read ~/.claude/skills/gstack/review/specialists/security.md 4. Performance — if SCOPE_BACKEND=true OR SCOPE_FRONTEND=true. Read ~/.claude/skills/gstack/review/specialists/performance.md 5. Data Migration — if SCOPE_MIGRATIONS=true. Read ~/.claude/skills/gstack/review/specialists/data-migration.md 6. API Contract — if SCOPE_API=true. Read ~/.claude/skills/gstack/review/specialists/api-contract.md 7. Design — if SCOPE_FRONTEND=true. Use the existing design review checklist at ~/.claude/skills/gstack/review/design-checklist.md and run the mechanical pass at the top of that checklist (the user-installed design detector, when present) before the LLM items 8. Simplification — if DIFF_LINES > 100. Read ~/.claude/skills/gstack/review/specialists/simplification.md. Advisory-only lens: hunts unrequested structure (hand-rolled stdlib, one-implementation abstractions, dependencies duplicating platform features), never coverage.

Adaptive gating

After scope-based selection, apply adaptive gating based on specialist hit rates:

For each conditional specialist that passed scope gating, check the gstack-specialist-stats output above:

• If tagged [GATE_CANDIDATE] (0 findings in 10+ dispatches): skip it. Print: "[specialist] auto-gated (0 findings in N reviews)."
• If tagged [NEVER_GATE]: always dispatch regardless of hit rate. Security and data-migration are insurance policy specialists — they should run even when silent.

Force flags: If the user's prompt includes --security, --performance, --testing, --maintainability, --data-migration, --api-contract, --design, --simplification, or --all-specialists, force-include that specialist regardless of gating.

Note which specialists were selected, gated, and skipped. Print the selection: "Dispatching N specialists: [names]. Skipped: [names] (scope not detected). Gated: [names] (0 findings in N+ reviews)."

---

Dispatch specialists in parallel

For each selected specialist, launch an independent subagent via the Agent tool. Launch ALL selected specialists in a single message (multiple Agent tool calls) so they run in parallel. Each subagent has fresh context — no prior review bias.

Each specialist subagent prompt:

Construct the prompt for each specialist. The prompt includes:

1. The specialist's checklist content (you already read the file above)
2. Stack context: "This is a {STACK} project."
3. Past learnings for this domain (if any exist):

~/.claude/skills/gstack/bin/gstack-learnings-search --type pitfall --query "{specialist domain}" --limit 5 2>/dev/null || true

If learnings are found, include them: "Past learnings for this domain: {learnings}"

4. Instructions:

"You are a specialist code reviewer. Read the checklist below, then run DIFF_BASE=$(git merge-base origin/<base> HEAD) && git diff "$DIFF_BASE" to get the full diff. Apply the checklist against the diff.

For each finding, output a JSON object on its own line: {"severity":"CRITICAL|INFORMATIONAL","confidence":N,"path":"file","line":N,"category":"category","summary":"description","fix":"recommended fix","fingerprint":"path:line:category","specialist":"name"}

Required fields: severity, confidence, path, category, summary, specialist. Optional: line, fix, fingerprint, evidence, test_stub, advisory, evidence_paths, helper_target.

Optional extraction advice belongs to the core shared-code check; do not duplicate its proposals. Report real defects in duplicated code independently. Preserve advisory metadata when returning structural advice, and never label a demonstrated defect advisory merely because sharing a helper could fix it.

If you can write a test that would catch this issue, include it in the test_stub field. Use the detected test framework ({TEST_FW}). Write a minimal skeleton — describe/it/test blocks with clear intent. Skip test_stub for architectural or design-only findings.

If no findings: output NO FINDINGS and nothing else. Do not output anything else — no preamble, no summary, no commentary.

Stack context: {STACK} Past learnings: {learnings or 'none'}

CHECKLIST: {checklist content}"

Subagent configuration:

• Use subagent_type: "general-purpose"
• Pass run_in_background: false on every specialist Agent call — background is the default since Claude Code v2.1.198; omitting the flag is not foreground.

Wait for readers before editing:

• Confirm that each task has finished or is stopped. A timeout alone does not prove termination. If a reader or writer is still active, wait; if its state is unknown, inspect its task/process status. If you cannot confirm it stopped, use the parent's Fix-First stop path without edits.
• A failed task may be stopped without having completed its review. Record the failure and retain usable partial findings.
• Continue independent evidence collection after a terminal failure. Missing dispatched coverage remains incomplete, never completed or clean; successful peers cannot replace it.

---

Step 4.6: Collect and merge findings

Follow these stages in order. Validate core and specialist findings alike, but keep their source labels: specialist scoring is not the final review's defect count.

1. Parse outputs

After specialist attempts settle, collect their outputs, tagged by actual source. Successful NO FINDINGS is a completed empty result. Otherwise parse each JSON line and skip invalid lines. Missing or unusable output is incomplete coverage, not an empty success. Retain each specialist's returned findings for activity stats.

2. Validate severity

For core and specialist findings with "severity":"CRITICAL" and "advisory":true, remove advisory and retain its CRITICAL severity. Treat these as defects before identity, merging, counting, scoring or Fix-First. Never downgrade severity to make advisory metadata consistent. Valid INFORMATIONAL advisories remain advisory in every category, including simplification.

3. Identify and merge

Partition defects and advisories BEFORE grouping by fingerprint. Never merge a defect with advice, even on a supplied-hash collision. Neither higher-confidence advice nor a prior skipped extraction may replace, downgrade or suppress a defect.

Compute identities for both core and specialist findings:

• Shared-code advice (category shared-libs or fingerprint prefix shared-libs:): call installed sharedLibsFingerprint from ~/.claude/skills/gstack/lib/review-evidence.ts with evidence_paths and helper_target as literal JSON on stdin, as in the core pass; never trust a supplied hash or generate one yourself. Missing/malformed metadata cannot deduplicate or reuse a saved decision.
• Other findings: use supplied fingerprint, else {path}:{line}:{category} or {path}:{category} when no line exists.

Within the specialist list, merge matching identities in the same partition: keep the highest confidence and all source names. Confirmation by distinct specialists adds +1 (cap at 10) and MULTI-SPECIALIST CONFIRMED ({specialist1} + {specialist2}). Core findings never earn a specialist confidence boost. Preserve advisory, evidence_paths and helper_target through every merge.

4. Apply specialist confidence gates

• Confidence 7+: show normally in the findings output
• Confidence 5-6: show with caveat "Medium confidence — verify this is actually an issue"
• Confidence 3-4: move to appendix (suppress from main findings)
• Confidence 1-2: suppress entirely

Core findings keep the core Confidence Calibration gates.

5. Score and present specialists

Only specialist findings enter this header and quality_score; core findings do not. Use the merged NON-advisory specialist findings for both counts and score: quality_score = max(0, 10 - (critical_count * 2 + informational_count * 0.5)) Cap at 10 and retain for the review-log entry in Step 5.8. These are not final unresolved-defect totals. Validated "advisory": true findings from any source are excluded from score, header, unresolved-defect totals and clean-status blockers. Show them separately; they remain ASK-only, never auto-applied. Real defects follow normal Fix-First.

SPECIALIST REVIEW: N findings (X critical, Y informational) from Z specialists

[For each finding, in order: CRITICAL first, then INFORMATIONAL, sorted by confidence descending;
 advisory findings last, each rendered with an [ADVISORY] label in place of the severity]
[SEVERITY] (confidence: N/10, specialist: name) path:line — summary
  Fix: recommended fix
  [If MULTI-SPECIALIST CONFIRMED: show confirmation note]

PR Quality Score: X/10

Simplification footer (after the score line):

• If the simplification specialist was dispatched and returned findings, sum their lines_removable values and print: net: -N lines possible (omit findings without the field from the sum).
• If it was dispatched and returned NO FINDINGS, print: Simplification: lean already — nothing to cut.
• If it was not dispatched, print neither line.

Do not add core shared-code savings to this specialist footer. Explain any overlap once in the core proposal instead of presenting duplicate savings.

6. Save specialist activity

Compile a specialists object for the review-log entry in Step 5.8. For DIFF_LINES < 50, keep specialists: {}; do not manufacture per-specialist scope records. Otherwise record each considered specialist (testing, maintainability, security, performance, data-migration, api-contract, design, simplification, red-team):

• If dispatched: {"dispatched": true, "findings": N, "critical": N, "informational": N}
• If skipped by scope: {"dispatched": false, "reason": "scope"}
• If skipped by gating: {"dispatched": false, "reason": "gated"}
• If not applicable (e.g., red-team not activated): omit from the object

Count only findings that specialist actually returned, before deduplication. Advisory findings COUNT in the stats findings field, not its defect counts. Include Design despite its different checklist. Preserve dispatch/failure status: zero returned findings from a failed attempt is not a clean review.

7. Hand off to Fix-First

Send these findings to Step 5 Fix-First alongside the CRITICAL pass findings from Step 4. Consolidate equivalent shared-code advice under the core proposal, retaining all sources and counting overlapping savings once. Keep actual specialist stats; core-only advice must not create a specialist dispatch or finding. Normal AUTO-FIX/ASK rules apply, with advice ASK-only. Missing coverage still blocks completion. Advice never permits edits while readers are active or replaces a required review.

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Red Team dispatch (conditional)

Activation: Only if DIFF_LINES > 200 OR any specialist produced a CRITICAL finding.

If activated, dispatch one more subagent via the Agent tool (pass run_in_background: false — foreground; subagents default to background since Claude Code v2.1.198).

The Red Team subagent receives:

1. The red-team checklist from ~/.claude/skills/gstack/review/specialists/red-team.md
2. The merged specialist findings from Step 4.6 (so it knows what was already caught)
3. The git diff command

Prompt: "You are a red team reviewer. The code has already been reviewed by N specialists who found the following issues: {merged findings summary}. Your job is to find what they MISSED. Read the checklist, run DIFF_BASE=$(git merge-base origin/<base> HEAD) && git diff "$DIFF_BASE", and look for gaps. Output findings as JSON objects (same schema as the specialists). Focus on cross-cutting concerns, integration boundary issues, and failure modes that specialist checklists don't cover."

If the Red Team finds additional issues, tag them "specialist":"red-team". Add them to the original specialist outputs and rerun stages 1–7 of Step 4.6 before Step 5 Fix-First; do not boost or count the earlier findings twice.

If the Red Team returns NO FINDINGS, note: "Red Team review: no additional issues found." If the Red Team fails or times out, confirm it stopped and record its review as incomplete, just as for other specialists. Continue independent Step 4.7 QA and Step 4.8 adversarial review; Step 5.8 cannot certify missing dispatched coverage as completed or clean.