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 5. This threshold only gates specialist dispatch; any core shared-code check still runs.

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 — subagents run in the BACKGROUND by default since Claude Code v2.1.198, and all specialists must complete before merge. (Merely omitting the flag no longer produces a foreground run; it must be explicitly false.)
• If any specialist subagent fails or times out, log the failure and continue with results from successful specialists. Specialists are additive — partial results are better than no results.

---

Step 4.6: Collect and merge findings

After all specialist subagents complete, collect their outputs.

Parse findings: For each specialist's output:

1. If output is "NO FINDINGS" — skip, this specialist found nothing
2. Otherwise, parse each line as a JSON object. Skip lines that are not valid JSON.
3. Collect all parsed findings into a single list, tagged with their specialist name.

Validate advisory severity first. If a current finding has "severity":"CRITICAL" and "advisory":true, remove advisory and retain its CRITICAL severity. Handle it as a normal defect before fingerprinting, partitioning, deduplication, counting, scoring, and Fix-First. Never downgrade severity to make advisory metadata consistent. Valid INFORMATIONAL advisories remain advisory in every category, including simplification. Apply this validation to core and specialist findings alike before combining them.

Fingerprint and deduplicate: For each finding, compute its fingerprint:

• For a shared-code advisory (category shared-libs or a shared-libs: fingerprint), call the installed sharedLibsFingerprint helper from ~/.claude/skills/gstack/lib/review-evidence.ts with literal JSON on stdin, as in the core pass. Recompute from evidence_paths and helper_target; never trust a supplied hash or generate hash text yourself. Missing/malformed metadata cannot deduplicate or reuse a saved decision.
• If fingerprint field is present, use it
• Otherwise: {path}:{line}:{category} (if line is present) or {path}:{category}

The last two rules apply only to other findings. Preserve advisory, evidence_paths, and helper_target through merging. Core review owns shared-code proposals: consolidate equivalent specialist advice with the core proposal and count overlapping savings once. Keep the actual specialist activity in its stats; core-only advice must not create a specialist dispatch or finding.

Partition defects and advisories BEFORE grouping by fingerprint. A defect and an advisory must never merge with each other, even if a supplied fingerprint collides. A higher-confidence advisory or prior skipped extraction cannot replace, downgrade, or suppress a demonstrated defect. For findings sharing the same fingerprint within the same partition:

• Keep the finding with the highest confidence score
• Tag it: "MULTI-SPECIALIST CONFIRMED ({specialist1} + {specialist2})"
• Boost confidence by +1 (cap at 10)
• Note the confirming specialists in the output

Apply 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

Advisory carve-out (all sources, including core shared-code and simplification): After severity validation, remaining findings with "advisory": true are excluded from BOTH the quality_score summation and the findings-count header below — they are structure suggestions, not defects, and must not make "5 findings … 10/10" look contradictory. In Fix-First they are ASK-only: NEVER auto-applied, even when mechanical. Also exclude them from unresolved-defect totals and clean-status blockers. Preserve normal Fix-First handling for any real defect affecting the same code.

Compute PR Quality Score: After merging, compute the quality score over NON-advisory findings only: quality_score = max(0, 10 - (critical_count * 2 + informational_count * 0.5)) Cap at 10. Log this in the review result at the end.

Output merged findings: Present the merged findings in the same format as the current review:

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.

These findings flow into Step 5 Fix-First alongside the CRITICAL pass findings from Step 4. The Fix-First heuristic applies identically — specialist findings follow the same AUTO-FIX vs ASK classification (except advisory findings, which are ASK-only per the carve-out above).

Compile per-specialist stats: After merging findings, compile a specialists object for the review-log entry in Step 5.8. For each 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

Advisory findings COUNT in the stats findings field — the advisory carve-out governs defect counts, score penalties, and clean-status blockers, not specialist activity. Count only findings that specialist actually returned. Logging simplification's advisories as findings: 0 would auto-gate the lens into permanent silence after 10 dispatches.

Include the Design specialist even though it uses design-checklist.md instead of the specialist schema files. Remember these stats — you will need them for the review-log entry in Step 5.8.

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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, merge them into the findings list before Step 5 Fix-First. Red Team findings are tagged with "specialist":"red-team".

If the Red Team returns NO FINDINGS, note: "Red Team review: no additional issues found." If the Red Team subagent fails or times out, skip silently and continue.