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Multi-agent Zig best-practices review of large hot subsystems: review, adversarial verify, then synthesize durable lint/agent/doc guidance. Use when the user asks for best-practices review, Zig idioms pass, durable lint guidance, or /zig-best-practices-review.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | zig-best-practices-review |
| description | Multi-agent Zig best-practices review of large hot subsystems: review, adversarial verify, then synthesize durable lint/agent/doc guidance. Use when the user asks for best-practices review, Zig idioms pass, durable lint guidance, or /zig-best-practices-review. |
Weight toward mechanizable / generalizable best-practice issues, not one-off gameplay bugs. Parent orchestrates all spawns.
Agents: zig-review-specialist.
Units: .grok/skills/zig-workflows/references/module-presets.md →
best-practices-review.
One zig-review-specialist per preset unit (background: true,
capability_mode: read-only).
Hunt for:
assumeCapacity without same-change
FailingAllocator proof; hidden per-frame alloc; mid-function allocator grabunreachable / catch unreachable / orelse unreachable / .?items(.field) inside loops vs cached slice(); wrong append patternssrc/core/simd.zig / math.zigerrdefer on partial initEach finding: file, line, severity (high|medium|low), category slug, title,
detail, fix_direction, durable (bool), durable_mechanism
(lint-rule|agent-guidance|doc|none), durable_rationale.
For each finding (batch in parallel groups if many), spawn a
zig-review-specialist that tries to refute it:
// lint:allow / deliberate scalar tail?Verdict: CONFIRMED | PLAUSIBLE | REJECTED, plus is_real,
corrected_severity, durable_confirmed.
Keep only CONFIRMED/PLAUSIBLE with is_real.
One synthesis agent over confirmed findings. Do not duplicate existing
tools/lint_idioms.py rules or guidance already in .grok/agents/zig-*.md
(allocator proofs, ReleaseFast UB, MultiArrayList, fixed budgets, SIMD-through-core,
threaded writes, no test-only production APIs).
Output:
lint_rules — only low-FP mechanical rules with detection heuristic + exemptionsagent_guidance — terse lines targeting zig-specialist / zig-review-specialist / bothdoc_updates — if neededtop_fixes — ranked one-off concrete fixessummaryDeliver a readable Markdown report to the user. Do not apply code changes unless asked.
ZeroLight-Framework (Zig 0.16, SDL3, SDL_GPU) specialist routing: design, implement, debug, PR/diff review, and multi-phase review passes. Use when the user asks for zig-design-specialist, zig-specialist, zig-debug-specialist, or zig-review-specialist; when zig build, zig test, or shader compile fails; when reviewing a PR or branch diff; when planning ECS, DataSystem, SimulationPipeline, pathfinding, collision, AI, or threading changes; or when assessing emergent-gameplay architecture readiness. Slash: /zig-workflows.
Assess ZeroLight-Framework architecture for scalable emergent gameplay simulation. Use when the user asks how ready the engine is for emergent gameplay, architecture assessment, extensibility review, or runs /architecture-assessment.
Multi-agent Zig review of the pathfinder module for coherency, cohesion, and standards adherence. Use when the user asks for pathfinder review, pathfinding module review, /pathfinder-review, or a module-wide pass on src/game/systems/pathfinding/.
Deep behavioral correctness review (pass 3): concurrency, algorithms, SIMD parity, pipeline determinism, resource lifetime, outliers, and test-coverage gaps. Use when the user asks for deep correctness, concurrency review, determinism audit, race hunting, or /zig-deep-correctness-review. Goes beyond idiom/surface reviews.
ZeroLight-Framework (Zig 0.16, SDL3, SDL_GPU) specialist workflows: design plans, implementation, debug/triage, PR/diff review, pathfinder module review, architecture assessment. Use when the user asks for zig-design-specialist, zig-specialist, zig-debug-specialist, or zig-review-specialist; when zig build, zig test, or shader compile fails; when reviewing a PR or branch diff; when planning ECS, DataSystem, SimulationPipeline, pathfinding, collision, AI, or threading changes; or when assessing emergent-gameplay architecture readiness.