cbbkrd-tech/ln-650-persistence-performance-auditor

Paths: File paths (shared/, references/, ../ln-*) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root.

Persistence & Performance Auditor (L2 Coordinator)

Coordinates 3 specialized audit workers to perform database efficiency, transaction correctness, and runtime performance analysis.

Purpose & Scope

• Coordinates 3 audit workers (ln-651, ln-652, ln-653) running in parallel
• Research current best practices for detected DB, ORM, async framework via MCP tools ONCE
• Pass shared context to all workers (token-efficient)
• Aggregate worker results into single consolidated report
• Create single task in Linear under Epic 0 with all findings
• Manual invocation by user; not part of Story pipeline
• Independent from ln-620 (can be run separately or after ln-620)

Workflow

1. Discovery: Load tech_stack.md, package manifests, detect DB/ORM/async framework, auto-discover Team ID
2. Research: Query MCP tools for DB/ORM/async best practices ONCE
3. Build Context: Create contextStore with best practices + DB-specific metadata
4. Delegate: 3 workers in PARALLEL
5. Aggregate: Collect worker results, calculate scores
6. Generate Report: Build consolidated report
7. Create Task: Create Linear task in Epic 0 titled "Persistence & Performance Audit: [YYYY-MM-DD]"

Phase 1: Discovery

Load project metadata:

• docs/project/tech_stack.md - detect DB, ORM, async framework
• Package manifests: requirements.txt, pyproject.toml, package.json, go.mod
• Auto-discover Team ID from docs/tasks/kanban_board.md

Extract DB-specific metadata:

| Metadata | Source | Example | |----------|--------|---------| | Database type | tech_stack.md, docker-compose.yml | PostgreSQL 16 | | ORM | imports, requirements.txt | SQLAlchemy 2.0 | | Async framework | imports, requirements.txt | asyncio, FastAPI | | Session config | grep create_async_engine, sessionmaker | expire_on_commit=False | | Triggers/NOTIFY | migration files | pg_notify('job_events', ...) | | Connection pooling | engine config | pool_size=10, max_overflow=20 |

Scan for triggers:

Grep("pg_notify|NOTIFY|CREATE TRIGGER", path="alembic/versions/")
  OR path="migrations/"
→ Store: db_config.triggers = [{table, event, function}]

Phase 2: Research Best Practices (ONCE)

For each detected technology:

| Technology | Research Focus | |------------|---------------| | SQLAlchemy | Session lifecycle, expire_on_commit, bulk operations, eager/lazy loading | | PostgreSQL | NOTIFY/LISTEN semantics, transaction isolation, batch operations | | asyncio | to_thread, blocking detection, event loop best practices | | FastAPI | Dependency injection scopes, background tasks, async endpoints |

Build contextStore:

{
  "tech_stack": {"db": "postgresql", "orm": "sqlalchemy", "async": "asyncio"},
  "best_practices": {"sqlalchemy": {...}, "postgresql": {...}, "asyncio": {...}},
  "db_config": {
    "expire_on_commit": false,
    "triggers": [{"table": "jobs", "event": "UPDATE", "function": "notify_job_events"}],
    "pool_size": 10
  },
  "codebase_root": "/project"
}

Phase 3: Delegate to Workers

CRITICAL: All delegations use Task tool with subagent_type: "general-purpose" for context isolation.

Prompt template:

Task(description: "Audit via ln-65X",
     prompt: "Execute ln-65X-{worker}-auditor. Read skill from ln-65X-{worker}-auditor/SKILL.md. Context: {contextStore}",
     subagent_type: "general-purpose")

Anti-Patterns:

• ❌ Direct Skill tool invocation without Task wrapper
• ❌ Any execution bypassing subagent context isolation

Workers (ALL 3 in PARALLEL):

| # | Worker | Priority | What It Audits | |---|--------|----------|----------------| | 1 | ln-651-query-efficiency-auditor | HIGH | Redundant queries, N-UPDATE loops, over-fetching, caching scope | | 2 | ln-652-transaction-correctness-auditor | HIGH | Commit patterns, trigger interaction, transaction scope, rollback | | 3 | ln-653-runtime-performance-auditor | MEDIUM | Blocking IO in async, allocations, sync sleep, string concat |

Invocation (3 workers in PARALLEL):

FOR EACH worker IN [ln-651, ln-652, ln-653]:
  Task(description: "Audit via " + worker,
       prompt: "Execute " + worker + ". Read skill. Context: " + JSON.stringify(contextStore),
       subagent_type: "general-purpose")

Phase 4: Aggregate Results

Collect results from workers:

{
  "category": "Query Efficiency",
  "score": 6,
  "total_issues": 8,
  "findings": [...]
}

Aggregation steps:

1. Merge findings from all 3 workers
2. Calculate overall score: average of 3 category scores
3. Sum severity counts across all workers
4. Sort findings by severity (CRITICAL → HIGH → MEDIUM → LOW)
5. Context Validation (Post-Filter)

Context Validation:

MANDATORY READ: Load shared/references/context_validation.md

Apply Rules 1, 6 to merged findings:

FOR EACH finding WHERE severity IN (HIGH, MEDIUM):
  # Rule 1: ADR/Planned Override
  IF finding matches ADR → advisory "[Planned: ADR-XXX]"

  # Rule 6: Execution Context
  IF finding.check IN (blocking_io, redundant_fetch, transaction_wide, cpu_bound):
    context = 0
    - Function in __init__/setup/bootstrap/migrate → context += 1
    - File in tasks/jobs/cron/                      → context += 1
    - Has timeout/safeguard nearby                  → context += 1
    - Small data (<100KB file, <100 items dataset)  → context += 1
    IF context >= 3 → advisory
    IF context >= 1 → severity -= 1

Downgraded findings → "Advisory Findings" section in report.
Recalculate overall score excluding advisory findings from penalty.

Exempt: Missing rollback CRITICAL, N-UPDATE loops in hot paths.

Output Format

## Persistence & Performance Audit Report - [DATE]

### Executive Summary
[2-3 sentences on overall persistence/performance health]

### Compliance Score

| Category | Score | Notes |
|----------|-------|-------|
| Query Efficiency | X/10 | ... |
| Transaction Correctness | X/10 | ... |
| Runtime Performance | X/10 | ... |
| **Overall** | **X/10** | |

### Severity Summary

| Severity | Count |
|----------|-------|
| Critical | X |
| High | X |
| Medium | X |
| Low | X |

### Findings by Category

#### 1. Query Efficiency

| Severity | Location | Issue | Recommendation | Effort |
|----------|----------|-------|----------------|--------|
| HIGH | job_processor.py:434 | Redundant entity fetch | Pass object not ID | S |

#### 2. Transaction Correctness

| Severity | Location | Issue | Recommendation | Effort |
|----------|----------|-------|----------------|--------|
| CRITICAL | job_processor.py:412 | Missing intermediate commits | Add commit at milestones | S |

#### 3. Runtime Performance

| Severity | Location | Issue | Recommendation | Effort |
|----------|----------|-------|----------------|--------|
| HIGH | job_processor.py:444 | Blocking read_bytes() in async | Use aiofiles/to_thread | S |

### Recommended Actions (Priority-Sorted)

| Priority | Category | Location | Issue | Recommendation | Effort |
|----------|----------|----------|-------|----------------|--------|
| CRITICAL | Transaction | ... | Missing commits | Add strategic commits | S |
| HIGH | Query | ... | Redundant fetch | Pass object not ID | S |

### Sources Consulted
- SQLAlchemy best practices: [URL]
- PostgreSQL NOTIFY docs: [URL]
- Python asyncio-dev: [URL]

Phase 5: Create Linear Task

Create task in Epic 0:

• Title: Persistence & Performance Audit: [YYYY-MM-DD]
• Description: Full report from Phase 4 (markdown format)
• Team: Auto-discovered from kanban_board.md
• Epic: 0 (technical debt / refactoring epic)
• Labels: refactoring, performance, audit
• Priority: Based on highest severity findings

Critical Rules

• Single context gathering: Research best practices ONCE, pass contextStore to all workers
• Parallel execution: All 3 workers run in PARALLEL
• Trigger discovery: Scan migrations for triggers/NOTIFY before delegating (pass to ln-652)
• Metadata-only loading: Coordinator loads metadata; workers load full file contents
• Single task: Create ONE task with all findings; do not create multiple tasks
• Do not audit: Coordinator orchestrates only; audit logic lives in workers

Definition of Done

• Tech stack discovered (DB type, ORM, async framework)
• DB-specific metadata extracted (triggers, session config, pool settings)
• Best practices researched via MCP tools
• contextStore built and passed to workers
• All 3 workers invoked in PARALLEL and completed
• Results aggregated with severity-sorted findings
• Compliance score calculated per category + overall
• Executive Summary included
• Linear task created in Epic 0 with full report
• Sources consulted listed with URLs

Workers

• ln-651-query-efficiency-auditor
• ln-652-transaction-correctness-auditor
• ln-653-runtime-performance-auditor

Reference Files

• Tech stack: docs/project/tech_stack.md
• Kanban board: docs/tasks/kanban_board.md

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Version: 1.0.0 Last Updated: 2026-02-04