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Add memory/ with audit findings and framework knowledge

7 memory files covering:
- audit-bug-patterns: recurring status.py bug patterns
- vram-model-matching: three-tier model prefix matching
- dashboard-security: .state priority, CORS removal, register-guards
- state-machine-workflow: legal transitions and approval gates
- testing-conventions: pytest patterns and helpers
- audit-process: report conventions and batch processing
- framework-architecture: enforcement layers and key files

2026-06-22 10:48:19 -04:00

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Audit process and report conventions

Report location

Root-level dotfiles (.bug_report.md, .adversarial_bug_report.md, .verdict.md) following precedent from previous reviews — NOT task artifacts.

Bug finder workflow

Read all source files in parallel, identify bugs, score by severity (1-10), write .bug_report.md with Bug N, Location, Description, Suggested Fix.

Adversarial bug find

Attack-vector focused — test edge cases, security implications, race conditions, input manipulation. Write .adversarial_bug_report.md.

Batch processing

Group bugs by severity. High-severity (batch 1) first, then Medium/Low (batch 2). Each bug gets its own task with full lifecycle (SPEC → IMPLEMENTATION → CODE_REVIEW → BUG_REPORT → ADVERSARIAL_BUG_REPORT → DOC_REVIEW → VERDICT).

Verdict format

Uses ## Status: PASS or ## Status: FAIL structured line (parsed by _parse_verdict_status_line() in status.py and parse_verdict_status() in dashboard task.py).

Pre-approved workflow

User can pre-approve all tasks upfront, allowing the agent to drive all phases to completion without stopping for approval gates.

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