Lap Tran
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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
20 lines
1.1 KiB
Markdown
20 lines
1.1 KiB
Markdown
# Audit process and report conventions
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## Report location
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Root-level dotfiles (`.bug_report.md`, `.adversarial_bug_report.md`, `.verdict.md`) following precedent from previous reviews — NOT task artifacts.
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## Bug finder workflow
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Read all source files in parallel, identify bugs, score by severity (1-10), write `.bug_report.md` with Bug N, Location, Description, Suggested Fix.
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## Adversarial bug find
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Attack-vector focused — test edge cases, security implications, race conditions, input manipulation. Write `.adversarial_bug_report.md`.
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## Batch processing
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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).
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## Verdict format
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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).
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## Pre-approved workflow
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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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