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Add Copilot review skills, project instructions, and coding agent setup for automated code review on regorus PRs. Files added: - .github/copilot-instructions.md — project context (no_std, 9 bindings, dual execution paths, deny lints, security-critical evaluation) - .github/skills/code-review/SKILL.md — fast single-agent review (~2 min) - .github/skills/deep-review/SKILL.md — multi-agent deep review (~12 min) - .github/copilot-setup-steps.yml — minimal coding agent environment Development and testing methodology: The skills were developed iteratively (v3 through v11.4) against a 460-line SARIF output module on the feature/sarif-output branch, which served as a controlled test bed with 25 known issues of varying severity (correctness, safety, API design, platform, security, performance). Each version was tested by running the skill via the Copilot CLI, then mapping discovered findings against the ground truth set to measure recall and precision. Key iterations: - v3: baseline single-agent (8/25 recall, 32%) - v7: 3 parallel agents + verification (14/25, 56%) - v10c: model diversity + adversarial pass (10/25, 40%) - v11.3: merged adversarial-verifier architecture (12/25 + 2 novel, 0 noise) - v11.4: domain expertise prompting (12/25 + 2 novel, 0 noise, full report) The final architecture uses 3 parallel discovery agents (with cross-model diversity and context asymmetry), risk-triggered micro-passes, and a single adversarial verifier that both validates candidates via disproval and hunts blind spots. Agents are prompted to reason from policy-author perspective across Rego/OPA, Azure Policy, and RVM workloads. Combined CR+DR catches 16-17/25 ground truth with zero false positives and produces verified findings with confidence levels, test gap analysis, and agent performance metrics. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>