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feat(copilot): add multi-agent code review skills - #46

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feat(copilot): add multi-agent code review skills#46
anakrish wants to merge 1 commit into
mainfrom
copilot-config

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@anakrish

@anakrish anakrish commented May 4, 2026

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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

Testing methodology

Skills were developed iteratively (v3→v11.4) against a 460-line SARIF module with 25 known issues. Final architecture: 3 parallel discovery agents + risk-triggered micro-passes + adversarial verifier. Combined CR+DR catches 16-17/25 ground truth with zero false positives.

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>
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