Orchestrate multi-persona code reviews on PRs, branches, or local changes.
npx skills add https://github.com/ponylang/llm-skills --skill pony-code-review
Please help me install this Agent Skill. Skill: pony-code-review Source: https://github.com/ponylang/llm-skills/tree/main/pony-code-review Command: npx skills add https://github.com/ponylang/llm-skills --skill pony-code-review
Ensemble code review that synthesizes findings from specialized reviewer personas. Has two modes: full (8 personas, iterative re-review) and lightweight (3 personas, single pass). Not for one-line config changes or typo fixes — ask permission to skip review for those.
Invoke the skill on a PR to run a full-ensemble review and generate the findings report.
High-intent search queries and answers about installing and using this skill.
Ensemble code review uses multiple specialized AI reviewer personas to analyze a PR, branch, or local changes. It orchestrates analyses for correctness, adversarial issues, and tests, then synthesizes evidence artifacts into a consolidated feedback report.
For complex code changes across subsystems, a full ensemble review applies 8 personas with iterative re-review and escalation loops. This generates structured feedback and evidence synthesis to catch issues standard reviews might miss.
Lightweight mode uses 3 personas in a single pass for faster analysis. Use it for smaller changes, while full mode handles complex subsystems. For one-line config changes or typo fixes, skip review entirely.
AI-assisted ensemble code review requires the /pony-ensemble orchestrator and /pony-synthesize workflow to guide mode selection, manage escalation, and produce the final consolidated findings report.
Reviewer personas generate individual analyses with evidence artifacts. The synthesis workflow then consolidates these findings, escalating issues when necessary, to produce a final structured PR review report.
You can invoke the ensemble code review skill on a PR or branch changes to automatically run multi-persona analysis. It guides mode selection and escalation to produce a consolidated findings report autonomously.
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