▸case-01 We just opened a massive PR affecting our core transaction engine and database models. Could you design an orchestration workflow using specialized AI agents to review this code? Please provide an action plan detailing agent selection, execution sequence, and how conflicting review findings will be merged into a single report. | fail→pass | 23,459 | 36,930 | +57% | 1 | 1 | 0% | 3,783 | 4,331 | +14% | 0 | 0 | — |
▸case-02 I am setting up an automated code inspection system that needs to route pull requests across security, performance, and maintainability reviewers. Please outline a complete orchestration design covering how to select appropriate reviewers, pass context between them efficiently, and aggregate their findings. | fail→fail | 31,624 | 25,298 | -20% | 1 | 1 | 0% | 5,630 | 5,098 | -9% | 0 | 0 | — |
▸case-03 Our automated code analysis setup had a conflict where the security scanner recommended heavy encryption while the performance agent flagged it for latency impact. How should our orchestration system resolve disputes like this and structure the synthesized final output for the developers? | fail→fail | 17,428 | 22,716 | +30% | 1 | 1 | 0% | 2,740 | 4,136 | +51% | 0 | 0 | — |
▸case-04 We need to create a sonarqube-analysis.yml configuration file for our monolithic Java repository to run static analysis on master branch pushes. Can you write the exact YAML configuration file for SonarScanner? | pass→pass | 11,624 | 10,503 | -10% | 1 | 1 | 0% | 2,289 | 2,728 | +19% | 0 | 0 | — |
▸case-05 Please write a JS load testing script for k6 that sends 100 concurrent HTTP GET requests per second to http://api.internal/v1/checkout to baseline response times and latency percentiles. | pass→pass | 11,581 | 9,571 | -17% | 1 | 1 | 0% | 2,304 | 2,683 | +16% | 0 | 0 | — |
▸case-06 Configure a standard GitHub Actions workflow file in .github/workflows/test.yml that checks out Python 3.11 code, installs dependencies with poetry, and executes pytest. | pass→pass | 7,915 | 6,274 | -21% | 1 | 1 | 0% | 1,533 | 1,833 | +20% | 0 | 0 | — |
▸case-07 We want our PR reviewer pipeline to immediately trigger agents without reading the PR diff. We plan to send every full file in the repository to all agents simultaneously to save time on parsing file changes. Outline the multi-agent routing architecture for this PR review. | fail→fail | 18,834 | 20,303 | +8% | 1 | 1 | 0% | 2,998 | 3,888 | +30% | 0 | 0 | — |
▸case-08 When passing state between sequential review agents in an AI review workflow, we want to dump the complete raw stdout of all prior agents into the prompt context for the next agent. Explain how to manage agent context and state passing. | fail→fail | 20,314 | 25,502 | +26% | 1 | 1 | 0% | 3,548 | 4,610 | +30% | 0 | 0 | — |
▸case-09 We have a security review agent that relies on a call-graph analysis agent's AST output, and a style agent that checks linter rules. We want to execute all three agents in strict sequential order to keep pipeline logic simple. How should we structure the execution pipeline? | fail→fail | 14,693 | 13,939 | -5% | 1 | 1 | 0% | 2,709 | 3,302 | +22% | 0 | 0 | — |
▸case-10 Three independent review agents (security, performance, code style) all flagged the exact same line of code for missing input validation. We plan to display all three raw comments as separate review comments on the pull request. How should our result aggregation module synthesize these outputs? | pass→pass | 16,798 | 14,226 | -15% | 1 | 1 | 0% | 2,683 | 3,045 | +13% | 0 | 0 | — |
▸case-11 An AI readability agent flagged a function for splitting into smaller components, but an AI memory agent advised keeping it inline to prevent stack frame overhead in a real-time audio loop. We plan to resolve this by picking the readability suggestion by default since readable code is always best practice. How should the orchestration tool handle this dispute? | fail→fail | 14,618 | 16,844 | +15% | 1 | 1 | 0% | 2,383 | 3,418 | +43% | 0 | 0 | — |
▸case-12 In our multi-agent code review platform, re-running a review on a PR update with one modified line forces every agent to re-analyze all unchanged files from scratch. We want to optimize review execution time. How should we implement performance optimization? | fail→fail | 17,955 | 24,336 | +36% | 1 | 1 | 0% | 3,006 | 4,705 | +57% | 0 | 0 | — |
▸case-13 Our multi-agent code review tool outputs hundreds of low-confidence warnings per PR, annoying developers. We plan to post all agent findings directly to GitHub without filtering, letting developers mark invalid ones manually. How should we build a quality validation framework for the orchestration system? | fail→pass | 22,631 | 22,464 | -1% | 1 | 1 | 0% | 3,528 | 4,080 | +16% | 0 | 0 | — |
▸case-14 We need detailed end-to-end implementation playbook examples for orchestrating a multi-agent review across multi-file refactoring PRs. Which resource should be consulted for comprehensive playbook examples? | fail→fail | 15,808 | 3,255 | -79% | 1 | 1 | 0% | 2,559 | 1,097 | -57% | 0 | 0 | — |
▸case-15 We are building an agent orchestration pipeline for pull requests where security and performance reviews run parallel, followed by a final synthesis agent. However, if the parallel phase detects a critical database schema change, a specialized DB migration agent must be dynamically spawned before synthesis. How should we structure this orchestration? | fail→fail | 18,422 | 21,986 | +19% | 1 | 1 | 0% | 3,332 | 4,529 | +36% | 0 | 0 | — |
▸case-16 Our agent router selects agents by picking 5 random agents from our pool of 20 review agents for every incoming pull request. Design a proper agent selection strategy for PR routing. | fail→pass | 19,337 | 24,053 | +24% | 1 | 1 | 0% | 3,108 | 4,811 | +55% | 0 | 0 | — |
▸case-17 Our orchestration system accepts unvalidated JSON payloads from webhook events containing pull request data, directly injecting fields into agent prompts. How should input parameter validation and schema handling be structured? | fail→fail | 18,819 | 28,931 | +54% | 1 | 1 | 0% | 3,032 | 4,904 | +62% | 0 | 0 | — |
▸case-18 A 5,000-line diff exceeds the context window of our review agents. We plan to truncate the diff at line 1,000 and silently ignore the remaining 4,000 lines. How should context management handle oversized PR diffs? | fail→fail | 16,514 | 21,650 | +31% | 1 | 1 | 0% | 2,595 | 4,518 | +74% | 0 | 0 | — |
▸case-19 In a sequential review workflow, Agent A flags a bug and suggests a fix, but the suggested fix introduces a syntax error that Agent B consumes without checking. How should context state and validation be handled between sequential review agents? | pass→fail | 13,201 | 16,067 | +22% | 1 | 1 | 0% | 2,187 | 3,250 | +49% | 0 | 0 | — |
▸case-20 Our multi-agent code review system spent $500 on LLM tokens reviewing a single 10-line documentation update because all 15 agents ran with maximum context. How should performance and cost optimization be configured? | pass→fail | 17,866 | 23,381 | +31% | 1 | 1 | 0% | 2,961 | 4,909 | +66% | 0 | 0 | — |
▸case-21 Our aggregation module outputs review findings as an unsorted flat list of 40 bullet points mixing critical security flaws with minor code formatting tips. How should result synthesis structure the final review report? | fail→fail | 18,081 | 15,384 | -15% | 1 | 1 | 0% | 2,882 | 3,069 | +6% | 0 | 0 | — |
▸case-22 We want to use a single generic system prompt for all 10 agents in our multi-agent code review tool, relying entirely on temperature settings to differentiate their reviews. How should agent types and specializations be configured? | fail→fail | 125,277 | 17,007 | -86% | 1 | 1 | 0% | 2,513 | 3,389 | +35% | 0 | 0 | — |
▸case-23 How can our orchestration tool verify that the multi-agent code review completed successfully and produced actionable, high-quality results for developer consumption? | fail→fail | 17,061 | 24,888 | +46% | 1 | 1 | 0% | 2,616 | 4,369 | +67% | 0 | 0 | — |