Check whether your AI initiative has enough traceable evidence for a credible value review, and receive a readiness score, evidence gaps, and clear next steps.
Turn your AI pilot data into a traceable decision pack with TCO, ROI, NPV, payback, sensitivity analysis, evidence gaps, risks, and clear next steps.
Three reviewer lenses audit an AI agent's or contractor's done report, cross-examine each other, and give an accept/do-not-accept recommendation.
离线对比两份JUnit XML报告，分别列出新增失败、恢复、新增与移除的测试用例，保留来源身份并输出差异报告。需自行提供CPython 3.11.1；不执行测试、不推断失败根因。
Three AI reviewer lenses audit your grant narrative for reviewer fatigue, eligibility gaps, and weak impact proof, then cross-examine each other. No funding outcome guaranteed.
Review an AI Skill before installation or publication. Get a concrete verdict with evidence, permissions, secret risks, license constraints, and the safest next step.
This agent helps Python teams using the Anthropic or OpenAI SDK safely cut LLM spend without risking regressions. With zero code changes, it maps call sites and runs your test scenarios to rank token waste from missed prompt caching or heavy model tiers. It tests fixes in isolated git worktrees, enforcing a strict "do-no-harm" gate that reverts any patch failing to preserve behavior. It delivers a clear savings audit that converts into verified, behavior-preserving code patches upon opt-in.