{"id":"fd907b3b-6322-405e-99bb-94f8abf35b0d","entityType":"agent","slug":"clawhub-yuchangxu1989-openclaw-agent-effect-operations","name":"Agent Effect Operations","canonicalUrl":"https://www.xpersona.co/agent/clawhub-yuchangxu1989-openclaw-agent-effect-operations","canonicalPath":"/agent/clawhub-yuchangxu1989-openclaw-agent-effect-operations","generatedAt":"2026-10-10T23:47:03.326Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-10T20:11:44.631Z","emptyReason":null},"description":"AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and mo... Skill: Agent Effect Operations Owner: yuchangxu1989-openclaw Summary: AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and mo... Tags: latest:0.0.2 Version history: v0.0.2 | 2026-04-19T10:08:46.898Z | user 更新描述至最新项目定位 v0.0.1 | 2026-04-19T04:31:15.972Z | auto Initial release of agent-effect-operations (AEO): - Measure","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. 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yuchangxu1989-openclaw\n\nSummary: AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and mo...\n\nTags: latest:0.0.2\n\nVersion history:\n\nv0.0.2 | 2026-04-19T10:08:46.898Z | user\n\n更新描述至最新项目定位\n\nv0.0.1 | 2026-04-19T04:31:15.972Z | auto\n\nInitial release of agent-effect-operations (AEO):\n\n- Measure agent performance and attribute outcomes to specific agents.\n- Track quality trends over time and detect changes in agent output.\n- Optimize costs by modeling token use, task durations, and retry rates.\n- Provide real-time SLA monitoring (completion rate, latency, error rate) and alerting.\n- Detect agent performance degradation and trigger evolution workflows automatically.\n- Part of the Self-Evolving Harness operations infrastructure.\n\nArchive index:\n\nArchive v0.0.2: 3 files, 1584 bytes\n\nFiles: skill-card.md (1599b), SKILL.md (334b), _meta.json (142b)\n\nFile v0.0.2:SKILL.md\n\n---\nname: agent-effect-operations\ndescription: >-\n  AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and monitor SLAs.\n---\n\n# Agent Effect Operations\n\nFile v0.0.2:_meta.json\n\n{\n  \"ownerId\": \"kn71kk78dbbgtentzcetv7d1zn8536vd\",\n  \"slug\": \"agent-effect-operations\",\n  \"version\": \"0.0.2\",\n  \"publishedAt\": 1776593326898\n}\n\nFile v0.0.2:skill-card.md\n\n## Description:\n\nAgent Effect Operations helps teams monitor agent performance, detect quality drift, diagnose root causes, track costs and SLAs, and plan optimization work.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[yuchangxu1989-openclaw](https://clawhub.ai/user/yuchangxu1989-openclaw)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and operations teams use this skill to frame agent effectiveness operations work, including performance measurement, outcome attribution, quality trend tracking, cost optimization, and SLA monitoring.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The release is a minimal descriptive stub, so users may overestimate the operational functionality available from the artifact alone.\n\nMitigation: Confirm required operational components, integrations, and workflows are present before relying on the skill for agent performance operations.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, text]\n\n**Output Format:** [Markdown or text guidance]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [The artifact is a descriptive stub and may require additional components to provide operational functionality.]\n\n## Skill Version(s):\n\n0.0.2 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v0.0.1: 2 files, 1000 bytes\n\nFiles: SKILL.md (941b), _meta.json (142b)\n\nFile v0.0.1:SKILL.md\n\n---\nname: agent-effect-operations\ndescription: AEO — Agent Effect Operations. Measure agent performance, attribute outcomes, track quality trends, optimize cost, and monitor SLAs across multi-agent systems. Part of the Self-Evolving Harness operations infrastructure.\n---\n\n# Agent Effect Operations (AEO)\n\nAgent效果运营平台。度量 Agent 表现、归因效果、追踪质量趋势、优化成本、监控 SLA。\n\n核心能力：\n- 任务级效果归因：每个 Agent 完成的任务与最终业务结果之间的因果链\n- 质量趋势分析：跨时间窗口的 Agent 产出质量变化检测\n- 成本效率优化：token 消耗、任务耗时、重试率的综合成本模型\n- SLA 监控与告警：任务完成率、响应时延、错误率的实时监控\n- 衰退检测与进化触发：发现 Agent 能力衰退时自动触发 KIVO 进化流程\n\nSelf-Evolving Harness 三大模块之一（KIVO / 研发流水线 / AEO）。\n\nFile v0.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn71kk78dbbgtentzcetv7d1zn8536vd\",\n  \"slug\": \"agent-effect-operations\",\n  \"version\": \"0.0.1\",\n  \"publishedAt\": 1776573075972\n}","readmeExcerpt":"Skill: Agent Effect Operations Owner: yuchangxu1989-openclaw Summary: AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and mo... Tags: latest:0.0.2 Version history: v0.0.2 | 2026-04-19T10:08:46.898Z | user 更新描述至最新项目定位 v0.0.1 | 2026-04-19T04:31:15.972Z | auto Initial release of agent-effect-operations (AEO): - Measure","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: agent-effect-operations\ndescription: >-\n  AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and monitor SLAs.\n---\n\n# Agent Effect Operations"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn71kk78dbbgtentzcetv7d1zn8536vd\",\n  \"slug\": \"agent-effect-operations\",\n  \"version\": \"0.0.2\",\n  \"publishedAt\": 1776593326898\n}"},{"path":"skill-card.md","content":"## Description:\n\nAgent Effect Operations helps teams monitor agent performance, detect quality drift, diagnose root causes, track costs and SLAs, and plan optimization work.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[yuchangxu1989-openclaw](https://clawhub.ai/user/yuchangxu1989-openclaw)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and operations teams use this skill to frame agent effectiveness operations work, including performance measurement, outcome attribution, quality trend tracking, cost optimization, and SLA monitoring.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The release is a minimal descriptive stub, so users may overestimate the operational functionality available from the artifact alone.\n\nMitigation: Confirm required operational components, integrations, and workflows are present before relying on the skill for agent performance operations.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, text]\n\n**Output Format:** [Markdown or text guidance]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [The artifact is a descriptive stub and may require additional components to provide operational functionality.]\n\n## Skill Version(s):\n\n0.0.2 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and mo... Skill: Agent Effect Operations Owner: yuchangxu1989-openclaw Summary: AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and mo... 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