Agno

检查代码风格、命名、错误处理、硬编码机密信息，以及潜在的缺陷、安全和性能问题；可运行自动风格检查，并按严重程度整理反馈。

• 合并前审查代码
• 查找风格和命名问题
• 按严重程度整理审查意见

根据Product记录和上次成功发送后的变化，总结进展、负责人待办和失败项，并写入Wiki、发送到已配置的目标；无需关注时记录一次正常检查。

• 查看Product有哪些变化
• 了解负责人需要处理的事项
• 将摘要写入Wiki并发送到指定目标

依据产品操作手册、项目、任务和已配置的信息源刷新状态，记录观察到的进展与失败，清除已归档工作的引用，并提交项目完工建议供人审核，不会更改任务或项目状态。

• 依据来源证据更新产品进展
• 在状态中记录失败或变化
• 清理已归档工作的引用

检查运行 AgentOS 进程的主机信息，包括操作系统、架构、Python 环境、处理器和 UTC 时间；也可列出指定目录的直接内容，默认查看技能目录。

• 查看主机操作系统和 Python 环境
• 查询主机名与当前 UTC 时间
• 列出目录中的直接内容

检查产品已配置的信息源，根据实际核实的证据，为合适的项目提出可执行的待办事项。每项建议附证据链接和内容哈希，并用稳定键避免重复。

• 从产品信息源中寻找待办事项
• 依据可核实的证据提出待办建议
• 信息源变更后重新评估建议

为 Git 提交、分支命名和拉取请求提供实用指引，包括格式示例、命名模式、说明模板，以及开始、更新和提交工作的常用命令。

• 为改动撰写提交说明
• 按工单确定分支名称
• 整理拉取请求说明

User-driven loop to change an existing agent in this AgentOS — add a tool/MCP server/toolkit, add a capability (knowledge base, learning/memory, sub-agent, scheduled task), grow the safe Studio registry so components built at runtime gain a new capability, refine its instructions, or fix a specific known bug, verifying each change against the selected development runtime. Use whenever the user names a concrete change to an agent, or wants a new building block available to the platform. For autonomous hardening with no specific change in mind, use eval-and-improve.

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Add a new agent to this AgentOS. Runs guided discovery or takes a concrete idea, then generates agents/slug.py, registers it in app/main.py, adds its manifest entry (description + quick prompts), reloads the selected development runtime, and smoke-tests it for you. Covers the product agent too — ingest a product's website/docs into the product knowledge base and build a knowledge-only agent over it. Use whenever the user wants to add or create a new agent, or names a product (theirs or one they use) they want an agent for.

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Evaluate and improve an existing AgentOS agent or team. Repair failing eval cases, or explore behavior by generating simulated user scenarios from instructions and sanitized usage, running probes, inspecting evidence and iterating within a bounded budget. Use for failing evals, autonomous hardening or simulation-driven improvement. Use create-evals for coverage authoring alone and extend-agent for a requested new capability.

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Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.

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Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.

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Author eval coverage for an agent in this AgentOS — map what the agent promises, mine real sessions and eval history from Postgres for scenarios, propose capabilities worth testing, then write, run, and audit Case entries in evals/cases.py. Use when the user wants evals created, coverage added, or an agent's behavior pinned down as tests. For failing suites or exploratory simulation-driven hardening, use eval-and-improve instead.

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Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.

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Agno AI agent framework - build production-ready agents, multi-agent teams, workflows, MCP integrations, and deploy with AgentOS. Use when building, debugging, or learning about Agno agents.

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User-driven loop to change the context agent (or any agent in this repo) — add a source/tool/MCP server, add a capability (knowledge base, memory, sub-agent, scheduled task), refine its instructions, or fix a specific known bug, verifying each change against the live container. Use whenever the user names a concrete change ("add a Google Drive source", "make the daily rundown include the calendar", "the agent keeps doing Y, fix it"). For autonomous hardening with no specific change in mind use improve-agent.

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Produce the owner's week-ahead plan — scheduled meetings and due-or-overdue reminders for the next 7 days, grouped by day, read from the CRM and enriched from the real calendar, Slack, and knowledge-base briefs when connected. Use for "what's my week", "plan the week ahead", "what's coming up".

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Research a topic and file the result — sweep what we already know (knowledge base, crm, and slack when connected), go to the web for current and external background, then write a clear sourced brief and FILE it into the knowledge base with update_knowledge. Labels what came from the web versus our own notes. Use for "research X and save it", "look into X and write it up", "find out what's known about X and file it", "write up a brief on X".

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Process the day into filed context — sweep today's activity (the inbound queue, CRM meetings and reminders, plus calendar, slack, and gmail when connected), extract what's worth keeping, and file it into the CRM and knowledge base. Use for "process today", "log today", "file today", "end-of-day wrap".

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Brief the owner before a meeting or conversation with a person, company, or project — sweep what we already know (crm + knowledge, plus slack, calendar, and gmail when connected), anchor to any upcoming meeting with them, and for people or orgs we don't know, widen to a web search for public background. Use for "prep me for my meeting with X", "brief me on X before the call", "what do we know about X".

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Sweep the knowledge base for what needs attention — stubs with no real content, specs whose status is stale, decisions that were made but never written down, and pages missing from the index — and return a short "what needs attention" list. Read-only, so it reports rather than fixing anything itself. Use for "what in my knowledge base needs attention", "review my knowledge base", "what's stale or unfiled", "knowledge base health check", "tidy up my knowledge".

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The owner's morning brief — inbound updates awaiting acknowledgment (via the rundown tool), today's meetings and due-or-overdue reminders from the CRM and the real calendar, the emails worth seeing from Gmail, and the Slack threads that need their eyes — each source folded in when connected and stitched into one short digest. Use for "daily rundown", "what's on today", "morning brief", "catch me up".

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Run this repo's eval suite (python -m evals), diagnose every failing case, and fix in scope (agent instructions, or the eval case when its assertion was wrong) until all cases pass. Use whenever the user wants to run the evals, check for regressions, or fix a red eval suite ("run the evals", "why is a case failing", "make the eval suite green"). Targets the committed cases in evals/cases.py; for ad-hoc probing of one agent's live behavior use improve-agent instead.

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Autonomously harden the context agent (or any agent in this repo) — derive probes from the agent's own instructions, run them against the live container, judge the responses and tool calls, and edit the agent until it reliably does what it says it does. Use when the user wants to test, harden, stress-test, or improve an agent's reliability WITHOUT naming a specific change ("make sure context is solid", "harden this agent", "probe it for failure modes"). For a user-directed change use extend-agent; to run the committed eval suite use eval-and-improve.

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Repo-wide consistency sweep for @context — diff docs against code (the context agent and its providers wired as documented, every env var documented, every path real, every script behaving as advertised), auto-fix mechanical drift, and flag anything bigger. Use when the user wants to review the repo for consistency, prep a public release, or clean up drift after a refactor ("review the repo", "is everything consistent before we ship", "run the drift sweep"). This is a deliberate full sweep, not a quick lint.

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