Agile Product Owner
davila7/claude-code-templates
Agile product ownership toolkit for Senior Product Owner including INVEST-compliant user story generation, sprint planning, backlog management, and velocity tracking.
AI Agent 系统架构专家 Owner — 当任务涉及 Agent 路由、工具调用、上下文管理、记忆、状态机、权限、人机协作、可观测性、回放验证或模型辅助治理时使用;要求把 Agent 行为设计成可解释、可恢复、可审计。
$ npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install devcodex-labs/devcodex ai-agent-system-architecture --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/devcodex-labs/devcodex.git skills-src && mkdir -p .claude/skills && cp -r skills-src/content/skills/ai-agent-system-architecture .claude/skills/ai-agent-system-architecture && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ai-agent-system-architecture" agent skill from https://github.com/devcodex-labs/devcodex/tree/main/content/skills/ai-agent-system-architecture into .claude/skills/ai-agent-system-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-system-architecture", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/devcodex-labs/devcodex/tree/main/content/skills/ai-agent-system-architectureType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install devcodex-labs/devcodex ai-agent-system-architecture --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/devcodex-labs/devcodex.git skills-src && mkdir -p .agents/skills && cp -r skills-src/content/skills/ai-agent-system-architecture .agents/skills/ai-agent-system-architecture && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-agent-system-architecture" agent skill from https://github.com/devcodex-labs/devcodex/tree/main/content/skills/ai-agent-system-architecture into .agents/skills/ai-agent-system-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-system-architecture", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install devcodex-labs/devcodex ai-agent-system-architecture --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/devcodex-labs/devcodex.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/content/skills/ai-agent-system-architecture .cursor/skills/ai-agent-system-architecture && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ai-agent-system-architecture" agent skill from https://github.com/devcodex-labs/devcodex/tree/main/content/skills/ai-agent-system-architecture into .cursor/skills/ai-agent-system-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-system-architecture", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/devcodex-labs/devcodex.git --path content/skills/ai-agent-system-architecture--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install devcodex-labs/devcodex ai-agent-system-architecture --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/devcodex-labs/devcodex.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/content/skills/ai-agent-system-architecture .gemini/skills/ai-agent-system-architecture && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ai-agent-system-architecture" agent skill from https://github.com/devcodex-labs/devcodex/tree/main/content/skills/ai-agent-system-architecture into .gemini/skills/ai-agent-system-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-system-architecture", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install devcodex-labs/devcodex ai-agent-system-architectureInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/devcodex-labs/devcodex.git skills-src && mkdir -p .github/skills && cp -r skills-src/content/skills/ai-agent-system-architecture .github/skills/ai-agent-system-architecture && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ai-agent-system-architecture" agent skill from https://github.com/devcodex-labs/devcodex/tree/main/content/skills/ai-agent-system-architecture into .github/skills/ai-agent-system-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-system-architecture", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install devcodex-labs/devcodex ai-agent-system-architecture --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/devcodex-labs/devcodex.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/content/skills/ai-agent-system-architecture .opencode/skills/ai-agent-system-architecture && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ai-agent-system-architecture" agent skill from https://github.com/devcodex-labs/devcodex/tree/main/content/skills/ai-agent-system-architecture into .opencode/skills/ai-agent-system-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-system-architecture", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ai-agent-system-architectureAI Agent 系统架构专家 Owner — 当任务涉及 Agent 路由、工具调用、上下文管理、记忆、状态机、权限、人机协作、可观测性、回放验证或模型辅助治理时使用;要求把 Agent 行为设计成可解释、可恢复、可审计。
AI Agent System Architecture is an agent skill from devcodex-labs/devcodex. AI Agent 系统架构专家 Owner — 当任务涉及 Agent 路由、工具调用、上下文管理、记忆、状态机、权限、人机协作、可观测性、回放验证或模型辅助治理时使用;要求把 Agent 行为设计成可解释、可恢复、可审计。
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `intent.json`).
The repository describes itself as: Intent-driven AI coding workflow runtime for consistent context, skills, approvals, validation, and handoffs across six AI coding hosts. The licence is AGPL-3.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 1dd4525. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
AI Agent System Architecture loads about 2.4k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 572 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from devcodex-labs/devcodex at commit 1dd4525, republished under its AGPL-3.0 licence (© devcodex-labs). 572 words, ~2,419 tokens.
.claude/skills/ai-agent-system-architecture/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.本 Skill 负责 AI Agent 系统 Owner 视角。它把 Agent 当作状态机和工具执行系统,而不是只关注 prompt 文案。
模型表达是非确定性输入,质量诊断与工具事实分别处理:内部标题、模板、阅读回执和短期入口元数据应能重建,不能成为整项任务硬门禁。恢复需保留原任务身份、实际宿主身份、精确目标与 operation 派发/效果记录;同一操作可读回后结算,未知副作用不得盲目重放。验证采用公共入口、表达变体、故障恢复和真实文件结果,不把固定文案断言当成原意遵守的证明。
| 场景 | 是否触发 |
|---|---|
| Agent 路由、意图识别、Skill 触发、工具调用、权限、记忆、handoff、summary、hook 状态 | 必须 |
| 任务涉及模型辅助治理、自动吸纳、自动复审、回放验证、可观测性或人机确认 | 必须 |
| 报告或方案需要解释 Agent 为什么选择某工作流或工具 | 必须 |
| 纯业务代码且不涉及 Agent 执行系统 | N/A + skipReason |
| Gate | 要求 | 证据 |
|---|---|---|
AiAgentSystemArchitectureGate | Agent 行为必须有路由、工具权限、上下文、状态机、观测和人机边界 | intentRouting、toolPermissionBoundary |
AgentRoutingGate | 意图、Skill、模式和降级路径必须可解释 | intentRouting |
ToolPermissionBoundaryGate | 工具权限、危险操作、确认和 fallback 必须明确 | toolPermissionBoundary |
ContextAcquisitionGate | 每条消息必须先形成语义种子与唯一目标,再按计划读取最小充分上下文,并用 Post 成功回执证明完成 | IntentSeedV1、ContextReadPlanV2、ContextReadReceiptV2(V1 兼容) |
ContextMemoryStateGate | 上下文恢复、记忆、handoff 和状态新鲜度必须设计 | contextMemoryModel |
TaskContinuityWriteGate | 正式任务写入必须由单一 V5 store owner、精确 state/writer fence、canonical write context、operation 状态机与 terminal lineage 共同约束;TTL 不产生写权 | taskContinuityWriteContract |
ReplayObservabilityGate | 行为验证不能只靠文字说明,需 replay、fixture 或日志证据 | observabilityReplay |
TurnLivenessRecoveryGate | 长任务或工具输出后的 turn 必须用事件时间、AI-owned lease、continuation ACK、terminal invariant 与 checkpoint 区分运行、可疑、可恢复停滞和终态 | turnLivenessContract、TurnRecoveryCard、TurnLivenessEvidence |
LocalTaskTraceGate | 当前 turn 的 typed trace 必须严格有序、拒绝重复/终态后追加,并只提供不执行 payload 的只读 replay | LocalTaskTraceV1、LocalTaskTraceReplayV1 |
RepairCollaborationRoleBoundaryGate | repair task 必须把决策/验收与执行/验证角色、授权证据、状态与独立复证设计清楚;模型或 Agent 名称不构成风险分类 | roleAssignments、authorizationEvidence、independentReReview |
AgentCapabilityDomainCompletenessGate | 声称完整/最终 Agent 架构或平台前先声明 completenessObject,并验证请求链、反馈链、横切面及适用产品/企业链 | agentCapabilityDomainMatrix |
| completenessObject | 必查覆盖 |
|---|---|
| kernel | ingress→cognition→context/knowledge→planning→execution/tools→response;observe→evaluate→evolve;governance/model/infrastructure |
| runtime | kernel + local/hosted composition、state、tool registry、security、observability、replay |
| developer-product | runtime + build→version→publish→deploy→invoke、SDK/CLI/API、local developer runtime、Agent Studio |
| hosted-platform | developer-product + run API、provider/connector/credential、deployment/fleet、tenant/workspace |
| enterprise-saas | hosted-platform + organization、entitlement、usage/metering/billing、admin/ops、audit/compliance |
每个适用能力域必须记录 owner、publicPrivateBoundary、runtimeStatus、validationRoute。较窄对象通过不能升级解释为较宽对象完整;报告使用“完整/最终/无需新增域”时,缺少 completenessObject 或任一适用域即判 incomplete。
新增或改变 Agent 可调用能力面时,本 Skill 只向 spec-governance#CapabilitySurfaceDecisionGate 提供语义判断边界、model/application/user/host 控制方、read/write/execute 权限、状态机、authority、Task 协商、取消/超时/幂等和审计证据。它读取中央 decisionRef,不得自行决定或复制 preferredSurface 等 canonical 字段;MCP 能力、Tasks 或宿主行为没有 direct evidence 时保持 UNVERIFIED,不得由 Agent 名称或概念相似性推断支持。
Agent 上下文获取采用以下状态链,任何一步都不得用后一步的推断倒填:
IntentSeedV1 → unique project/activeRoot → ContextReadPlanV2(V1 兼容)→ attempted → PostToolUse observed → ContextReadReceiptV2(V1 兼容)→ project reality/final routeIntentSeedV1 仅来自当前消息语义和已观察到的 continuity,不得先全文读取 Profile / memory 再“识别”意图;关键词不是 canonical intent。ContextReadPlanV2 必须显式区分 baseline、selected、excluded、unclassified 与 fullReadReason,并把稳定 planContentId 与 invocation planId 分离;ContextReadPlanV1 仅保留读取兼容。默认读取最小充分来源;Profile 规划阶段不得 hidden full read,记忆使用 bounded status/session/summary query。ContextReadReceiptV2 只接受 planId、planContentId、contextEpoch、activeRoot、source identity/query 和结果精确关联的 PostToolUse 成功证据。PreToolUse、计算 cache hit、旧全文工具返回或 fallback 文案都不能声明 complete;V1 receipt 不具备跨 epoch delivery reuse 资格。ContextSourceObservationLedgerV1,lifecycle receipt 只作可重建投影;SkillRoute 仅在 epoch/plan/root/project 全同且 source metadata 新鲜时重放。旧快照覆盖不得丢失已交付证据,stale/blocked、source-digest 或 profile-drift 不得被 ledger 绕过。partial/unverified,后续安全、CP、治理和验证门禁不得因节流而降低。正式任务的 locator、当前写者、单次 operation、终态和运行时投影是不同边界,不能把“找到了任务”解释为“可以写”。所有正式 envelope commit 必须由 TaskRecoveryStoreV5 精确比较 TaskRecoveryCommitFenceV1.stateSequence + writerGeneration;force 不绕过 stale fence,writer generation 只可在 owner transition 中增加 1。owner claim/transition 后必须 readback CanonicalTaskWriteContextV1,writer 绑定 task/root、lifecycle revision、state sequence、writer generation、holder session、operation/settled set digest、runtime generation 与 context digest。TTL 只作 freshness、清理和诊断,不能产生 takeover。
每个任务最多存在一个未结算 mutating TaskOperationRecordV1,固定经过 prepared → dispatched → observed → settled,未知副作用转 reconcile-required,未派发才允许 aborted-zero-effect。已派发操作不得自动重试;迟到回执和 emergency reserve 只能推进同一 operationId、idempotency key、writer generation、exact targets 与 before digest。terminal 必须同时验证 current writer、write context fence、settled set 和独立证据;replay 零新写,reopen 产生 lifecycle revision+1 与新 owner generation。
当任务涉及长时间运行、工具完成后无续接、线程持续 inProgress、恢复或宿主停滞时,先冻结 TurnLivenessContract:
| 字段 | 要求 |
|---|---|
stateModel | 至少区分 idle / running / awaiting-continuation / suspect / stalled-recoverable / completed / error / interrupted |
eventEvidence | turnKey / lastEventType / lastEventAt / lastToolOutputAt / continuationAckAt |
lease | 只有已放行的 AI-owned operation 才能建立 lease;用户进程或未知 PID 不能作为可清理 lease |
terminalInvariant | tool output 不等于 turn 完成;显式 Stop/error/interruption 后必须清除 in-flight lease |
checkpoint | phase / artifactPaths / nextAction / resumeToken / idempotencyKey,恢复前必须验证幂等边界 |
checkpointValidation | response-time 与 post-execution 分开记录;缺 post evidence 只能 unverified/incomplete-timeout,实际 terminal evidence 才能 pass |
localTaskTrace | traceId/turnKey/status/sequence/openedAt/completedAt/events;eventId 唯一、sequence 从 1 递增、terminal 唯一且最后 |
capabilityBoundary | 分开记录 host-native watchdog、Hook event-time detection 与 read-only sidecar;Hook 无事件时不得宣称能自唤醒 |
validation | direct replay + no-continuation / active-lease / restart-rehydrate / duplicate-recovery fault matrix |
默认 awaiting-continuation 可采用 120 秒 suspect / 300 秒 stalled advisory;慢模型推理和长工具必须由更长的 agent/operation lease 覆盖,不能机械套用 ACK 阈值。观察到 stale 只生成 TurnRecoveryCard;没有宿主授权与幂等复证时,禁止自动重放 mutation、kill/restart/interrupt/resume。
LocalTaskTraceV1 只保存当前 turn;历史 turn 由 TurnLiveness 摘要承接。重启时先校验 identity/sequence/duplicate/terminal,再生成 LocalTaskTraceReplayV1 数据投影;replay 的 stateMutation/operationReplay/payloadExecution/processControl 必须全部为 false。
IntentSeedV1、目标、ContextReadPlanV2(V1 兼容)、Skill、工具、回执、确认、报告。## AiAgentSystemArchitectureGate
| 字段 | 内容 |
|------|------|
| intentRouting | 意图、Skill、模式、降级路径 |
| toolPermissionBoundary | 工具权限、确认、危险操作和 fallback |
| contextMemoryModel | 上下文、记忆、handoff、summary、状态新鲜度 |
| contextAcquisition | seed、target、plan、selected/excluded、fullReadReason、fallback |
| contextReadReceipt | `ContextReadReceiptV2`(V1 兼容)的 Post 成功证据、内容身份、复用来源、缺失来源与完成状态 |
| stateMachineHandoff | 状态机、恢复、阻塞、交接 |
| observabilityReplay | replay、fixture、日志、validate 证据 |
| turnLivenessContract | 状态、事件、lease、ACK、终态、双阶段 checkpoint、LocalTaskTrace、能力边界与故障矩阵 |
| taskContinuityWriteContract | store owner、state/writer fence、CanonicalTaskWriteContext、operation 状态机、terminal/reopen、TTL 边界与负向探针 |
| humanInLoopBoundary | 用户确认、auto、人工复核和最终责任边界 |
| capabilitySurfaceEvidence | `decisionRef`、控制方、read/write/execute、authority、状态/Task 与直接证据边界 |
| evidenceMatrix | 判断 -> hook / runtime / report / memory / replay / tests |
| agentCapabilityDomainMatrix | completenessObject -> domain -> owner / boundary / runtime / validation || 反模式 | 修正 |
|---|---|
| 把 Agent 能力写成 prompt 愿望 | 写路由、状态机、工具权限和验证 |
| 用 summary 覆盖文件真相源 | 遵循 Context Rehydration Contract |
| 先全文读取 Profile / memory 再判断意图 | 先形成 IntentSeedV1 与唯一目标,再执行有界计划和定向查询 |
| 把 PreToolUse、cache hit 或旧全文工具返回写成已完成 | 仅以精确关联的 PostToolUse 成功结果生成 ContextReadReceiptV2;delivery reuse 还须同 session/epoch/source identity |
| 自动模式绕过危险确认 | 保留 S01/S06 等不可豁免底线 |
| 行为变更无 replay | 补 direct/fixture replay 或等价 validate |
| 把 Hook 状态写入当成无事件 watchdog | 明确 event-time detection 边界,并用宿主能力或 gray read-only sidecar补充观察 |
| trace replay 执行 payload 或恢复写操作 | 只返回已校验的数据投影;写操作恢复必须另走授权和幂等复证 |
intent / routing:意图和 Skill 路由是 Agent 系统入口。memory / summary:上下文恢复和状态新鲜度需要联动。host-contract-verification:宿主行为变化需验证事件和可见回复。© devcodex-labs, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in content/skills/ai-agent-system-architecture of devcodex-labs/devcodex.
Open the folder on GitHubat commit 1dd4525
AI Agent System Architecture next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AI Agent System Architecture this skilldevcodex-labs/devcodex | 439 | — | ~2.4k | Automated safety check: Pass | AGPL-3.0 | |
| Agile Product Ownerdavila7/claude-code-templates | 32k | 2 repos | ~256 | Automated safety check: Pass | MIT | |
| Agile Product Owneralirezarezvani/claude-skills | 28k | 3 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Super Product Ownersyahiidkamil/Software-Engineer-AI-Agent-Atlas | 401 | — | ~7.9k | Automated safety check: Pass | None | |
| Find Lead Account Ownerzapier/gtm-cheat-codes | 342 | — | ~275 | Automated safety check: Pass | MIT | |
| Owner VoiceAnastasiyaW/codex-claude-code-config | 154 | — | ~874 | Automated safety check: Notes | MIT |
davila7/claude-code-templates
Agile product ownership toolkit for Senior Product Owner including INVEST-compliant user story generation, sprint planning, backlog management, and velocity tracking.
alirezarezvani/claude-skills
Writes INVEST-checked user stories with acceptance criteria, splits epics, plans sprints from velocity and ranks the backlog with a weighted score.
syahiidkamil/Software-Engineer-AI-Agent-Atlas
Complete Product Owner / Product Manager capability — a wiki-style knowledge map of product practice in active software development: the role and its boundaries, strategy cascade (vision → OKRs →…
zapier/gtm-cheat-codes
Resolve the right account owner for a lead using contact, company, and deal ownership evidence.
AnastasiyaW/codex-claude-code-config
Пишет и редактирует текст голосом владелицы — сообщение, письмо, пост, объяснение, урок, рабочий разбор, на русском и английском.
antopolskiy/kanban-md
Review kanban-md feature requests, issues, PRs, and design proposals for product fit, domain-model growth, configurability, and compatibility.
devcodex-labs/devcodex
无障碍与国际化专家 Owner — 当任务涉及可访问性、键盘操作、焦点、屏幕阅读器、ARIA、语言地区、本地化、RTL、翻译资源、用户可见文案或多语言文档时使用;要求把包容性体验和本地化验证绑定到真实用户路径。
devcodex-labs/devcodex
API 契约架构专家 Owner — 当任务涉及 public API、HTTP/SDK/CLI 契约、版本兼容、错误模型、分页过滤、幂等、Schema、类型、迁移或消费者影响时使用;要求先冻结消费者契约,再设计实现与验证。
devcodex-labs/devcodex
架构设计文档编排 Owner — 当用户要求架构设计、系统设计、技术架构或可指导开发、Review 与任务拆分的完整方案时使用;要求从业务流程反推节点、状态、数据、一致性、异常补偿、ADR 与实施任务。
devcodex-labs/devcodex
审查公共维度 G0~G5 + Profile Freshness Check — 所有 audit 子类型必先执行的基础维度层
devcodex-labs/devcodex
审计工作流的跨会话状态机 — 在 <audit-root/.audit-state/<session-id.json 持久化轮次/发现项/收敛状态,支持 Token 中断后精准恢复
devcodex-labs/devcodex
后端领域架构专家 Owner — 当任务涉及领域模型、业务流程、权限、API、事务、一致性、幂等、兼容、数据边界、服务职责或用户要求从后端/领域专家角度审查时使用;要求用领域语言和业务不变量约束实现。
AI Agent 系统架构专家 Owner — 当任务涉及 Agent 路由、工具调用、上下文管理、记忆、状态机、权限、人机协作、可观测性、回放验证或模型辅助治理时使用;要求把 Agent 行为设计成可解释、可恢复、可审计。. AI Agent System Architecture is an agent skill from devcodex-labs/devcodex.
Run `npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a claude-code`. Or copy the skill folder (content/skills/ai-agent-system-architecture in devcodex-labs/devcodex) into .claude/skills/ai-agent-system-architecture in your project. Claude Code loads it when a task matches its description.
Run `npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a codex`. Or copy the skill folder (content/skills/ai-agent-system-architecture in devcodex-labs/devcodex) into .agents/skills/ai-agent-system-architecture in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add devcodex-labs/devcodex --skill ai-agent-system-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-agent-system-architecture, .gemini/skills/ai-agent-system-architecture, .github/skills/ai-agent-system-architecture and .opencode/skills/ai-agent-system-architecture in your project.
SKILL.md names no scripts, command-line tools or credentials: AI Agent System Architecture is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
AI Agent System Architecture is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.4k tokens (SKILL.md is roughly 9.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with AI Agent System Architecture: Agile Product Owner (davila7/claude-code-templates, 32k stars), Agile Product Owner (alirezarezvani/claude-skills, 28k stars), Super Product Owner (syahiidkamil/Software-Engineer-AI-Agent-Atlas, 401 stars) and Find Lead Account Owner (zapier/gtm-cheat-codes, 342 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
devcodex-labs (a GitHub organization) maintains it in devcodex-labs/devcodex, which has 439 GitHub stars. The repository holds 70 skills in this directory. The repository was last updated on September 17, 2026.
Source: devcodex-labs/devcodex on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.