Use when tech-lead is about to record an architecture decision (new tech stack member, deviation from baseline, deployment / observability / auth scheme choice). Provides ADR structure, version-audit appendix format, and "what NOT to ADR" guidance. Replaces ad-hoc copy from ADR-000.
npx skills add https://github.com/pcliangx/AppGenesisForge --skill agf-writing-adr
Use this skill when:
Do NOT write an ADR for:
If unsure → write it. ADR cost is low; the "why" memory is what disappears.
docs/adr/NNN-[slug-kebab-case].md — sequential, zero-padded 3 digits. Examples:
001-jwt-vs-session-auth.md002-llm-caching-policy.md003-deploy-target-fly-io.mdADR-000 is reserved for the system architecture baseline. Never reuse a number; if abandoned, mark Status: Superseded by ADR-NNN.
Proposed → Accepted → (later) Superseded by ADR-NNN / Deprecated. Once Accepted, do not edit decisions; supersede with a new ADR.
Allowed in-place edits on Accepted ADRs:
## 版本与查证 rows when a deferred row resolvesAnything else → new ADR.
# ADR-NNN: [Title]
- 状态:Proposed / Accepted / Superseded by ADR-NNN / Deprecated
- 日期:YYYY-MM-DD
- 决策者:tech-lead [+ co-decider role if any]
- 影响范围:[模块/全栈/单服务]
## 上下文
为什么现在需要这个决策?1–3 段:业务驱动、技术约束、当前痛点、不做这个决策会出什么问题。
## 决策
| 维度 | 选型 | 理由 |
|---|---|---|
| ... | ... | 为什么是它,而不是 [备选] |
或者用文字描述(如果不是结构化对比)。**关键:必须列出至少一个备选方案 + 为什么否决它。**
## 备选方案
- **A. [备选 1]** — pros / cons / 否决理由
- **B. [备选 2]** — pros / cons / 否决理由
如果没列备选 = 你没真正决策,只是默认接受。回去补。
## 影响
- 对现有代码:哪些模块会变 / 不变
- 对团队:谁需要学新东西
- 对成本:每月预估增量(CNY 或 token)
- 对运维:新增监控点 / 告警 / 备份策略
## 本 ADR 不覆盖的决策
明确列出"相关但留给未来 ADR"的内容,避免读者期待落空。
## 后续工作
- [ ] 谁 / 什么时间 / 做什么(具体到角色 + 触发条件)
## 版本与查证
> tech-lead 行事原则 #3「先查最新版再决策」的回填段。新增技术或大版本升级时必填。
**查证基线日期**:YYYY-MM-DD
| 选型 | 选定版本 | 最新稳定版 | 与最新版差距 | 维护状态 | 信息来源(含原文摘录) |
|---|---|---|---|---|---|
| ... | x.y.z | a.b.c | 1 个 minor 落后 | Active | [官方 changelog URL] — "原文..." |
**回填规则**:执行层在落地时(write lockfile / pyproject.toml)回填本表对应行,commit message 加 `docs(adr): backfill ADR-NNN verification for [pkg]`。
ADR 不是事后总结,是决策前的工具:
resolve-library-id → query-docs)拉当前版本官方文档;未收录或版本信息不足再 WebFetch 官方 changelog / release notes。记录"今天最新稳定版 + 维护状态 + 已知 breaking change"做完 1 + 2 但跳过 3,未来一定会被问"当时为啥选这个"——答不了就是组织记忆缺失。
TaskCreate 跟踪「后续工作」中的事项(如有)?ADR 落盘后:
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take pcliangx/agf-writing-adr from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.