跳到正文
cccskills
免费在 GitHub 公开

spec-first-delivery

Force plan, HLD, ADRs, and constraints to exist BEFORE any code is generated. Trigger when starting a new feature, epic, or significant change.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(1)

  • SKILL.md3.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Spec-First Delivery

Review the spec harder than the code. The cheapest place to be wrong is the spec.

The harness runs in this order: spec → plan → tasks → code. Skipping the spec is not faster — it is a deferred rewrite.

When to use

  • Any new feature or epic with more than ~1 day of effort
  • Infrastructure or architectural changes
  • Anything involving a new external dependency, data boundary, or API contract
  • Any time an agent offers to "just start coding"

Procedure

  1. Establish the vision artifact — confirm the project's vision document exists and is current (e.g. docs/VISION.md or equivalent per project structure). If not, create or update it before proceeding.
  2. Write the feature spec — document: what, why, who (user/actor), constraints, acceptance criteria, and out-of-scope. Reject vague specs ("improve performance") in favour of measurable ones ("p99 latency < 200 ms under 1k rps").
  3. Author or reference the HLD — a high-level design covering: components affected, data flows, integration points, and failure modes. Diagrams preferred (C4 or sequence).
  4. Create or update ADRs — one ADR per significant technical decision. Format: Context → Decision → Consequences (trade-offs). Number sequentially (ADR-NNN).
  5. Document constraints up front — security requirements, data residency, budget ceiling, compliance scope, team skill set. Constraints missed here surface as blockers late.
  6. Plan applicable service transition — identify receiving owner, support, monitoring, runbooks, recovery, knowledge transfer and user acceptance. Schedule their dependencies before the releases they enable; document proportionate non-applicability.
  7. Validate spec completeness — the spec is done when: acceptance criteria are testable, the failure mode is named, rollback is considered, and cost impact is estimated.
  8. Only then: generate the implementation plan and tasks. Hand the spec (not a prompt) to the agent.

Use adoption and evidence guidance when requirements come from conversations or when operational ownership is unclear. A discussion remains a proposal until its accountable owner accepts it within scope.

Outputs

  • requirements/FEATURE_SPEC.md (or equivalent per project structure)
  • architecture/decisions/ADR-NNN-<short-title>.md (one per decision)
  • architecture/HLD-<feature>.md (if not already covered)
  • Updated project roadmap (e.g. docs/ROADMAP.md or equivalent per project structure) with new tasks or milestones

Guardrails

  • No code without a spec. If an agent begins generating code before a spec exists, stop it and invoke this skill.
  • Loose spec = confident but wrong output. A vague spec produces plausible-looking, wrong code. Tighten the spec first.
  • Spec is not a prompt. The spec is a structured document with testable criteria; a prompt is a hint. Do not substitute one for the other.
  • Context efficiency: Evaluate whether full spec documents are needed, or whether ADRs + plan mode provide sufficient context. Overhead should be proportional to risk.

Anti-rationalization table

Excuse the agent makesCounter
"I have enough context to start coding"No spec = no acceptance criteria = no way to know if the output is correct. Write the spec.
"The spec will slow us down"A missing spec causes rewrites. Rewrites cost more than specs.
"The requirements are obvious"Obvious requirements are the ones most often wrong. Write them down and verify.
"We can spec it after the prototype"Prototypes become production. Spec it now.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent systems built on ADK. Authoritative source: adk.dev.

中文简介正在准备中。当前显示原文说明。

jpantsjoha/ai-native-developer-experience132026年10月16日 更新

JP's signature red-team pass — "how would I break this?" Argue against your own approach before proceeding. Trigger on any high-stakes decision, architecture choice, or before marking work complete.

中文简介正在准备中。当前显示原文说明。

jpantsjoha/ai-native-developer-experience132026年10月16日 更新

Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.

中文简介正在准备中。当前显示原文说明。

jpantsjoha/ai-native-developer-experience132026年10月16日 更新

Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes here.

中文简介正在准备中。当前显示原文说明。

jpantsjoha/ai-native-developer-experience132026年10月16日 更新

LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.

中文简介正在准备中。当前显示原文说明。

jpantsjoha/ai-native-developer-experience132026年10月16日 更新

Challenge vision alignment, value, cost, risk and operating ownership at material scope or release decisions.

中文简介正在准备中。当前显示原文说明。

jpantsjoha/ai-native-developer-experience132026年10月16日 更新

查看 jpantsjoha 的全部技能

报告此技能的问题