Results for “specification”

Create, validate, and consume Google's Open Knowledge Format (OKF) bundles — YAML-frontmatter Markdown files with type / title / description / resource / tags / timestamp fields for portable, interoperable AI-agent knowledge sharing. OKF formalizes the LLM-Wiki pattern into a vendor-neutral open specification so any producer can write and any agent can consume without translation. Routes: use `llm-wiki` for raw source capture + vault maintenance, `obsidian` for Obsidian-vault workflows, `graphify` for durable committed graphs, `scrapling` for web-content extraction into OKF docs. Triggers on: okf, open knowledge format, knowledge bundle, okf document, llm wiki standard, knowledge atom, agent context format, okf frontmatter, okf bundle, knowledge interoperability.

Builds a feature specification from scratch with plan-a-feature and publishes it to a user-specified Confluence location, posting the spec as a parent page and each companion artifact (decision log, team findings, technical notes) as a child page beneath it. Use when the user wants a new feature planned, designed, scoped, or specified AND posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not plan to local files only — use plan-a-feature. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not refine or stress-test an existing plan — use iterative-plan-review. Does not document already-built features to Confluence — use project-documentation-to-confluence.

Run a structured discovery interview and produce a complete, developer-ready Product Requirements Document. Load when the user asks to write a PRD, create product requirements, document a feature, define user stories with acceptance criteria, or turn a rough idea into a formal product requirements document. Also triggers on "document this feature", "write requirements for", "create a one-pager", "turn this into a PRD", "I need a PRD for", or any request to produce a structured product document for stakeholder alignment or engineering handoff. Supports Full PRD, Lean PRD, and One-Pager formats. Note: for executable feature specifications (FRs, NFRs, ACs as Given/When/Then consumable by AI coding agents), route to `feature-spec` instead — PRDs frame the product, feature-specs encode the implementable contract.

Creates decision-focused PRDs and product specs — the kind that make explicit decisions, not document hopes. Make sure to use this skill whenever someone asks to write, review, draft, or improve any product document, including PRDs, feature specs, requirements docs, or product specifications. Trigger even when the user doesn't say "PRD" explicitly — phrases like "write a spec for X", "define requirements for Y", "what should my PRD include", "help me document this feature", or "my spec feels too vague" all apply. Do not attempt to write the PRD directly without consulting this skill. Produces 5-stage PRDs with behavior contracts (15-25 labeled examples for AI features), measurable success metrics with specific thresholds, precise rollout plans with percentages and ramp gates, and antipattern detection. Works for AI features, SaaS, mobile apps, and traditional software.

Validate, review, and compare DESIGN.md design-system files using the Google `@google/design.md` specification and CLI. Trigger this skill whenever the user asks to lint / validate / verify / check / review / diff a `DESIGN.md` (case-insensitive), mentions "design.md", "design system spec", "design tokens YAML", "WCAG contrast on tokens", or uses Korean phrases like "DESIGN.md 검증", "디자인 시스템 검사", "디자인 토큰 점검", "design.md 린트". Also fire when the user pastes a YAML front-matter block that looks like a design-token document (top-level `colors:` / `typography:` / `components:` keys) or when a DESIGN.md file appears in the workspace and the conversation turns to validating, comparing, or shipping it. Covers YAML front matter, markdown section order, token references, component property validity, WCAG contrast findings, missing primary colors or typography, orphaned tokens, and regressions between two DESIGN.md versions.