Surface and resolve ambiguity before the spec is written: ask targeted questions about underspecified behaviour, edge cases and decisions, and record the answers. Use between proposal and spec whenever the proposal leaves choices open.
Decide HOW the spec is met: the technical approach, the key architecture decisions and their trade-offs, with Mermaid diagrams where they clarify. Use after the spec is stable, before breaking work into tasks.
Investigate a feature or bug before committing to an approach: frame the problem, survey the relevant code, and lay out options with trade-offs. Use at the very start of a non-trivial change, when the problem or solution space is still unclear.
Turn an explored idea into a reviewable proposal: state the why, the scope (in and out), the approach, and the risks. Use once exploration converges and before writing the spec — it is the decision artifact a reviewer signs off.
Specify WHAT must be true as a delta against the current specs — testable requirements and acceptance scenarios in the OpenSpec delta format. Use after clarify; the spec, not the code, is the source of truth for behaviour. Avoid HOW (that is design).
Break the spec and design into an ordered, checkable task list — small, verifiable steps that map back to requirements. Use once spec and design are agreed, to drive (and track) implementation.
Validate the implementation against the spec: walk every requirement and acceptance scenario, run the checks, and report any gaps. Use after apply, before archiving — it is the evidence the change is actually done.
Set up VS Code (recommended extensions and profiles) to work well with this workspace. Trigger: when the user asks about extensions, VS Code profiles, or preparing the editor.
Teaches how to use this AI workspace (SDD, skills, MCP), for people new to AI. Trigger: when the user asks how to use the workspace, how to start, or what SDD is.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Gives the agent up-to-date documentation and code examples for libraries and frameworks, fetched on demand so answers are not stuck at the model's training cutoff. Runs locally from the @upstash/context7-mcp npm package.
2 1mo agoA
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copy · 100%Apache-2.0
Generate application code from a user-reviewed REASONS spec — Profile A (layered FastAPI/SQLAlchemy) or B (ES modules + SRI CDN). Trigger: materialising a signed-off spec as code.