feature-inventory

bởi microsoft

Lập danh mục các tính năng hiện có từ một mã nguồn (các điểm cuối API, luồng người dùng, màn hình giao diện, hành vi quan sát được) và tạo ra các đặc tả tính năng có cấu trúc với REQ-XXX…

npx skills add https://github.com/microsoft/github-copilot-modernization --skill feature-inventory

User Input

You MUST consider the user input before proceeding (if not empty).

The text in the task description is the feature description. Assume you always have it available. Do not ask the user to repeat it unless they provided an empty command.

Outline

Given that feature description, do this:

Output

All outputs are written under your task's Artifact path: (from task metadata).

spec.md                     ← main artifact (feature specification)
requirements-checklist.md   ← quality checklist (if applicable)

Workflow

  1. Load constitution document (if available) to understand principles and constraints.

  2. Load clarification.md if it exists ({{BASE_PATH}}/clarification.md or path from dependencyArtifacts). Treat every field that has an explicit value (i.e., resolution: user or resolution: default) as already resolved — do NOT re-ask the user about it and do NOT count it against the [NEEDS CLARIFICATION] budget below. Items listed under blocking_gaps should be treated as known risks but still do not count against the budget (they are surfaced separately by the coordinator).

  3. Read knowledge-graph.json (and, if needed, the query guidance in skills/building-java-knowledge-graph/SKILL.md) to understand the architecture and dependencies related to this feature. Also check for research files if available.

  4. Load skills/writing-feature-spec/templates/spec-template.md for required sections.

  5. Execute:

    1. Parse feature description from task If empty: ERROR "No feature description provided"
    2. Extract key concepts: actors, actions, data, constraints
    3. Assign unique requirement IDs (CRITICAL for traceability):
      • Format: REQ-XXX where XXX is a 3-digit sequence number (001, 002, ...)
      • Example: REQ-001, REQ-002, REQ-003
      • Each requirement MUST have unique ID across entire feature
    4. For unclear aspects:
      • Make informed guesses based on context and industry standards
      • Items already answered in clarification.md are NOT unclear — use the user's answer (or the recorded default) and do not mark them.
      • Only mark with [NEEDS CLARIFICATION: specific question] if:
        • The choice significantly impacts feature scope or user experience
        • Multiple reasonable interpretations exist with different implications
        • No reasonable default exists
      • LIMIT: Maximum 3 [NEEDS CLARIFICATION] markers total
      • Prioritize clarifications by impact: scope > security/privacy > user experience > technical details
    5. Fill User Scenarios & Testing section If no clear user flow: ERROR "Cannot determine user scenarios"
    6. Generate Functional Requirements Each requirement must be testable Use reasonable defaults for unspecified details (document assumptions in Assumptions section)
    7. Define Success Criteria Create measurable, technology-agnostic outcomes Include both quantitative metrics (time, performance, volume) and qualitative measures (user satisfaction, task completion) Each criterion must be verifiable without implementation details
    8. Identify Key Entities (if data involved)
    9. Return: SUCCESS (spec ready for planning)
  6. Write the specification to the assigned artifact path using the template structure, replacing placeholders with concrete details derived from the feature description while preserving section order and headings.

  7. Report completion with spec file path, checklist results, and readiness for the next phase.

Research-Driven Mode

When pre-built research files are available (provided by a design/explore phase), use this alternate workflow instead of reading source code directly:

⚠️ NO Direct Codebase Exploration

When research files exist, do NOT read project source files directly (source code, build files, config files). All codebase analysis is already in the research files.

Research File Inputs

Look for these files in the current working directory or as referenced in the task:

  • project-structure.md — functional domain list (REQUIRED)
  • tech-stack.md — technology inventory (REQUIRED)
  • data-model.md — key entities summary (optional)
  • architecture-summary.md — substitute for direct knowledge graph reading (optional)
  • Any additional *.md research files (optional)

Domain-Driven Generation

Use the functional domain list from project-structure.md to drive:

  • User Scenarios: For each domain, generate 1–3 user stories (P1/P2/P3, Given-When-Then)
  • Requirements: For each domain, generate 2–4 REQ-XXX items referencing constitution principles
  • Key Entities: Use entities from data-model.md directly (top 6–8)

Incremental Section Writing

Write the spec section by section, appending each to the assigned artifact before moving to the next:

  1. Header + Scope Baseline → create/write artifact
  2. User Scenarios & Testing → append
  3. Requirements (Functional per domain + Non-Functional) → append
  4. Success Criteria → append

Checklist Generation

After spec is complete, append a quality checklist section at the end of the artifact:

Requirements Quality Checklist

Requirement ID Coverage

  • All requirements use REQ-XXX format
  • IDs are unique and sequential

Testability

  • Every requirement is independently testable
  • Acceptance criteria are concrete (Given-When-Then)

Completeness

  • Scope Baseline section complete
  • User Scenarios prioritized (P1, P2, P3)
  • Functional requirements cover all in-scope items
  • Success criteria are measurable

Constitution Alignment

  • All constitution principles referenced
  • Migration mode constraints respected

General Guidelines

  • Focus on WHAT users need and WHY.
  • Avoid HOW to implement (no tech stack, APIs, code structure).
  • Written for business stakeholders, not developers.
  • DO NOT create any checklists that are embedded in the spec. That will be a separate command.

Section Requirements

  • Mandatory sections: Must be completed for every feature
  • Optional sections: Include only when relevant to the feature
  • When a section doesn't apply, remove it entirely (don't leave as "N/A")

For AI Generation

When creating this spec from a user prompt:

  1. Make informed guesses: Use context, industry standards, and common patterns to fill gaps
  2. Document assumptions: Record reasonable defaults in the Assumptions section
  3. Limit clarifications: Maximum 3 [NEEDS CLARIFICATION] markers - use only for critical decisions that:
    • Significantly impact feature scope or user experience
    • Have multiple reasonable interpretations with different implications
    • Lack any reasonable default
  4. Prioritize clarifications: scope > security/privacy > user experience > technical details
  5. Think like a tester: Every vague requirement should fail the "testable and unambiguous" checklist item
  6. Common areas needing clarification (only if no reasonable default exists):
    • Feature scope and boundaries (include/exclude specific use cases)
    • User types and permissions (if multiple conflicting interpretations possible)
    • Security/compliance requirements (when legally/financially significant)

Examples of reasonable defaults (don't ask about these):

  • Data retention: Industry-standard practices for the domain
  • Performance targets: Standard web/mobile app expectations unless specified
  • Error handling: User-friendly messages with appropriate fallbacks
  • Authentication method: Standard session-based or OAuth2 for web apps
  • Integration patterns: RESTful APIs unless specified otherwise

Success Criteria Guidelines

Success criteria must be:

  1. Measurable: Include specific metrics (time, percentage, count, rate)
  2. Technology-agnostic: No mention of frameworks, languages, databases, or tools
  3. User-focused: Describe outcomes from user/business perspective, not system internals
  4. Verifiable: Can be tested/validated without knowing implementation details

Good examples:

  • "Users can complete checkout in under 3 minutes"
  • "System supports 10,000 concurrent users"
  • "95% of searches return results in under 1 second"
  • "Task completion rate improves by 40%"

Bad examples (implementation-focused):

  • "API response time is under 200ms" (too technical, use "Users see results instantly")
  • "Database can handle 1000 TPS" (implementation detail, use user-facing metric)
  • "React components render efficiently" (framework-specific)
  • "Redis cache hit rate above 80%" (technology-specific)

Resources

Templates

  • templates/spec-template.md — Specification document template with all mandatory sections

Thêm skills từ microsoft

oss-growth
microsoft
Cá tính tăng trưởng OSS
agent-framework-azure-ai-py
microsoft
Xây dựng các tác nhân Azure AI Foundry bằng SDK Python của Microsoft Agent Framework (agent-framework-azure-ai). Sử dụng khi tạo các tác nhân bền vững với AzureAIAgentsProvider, sử dụng các công cụ được lưu trữ (trình thông dịch mã, tìm kiếm tệp, tìm kiếm web), tích hợp máy chủ MCP, quản lý chuỗi hội thoại hoặc triển khai phản hồi phát trực tuyến. Bao gồm các công cụ hàm, đầu ra có cấu trúc và các tác nhân đa công cụ.
development
airunway-aks-setup
microsoft
Thiết lập AI Runway trên AKS — từ cụm trống đến mô hình đang chạy. Bao gồm xác minh cụm, cài đặt controller, đánh giá GPU, thiết lập nhà cung cấp và triển khai đầu tiên. KHI NÀO: "thiết lập AI Runway", "onboard cụm AKS", "cài đặt AI Runway", "thiết lập airunway", "triển khai mô hình lên AKS", "suy luận GPU trên AKS", "thiết lập KAITO trên AKS", "chạy LLM trên AKS", "vLLM trên AKS", "thiết lập phục vụ mô hình trên AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Hướng dẫn để instrument các ứng dụng web với Azure Application Insights. Cung cấp các mẫu telemetry, thiết lập SDK, và tài liệu tham khảo cấu hình. KHI NÀO: cách instrument ứng dụng, App Insights SDK, các mẫu telemetry, App Insights là gì, hướng dẫn Application Insights, ví dụ instrumentation, các phương pháp tốt nhất APM.
devops
applicationinsights-web-ts
microsoft
Instrument các ứng dụng trình duyệt/web bằng SDK JavaScript Application Insights (@microsoft/applicationinsights-web). Dùng cho Real User Monitoring (RUM) — lượt xem trang, nhấp chuột, phụ thuộc AJAX/fetch, ngoại lệ, sự kiện tùy chỉnh và dấu vết tác nhân GenAI phía trình duyệt tương quan với dấu vết OpenTelemetry phía backend. Bao gồm thiết lập SDK Loader Script và npm, tiện ích mở rộng framework (React, React Native, Angular), Click Analytics, trình khởi tạo telemetry và quy ước ngữ nghĩa OTel GenAI cho các span tác nhân/công cụ/mô hình phát ra từ trình duyệt.
devops
azure-ai-anomalydetector-java
microsoft
Xây dựng ứng dụng phát hiện bất thường với Azure AI Anomaly Detector SDK cho Java. Sử dụng khi triển khai phát hiện bất thường đơn biến/đa biến, phân tích chuỗi thời gian hoặc giám sát hỗ trợ AI.
development
azure-ai-language-conversations-py
microsoft
Triển khai Conversational Language Understanding (CLU) bằng SDK Python azure-ai-language-conversations. Sử dụng khi làm việc với ConversationAnalysisClient để phân tích ý định và thực thể trong hội thoại, xây dựng tính năng NLP, hoặc tích hợp hiểu ngôn ngữ vào ứng dụng.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 cho Python. Dùng cho không gian làm việc ML, công việc, mô hình, tập dữ liệu, tính toán và quy trình. Kích hoạt: "azure-ai-ml", "MLClient", "không gian làm việc", "đăng ký mô hình", "công việc đào tạo", "tập dữ liệu".
development