feature-inventory

Catalogs existing features from a codebase (API endpoints, user flows, UI screens, observable behaviors) and generates structured feature specs with 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

More skills from microsoft

oss-growth
microsoft
OSS growth hacker persona
agent-framework-azure-ai-py
microsoft
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
devops
azure-ai-anomalydetector-java
microsoft
Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
development
azure-ai-language-conversations-py
microsoft
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development