feature-inventory

Catalogue les fonctionnalités existantes d’une base de code (points d’accès API, flux utilisateur, écrans d’interface, comportements observables) et génère des spécifications structurées avec 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

Plus de skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Créez des agents Azure AI Foundry à l’aide du SDK Python Microsoft Agent Framework (agent-framework-azure-ai). À utiliser lors de la création d’agents persistants avec AzureAIAgentsProvider, de l’utilisation d’outils hébergés (interpréteur de code, recherche de fichiers, recherche web), de l’intégration de serveurs MCP, de la gestion de fils de conversation ou de l’implémentation de réponses en streaming. Couvre les outils de fonction, les sorties structurées et les agents multi-outils.
development
airunway-aks-setup
microsoft
Configurez AI Runway sur AKS — du cluster nu au modèle en cours d'exécution. Couvre la vérification du cluster, l'installation du contrôleur, l'évaluation GPU, la configuration du fournisseur et le premier déploiement. QUAND : « configurer AI Runway », « intégrer un cluster AKS », « installer AI Runway », « configuration airunway », « déployer un modèle sur AKS », « inférence GPU sur AKS », « configuration KAITO sur AKS », « exécuter LLM sur AKS », « vLLM sur AKS », « configurer le service de modèles sur AKS », « contrôleur AI Runway ».
devops
appinsights-instrumentation
microsoft
Conseils pour instrumenter les applications web avec Azure Application Insights. Fournit des modèles de télémétrie, la configuration du SDK et des références de configuration. QUAND : comment instrumenter une application, SDK App Insights, modèles de télémétrie, qu'est-ce qu'App Insights, conseils sur Application Insights, exemples d'instrumentation, bonnes pratiques APM.
devops
applicationinsights-web-ts
microsoft
Instrumentez les applications navigateur/web avec le SDK JavaScript Application Insights (@microsoft/applicationinsights-web). Utilisez-le pour la surveillance des utilisateurs réels (RUM) — vues de page, clics, dépendances AJAX/fetch, exceptions, événements personnalisés et traces d’agents GenAI côté navigateur corrélées aux traces OpenTelemetry backend. Couvre le script de chargement du SDK et la configuration npm, les extensions de framework (React, React Native, Angular), Click Analytics, les initialiseurs de télémétrie et les conventions sémantiques OTel GenAI pour les spans d’agents/outils/modèles émises depuis le navigateur.
devops
azure-ai-anomalydetector-java
microsoft
Créez des applications de détection d'anomalies avec le SDK Azure AI Anomaly Detector pour Java. Utilisez-le lors de l'implémentation de la détection d'anomalies univariées/multivariées, de l'analyse de séries temporelles ou de la surveillance basée sur l'IA.
development
azure-ai-language-conversations-py
microsoft
Implémentez la compréhension du langage conversationnel (CLU) à l’aide du SDK Python azure-ai-language-conversations. Utilisez-le lorsque vous travaillez avec ConversationAnalysisClient pour analyser l’intention et les entités d’une conversation, créer des fonctionnalités de NLP ou intégrer la compréhension du langage dans des applications.
development
azure-ai-ml-py
microsoft
SDK v2 d’Azure Machine Learning pour Python. Utiliser pour les espaces de travail ML, les tâches, les modèles, les jeux de données, le calcul et les pipelines. Déclencheurs : « azure-ai-ml », « MLClient », « espace de travail », « registre de modèles », « tâches d’entraînement », « jeux de données ».
development