ldy26098
/
BMAD-METHOD
mirror of https://github.com/bmad-code-org/BMAD-METHOD.git
1
0
Fork 0
Code Packages Projects Releases Wiki Activity
BMAD-METHOD/bmad-core/tasks/story-analysis.md

5.3 KiB
Raw Blame History

Story Analysis Task

Purpose

Perform comprehensive analysis of a story before implementation, including codebase search, dependency analysis, and complexity assessment.

Process

Section 1: Pre-Analysis Setup

  1. Check for User Override:

    • If the user has explicitly requested "Review Mode" (e.g., "Please use review mode for this story"), skip complexity assessment and proceed directly to review mode.

  2. Establish Temporal Context:

    • Check checkCurrentDate setting in core-config.yml. If true, run get-Date -Format "yyyy-MM-dd" and store the current year. Announce the year being used for context-setting (e.g., "Operating with standards context for the year 2024."). If false, skip this step as current date is assumed to be available in system context.

  3. Codebase Semantic Search:

    • Perform a semantic search across the codebase for patterns, functions, or components related to the story's requirements.
    • Focus on finding existing implementations that might be relevant, duplicated, or need to be extended.
    • Log findings for later reference during implementation.

Section 2: Dependency & Version Analysis

  1. Dependency & Standards Analysis with Version-Based Criticality Assessment:
    • Read the package.json (or equivalent) to identify currently installed libraries relevant to the story.
    • Version Criticality Analysis:
      • Compare dependency versions against model training cutoff (typically 2024 for current models)
      • Flag packages with versions newer than training data or using "latest"/"^" specifiers
      • Identify completely new packages not in model's training data
    • [[LLM: Attempt Internet Search: If critical versions detected OR for general best practices research, perform a targeted internet search for "best practices for [library/feature] in [current year]".]]
    • [[LLM: On Success: Silently incorporate the findings and proceed to the next step.]]
    • [[LLM: On Failure with Critical Versions Detected:
      1. Announce the critical situation: "I detected [library] version [X.Y.Z] which is newer than my training data (cutoff: [date]). Without MCP search tools, I cannot safely implement features using this version."
      2. Explain the risk: "Proceeding without current knowledge could result in deprecated patterns, security vulnerabilities, or broken implementations."
      3. Request explicit decision: "Please enable MCP search tools or confirm you want me to proceed with potentially outdated patterns. Type 'proceed-anyway' to continue at your own risk."
      4. HALT and await user response. Only continue after explicit user confirmation.]]
    • [[LLM: On Failure with Non-Critical Research:
      1. Announce the situation: "I am currently unable to access the internet to research the latest standards for this task."
      2. Offer a choice: "This appears to be non-critical research. I can proceed using best practices from my existing training data."
      3. Request confirmation: "Please let me know if you'd like me to continue or if you prefer to enable MCP search tools first."
      4. HALT and await user response. Continue based on user preference.]]

Section 3: Complexity Assessment

  1. Initial Complexity Assessment & Mode Declaration:
    • Calculate a "Story Complexity" score using Fibonacci scale (1, 2, 3, 5, 8, 13).
    • If Review Mode was forced by the user OR if Story Complexity > agentThresholdStory, declare: "Entering high-scrutiny 'Review Mode' for this story. Each task will be individually assessed."
    • Otherwise, declare: "Story complexity is within standard limits. Each task will still be individually assessed for complexity."
    • Log the complexity score (if calculated) and the reason for the mode in the story's Dev Notes.

Fibonacci Complexity Guidelines:

  • 1: Trivial changes (typos, simple styling)
  • 2: Minor feature additions (new button, basic validation)
  • 3: Standard feature implementation (form handling, API calls)
  • 5: Complex features (authentication, data processing)
  • 8: Major architectural changes (new services, database schema)
  • 13: High-risk/high-uncertainty work (new frameworks, complex integrations)

Version Analysis Guidelines

Critical Version Indicators (Require MCP search):

  • Package versions released after model training cutoff
  • "latest", "^", or "~" version specifiers for major dependencies
  • Completely new packages/frameworks not in training data
  • Major version bumps (e.g., React 17→19, Node 16→20)

Non-Critical Research (Optional MCP search):

  • Established libraries within training data timeframe
  • Minor version updates with backward compatibility
  • General coding patterns and best practices
  • Documentation and styling techniques

Output

Create analysis artifacts in the story's Dev Notes section:

  • Semantic search findings
  • Dependency analysis results
  • Complexity assessment and mode declaration
  • Any critical version warnings or research limitations

Dependencies

  • Access to core-config.yml for configuration settings
  • Codebase search capabilities
  • Package management file access (package.json, etc.)
  • Optional: MCP search tools for dependency research