Product Requirements Document - AI CLI Preparation

Version: 1.0 Last Updated: 2025-10-09 Status: Phase 1 Complete, Phase 2 Specification

Executive Summary

Vision

Maximize AI coding agent performance by ensuring all necessary developer tools are installed, up-to-date, and correctly configured on development systems. Eliminate the "missing tool" friction that reduces agent effectiveness and interrupts development workflows.

Problem Statement

AI coding agents (like Claude Code) require access to a comprehensive developer toolchain to function effectively. When tools are missing, outdated, or incorrectly installed, agent performance suffers through:

Failed Operations: Agents cannot execute commands requiring missing tools
Degraded Quality: Outdated tools lack features or have known bugs
Configuration Issues: Tools installed via incorrect methods create PATH conflicts
Time Loss: Manual tool management interrupts development flow

Solution Overview

AI CLI Preparation is a specialized environment audit and installation management tool that:

Phase 1 (Complete): Detects and reports on 50+ developer tools with version checking
Phase 2 (Specified): Installs, updates, and upgrades tools with context-aware strategies
Phase 3 (Future): Automated maintenance and proactive monitoring

Success Metrics

Metric	Target	Current
Tool Coverage	50+ essential tools	50+ tools ✓
Detection Accuracy	>95% version detection	~92%
Installation Success Rate	>90% first-attempt	TBD (Phase 2)
Time to Readiness	<10 minutes for fresh system	TBD (Phase 2)
Agent Performance Gain	30-50% fewer tool-related failures	TBD (validation)

Phase 1: Detection and Auditing (Complete)

Capabilities Delivered

Tool Discovery:

50+ tools across 10 categories (runtimes, search, editors, formatters, security, etc.)
Multi-source PATH scanning (system, user, project-local bins)
Installation method classification (uv, pipx, npm, cargo, apt, rustup, nvm, etc.)

Version Detection:

Parallel execution (16 workers, configurable)
Multiple version flag detection (--version, -v, version, -V)
Intelligent parsing for diverse output formats
Resilient error handling with graceful degradation

Upstream Resolution:

Multi-source API support (GitHub, PyPI, crates.io, npm, GNU FTP)
HTTP layer with retries, exponential backoff, rate limiting
Per-origin request caps (GitHub: 5/min, PyPI: 10/min, crates.io: 5/min)
Offline-first design with committed cache (upstream_versions.json)

Output Formats:

Table view with status icons (✓ UP-TO-DATE, ↑ OUTDATED, ✗ NOT INSTALLED, ? UNKNOWN)
JSON export for programmatic consumption
Snapshot-based workflow (collect once, render many times)
Role-based filtering (agent-core, python-core, security-core, etc.)

Performance:

~10s for 50 tools online (parallel execution)
~3s offline (cache hits)
<100ms render-only mode (from snapshot)

Architecture Highlights

Threading Model:

ThreadPoolExecutor with configurable workers (default: 16)
Independent tool audits (failures isolated)

Data Model:

upstream_versions.json: Cached upstream versions (committed)
local_state.json: Machine-specific state (gitignored)

Resilience Patterns:

Network failures → fallback to cache
Version detection failures → graceful unknown status
Upstream API errors → retry with exponential backoff
Atomic file writes → prevent cache corruption

Current Limitations (Phase 1)

No Installation: Only detection and reporting
No Upgrades: Cannot update outdated tools
Manual Intervention: User must install/update tools manually
No Reconciliation: Cannot resolve installation method conflicts
Limited Guidance: Hints provided, but no automated execution

Phase 2: Installation and Upgrade Management (Specification)

Objectives

Enable automated installation, updating, and upgrading of tools with context-aware strategies that adapt to different environments (dev workstations, shared servers, CI/CD).

Target Environments

Primary: Development Workstations

Single-user systems (laptops, personal desktops)
User-level installations preferred (~/.local/bin, ~/.cargo/bin)
Flexibility and latest versions prioritized
Breaking changes acceptable (developer handles)

Secondary: Shared Development Servers

Multi-user systems (shared Linux servers, jump boxes)
System-level installations preferred (/usr/local/bin)
Coordination via advisory locks (prevent simultaneous installs)
Stability prioritized over bleeding-edge versions

Tertiary: CI/CD Environments

Ephemeral containers or VMs
Fast installation critical (cache-based)
Minimal installations (only required tools)
Reproducibility via snapshot-based installs

Core Capabilities

1. Context-Aware Installation

Environment Detection:

def detect_environment():
    # CI indicators
    if any(env in os.environ for env in ['CI', 'GITHUB_ACTIONS', 'GITLAB_CI']):
        return 'ci'

    # Multi-user indicators
    user_count = len([u for u in get_active_users() if u.login_time])
    if user_count > 3:
        return 'server'

    # Default to workstation
    return 'workstation'

Mode Behaviors:

Aspect	Workstation	Server	CI
Scope	User (~/.local)	System (/usr/local)	Minimal (project)
Prefer	Vendor tools (rustup, nvm)	System packages (apt, brew)	Cache/snapshot
Reconciliation	Parallel (keep both)	Advisory (coordinate)	Replace (clean slate)
Breaking Changes	Accept (always latest)	Warn (manual approval)	Lock (exact versions)

Configuration Override:

# .cli-audit.yml
version: 1
environment:
  mode: auto  # auto | workstation | server | ci
  # mode: workstation  # Force workstation behavior

2. Package Manager Hierarchy

Preference Order (Vendor → GitHub → System):

Vendor-Specific Tools (Highest Priority)
- Python: uv → pipx → pip
- Rust: rustup → cargo → system
- Node.js: nvm → npm → system
- Go: official installer → system
- Rationale: Better version management, user isolation, parallel installs
GitHub Releases (Medium Priority)
- Standalone binaries (fd, ripgrep, bat, delta, etc.)
- Rationale: Latest versions, no system dependency conflicts
System Package Managers (Lowest Priority)
- apt/dpkg, brew, pacman, etc.
- Rationale: Slower updates, system-wide impact, potential conflicts

Hierarchy Example:

Install Python tools:
  1. Check for uv → Use if available
  2. Fallback to pipx → Install if not present
  3. Last resort: pip → Warn about global install risks

Configuration Override:

tools:
  python:
    method: uv  # Force specific package manager
    fallback: pipx

3. Parallel Installation Approach (Reconciliation)

Philosophy: Keep multiple installations, prefer user-level via PATH ordering

Reconciliation Behavior:

Scenario: ripgrep installed both via apt (system) and cargo (user)

Current State:
  ✓ ripgrep: 14.1.1 (cargo, ~/.cargo/bin) [ACTIVE]
  ℹ️  Also found: 14.0.0 (apt, /usr/bin)

Action: No removal
  - User version takes precedence (PATH ordering)
  - System version remains (may be needed for other users/scripts)
  - User can manually remove system version if desired

PATH Management:
  ~/.cargo/bin:/usr/local/bin:/usr/bin
  ↑ User bins first → takes precedence

Aggressive Reconciliation (Optional, disabled by default):

preferences:
  reconciliation: aggressive  # Remove non-preferred installations

Benefits:

No accidental removals
Preserves system integrity
User has full control
Safe rollback (switch PATH ordering)

Trade-offs:

Disk space (multiple versions)
PATH complexity (potential confusion)
Manual cleanup required

4. Always-Latest Version Policy

Philosophy: Maximize tool performance by using latest versions; user handles breaking changes

Version Selection:

For each tool:
  1. Query upstream for latest version
  2. Compare with installed version
  3. If installed < latest:
     - Workstation: Auto-upgrade (with warning)
     - Server: Prompt for approval
     - CI: Use locked version from snapshot

Breaking Change Handling:

Major Version Upgrade Detection:

def is_major_upgrade(current: str, latest: str) -> bool:
    curr_major = int(current.split('.')[0])
    latest_major = int(latest.split('.')[0])
    return latest_major > curr_major

Warning System:

⚠️  Major version upgrade detected:
    Tool: ripgrep
    Current: 13.0.0
    Latest: 14.1.1

    Potential breaking changes:
    - Regex syntax changes
    - Flag deprecations
    - Output format changes

    Proceed? [y/N]

Configuration:

preferences:
  breaking_changes: accept  # accept | warn | reject
  auto_upgrade: true        # Auto-upgrade minor/patch versions

Rollback Strategy:

# Before upgrade, create restore point
cli_audit upgrade --create-snapshot

# After upgrade, if issues:
cli_audit rollback --from-snapshot

5. Installation Execution

Single Tool Install:

# Auto-detect best method
cli_audit install ripgrep

# Force specific method
cli_audit install ripgrep --method cargo

# Install specific version
cli_audit install ripgrep@14.0.0

Bulk Install:

# Install all missing tools
cli_audit install --missing

# Install all outdated tools
cli_audit upgrade --all

# Install from role preset
cli_audit install --preset agent-core

Script Generation:

# Generate install script (review before execution)
cli_audit install --missing --dry-run > install.sh
bash install.sh

Installation Workflow:

1. Pre-check: Verify permissions, disk space, network connectivity
2. Dependency resolution: Check for required runtimes (e.g., Python for uv)
3. Download: Fetch installers/binaries with retry logic
4. Verification: Checksum validation (if available)
5. Installation: Execute package manager or extract binary
6. Post-install: Verify version, update PATH (if needed)
7. Snapshot update: Record new installation state

6. Configuration File Format

File Location:

.cli-audit.yml (project root)
~/.config/cli-audit/config.yml (user global)
/etc/cli-audit/config.yml (system global)

Precedence: Project → User → System

Schema:

version: 1  # Schema version

environment:
  mode: auto  # auto | workstation | server | ci

tools:
  # Per-tool configuration
  python:
    version: "3.12.*"  # SemVer range
    method: uv         # Preferred package manager
    fallback: pipx

  ripgrep:
    version: "latest"  # Always latest
    method: cargo

  node:
    version: "20.*"    # Major version lock
    method: nvm

preferences:
  reconciliation: parallel     # parallel | aggressive
  breaking_changes: warn       # accept | warn | reject
  auto_upgrade: true           # Auto-upgrade minor/patch
  timeout_seconds: 5           # Network timeout
  max_workers: 16              # Parallel workers

  # Package manager hierarchy override
  package_managers:
    python: [uv, pipx, pip]
    rust: [rustup, cargo]
    node: [nvm, npm]

presets:
  # Custom role presets
  my-python-stack:
    - python
    - uv
    - black
    - ruff
    - pyright

Validation:

# Validate configuration file
cli_audit config validate

# Show effective configuration (after merging)
cli_audit config show

Implementation Phases

Phase 2.1: Foundation (Week 1-2, 8-10 days)

Deliverables:

Environment detection logic (detect_environment())
Configuration file parsing (.cli-audit.yml)
Installation method selection (package_manager_hierarchy)
Dry-run mode for all operations

Key Functions:

def detect_environment() -> str:
    """Detect environment type: workstation | server | ci"""

def load_config(path: str) -> Config:
    """Load and merge configuration files"""

def select_package_manager(tool: Tool, config: Config) -> str:
    """Choose best package manager for tool"""

def dry_run_install(tool: Tool, method: str) -> InstallPlan:
    """Generate installation plan without executing"""

Success Criteria:

Correctly detects environment in 95%+ cases
Configuration validation catches malformed YAML
Package manager selection follows hierarchy
Dry-run generates accurate install scripts

Phase 2.2: Core Installation (Week 3-4, 10-12 days)

Deliverables:

Single tool installation (cli_audit install )
Installation script execution with retries
Checksum verification (where available)
Post-install validation

Key Functions:

def install_tool(tool: Tool, method: str, version: str = "latest") -> Result:
    """Install a single tool using specified method"""

def execute_install_script(script: str, tool: Tool) -> Result:
    """Execute installation with error handling"""

def verify_checksum(file: str, expected: str) -> bool:
    """Verify downloaded file integrity"""

def post_install_validate(tool: Tool) -> bool:
    """Verify tool is accessible and version is correct"""

Installation Methods:

Python: uv install, pipx install, pip install
Rust: cargo install, rustup install
Node: nvm install, npm install -g
Go: go install
GitHub releases: wget + chmod + mv
System: apt install, brew install

Success Criteria:

90%+ first-attempt installation success rate
Checksum verification prevents corrupted installs
Post-install validation detects failures
Error messages guide user to resolution

Phase 2.3: Bulk Operations (Week 5, 5-6 days)

Deliverables:

Bulk install (--missing, --preset, --all)
Parallel installation with coordination
Progress reporting and streaming output
Atomic rollback on partial failures

Key Functions:

def install_missing(config: Config) -> BulkResult:
    """Install all missing tools"""

def install_preset(preset: str, config: Config) -> BulkResult:
    """Install tools from role preset"""

def parallel_install(tools: list[Tool], max_workers: int) -> BulkResult:
    """Install multiple tools in parallel"""

def rollback_install(snapshot: Snapshot) -> None:
    """Rollback partial installation to snapshot state"""

Progress Reporting:

Installing 15 tools...
[1/15] ✓ ripgrep (cargo) - 2.3s
[2/15] 🔄 fd (cargo) - installing...
[3/15] ⏳ bat (cargo) - queued
...

Success Criteria:

Install 10+ tools in parallel without failures
Progress reporting updates in real-time
Rollback restores system to pre-install state
Advisory locks prevent conflicts on servers

Phase 2.4: Upgrade Management (Week 6, 5-6 days)

Deliverables:

Upgrade single tool (cli_audit upgrade )
Bulk upgrade (--all, --outdated)
Breaking change detection and warnings
Rollback capability

Key Functions:

def upgrade_tool(tool: Tool, to_version: str = "latest") -> Result:
    """Upgrade tool to specified version"""

def detect_breaking_changes(tool: Tool, from_ver: str, to_ver: str) -> list[str]:
    """Detect potential breaking changes"""

def create_restore_point() -> Snapshot:
    """Create snapshot for rollback"""

def rollback_upgrade(snapshot: Snapshot, tool: Tool) -> None:
    """Rollback tool to snapshot version"""

Breaking Change Warning:

⚠️  Major version upgrade: python 3.11.5 → 3.12.1

Potential breaking changes:
  - Removed deprecated APIs (see PEP 594)
  - Changes to f-string syntax
  - Type inference improvements may expose bugs

Impact assessment:
  - 12 project dependencies may require updates
  - Estimated compatibility: 85%

Proceed with upgrade? [y/N]

Success Criteria:

Detects 90%+ major version upgrades
Breaking change warnings prevent surprises
Rollback restores tool to previous version
Upgrade preserves configuration files

Phase 2.5: Reconciliation (Week 7-8, 6-8 days)

Deliverables:

Parallel reconciliation (keep both, prefer user)
Aggressive reconciliation (remove non-preferred)
PATH management and verification
Conflict resolution guidance

Key Functions:

def reconcile_installations(tool: Tool, config: Config) -> ReconcileResult:
    """Reconcile multiple installations of same tool"""

def manage_path_ordering(preferred_bins: list[str]) -> None:
    """Ensure preferred bin directories appear first in PATH"""

def detect_conflicts(tool: Tool) -> list[Conflict]:
    """Detect version conflicts and PATH issues"""

def resolve_conflict(conflict: Conflict, strategy: str) -> None:
    """Resolve conflict using specified strategy"""

Reconciliation Output:

Reconciling ripgrep installations:

Found 2 installations:
  [1] 14.1.1 (cargo, ~/.cargo/bin) [PREFERRED]
  [2] 14.0.0 (apt, /usr/bin)

Strategy: parallel (keep both)

Actions:
  ✓ PATH ordering ensures [1] is active
  ℹ️  [2] remains available for other users
  💡 Run 'cli_audit reconcile --aggressive' to remove [2]

Current PATH:
  ~/.cargo/bin:/usr/local/bin:/usr/bin
  ↑ ripgrep 14.1.1 will be used

Success Criteria:

Correctly identifies all tool installations
PATH ordering ensures preferred version is active
Aggressive mode safely removes non-preferred versions
Conflict resolution prevents broken installations

Technical Requirements

System Requirements:

Python 3.10+ (for core runtime)
Network access (for upstream queries, can work offline with cache)
Write permissions (user: ~/.local, system: /usr/local or /opt)
100MB disk space (for cache and snapshots)

Dependencies:

Standard library only (no external Python packages for core)
Optional: requests (for improved HTTP handling vs urllib)

Performance Targets:

Single tool install: <30s (excluding download time)
Bulk install (10 tools): <5 minutes
Upgrade check: <10s (parallel upstream queries)
Environment detection: <100ms

Error Handling:

Network failures: retry with exponential backoff (max 3 attempts)
Permission errors: clear error message with sudo guidance
Disk space: pre-check before installation
Dependency failures: install dependencies automatically or guide user

Security:

Checksum verification for GitHub releases (SHA256)
HTTPS-only for upstream queries
No arbitrary code execution (package manager commands only)
User confirmation for system-level installations

Configuration Examples

Workstation (Developer Laptop):

version: 1
environment:
  mode: workstation

preferences:
  reconciliation: parallel
  breaking_changes: accept
  auto_upgrade: true

tools:
  python:
    version: "latest"
    method: uv

  node:
    version: "latest"
    method: nvm

Shared Server (Multi-User):

version: 1
environment:
  mode: server

preferences:
  reconciliation: parallel
  breaking_changes: warn
  auto_upgrade: false  # Require manual approval

tools:
  python:
    version: "3.11.*"  # Lock to 3.11.x
    method: apt

  node:
    version: "20.*"
    method: apt

CI/CD (Ephemeral):

version: 1
environment:
  mode: ci

preferences:
  reconciliation: replace
  breaking_changes: reject  # Exact versions only
  auto_upgrade: false

# Install from snapshot for reproducibility
snapshot: tools_snapshot.json

User Stories

US-1: Developer sets up new workstation

As a developer setting up a new laptop,
I want to install all necessary tools in one command,
So that I can start coding with AI agents immediately.

Acceptance:
  - Run: cli_audit install --preset agent-core
  - 20+ tools installed in <5 minutes
  - All tools accessible on PATH
  - AI agent can execute commands successfully

US-2: Developer upgrades outdated tools

As a developer with outdated tools,
I want to upgrade all tools to latest versions,
So that I benefit from performance improvements and bug fixes.

Acceptance:
  - Run: cli_audit upgrade --all
  - Breaking change warnings for major upgrades
  - User confirms or skips major upgrades
  - Minor/patch upgrades happen automatically
  - Snapshot created for rollback

US-3: Server admin maintains shared environment

As a server admin managing a multi-user dev server,
I want to upgrade tools without breaking existing user workflows,
So that all users benefit from updates without disruptions.

Acceptance:
  - Run: cli_audit upgrade --all (in server mode)
  - Major upgrades require confirmation
  - Advisory locks prevent simultaneous installs
  - Existing user installations remain functional
  - System-level tools updated in /usr/local

US-4: Developer resolves installation conflicts

As a developer with multiple Python installations,
I want to reconcile conflicting installations,
So that the correct version is used by AI agents.

Acceptance:
  - Run: cli_audit reconcile python
  - All Python installations detected
  - Preferred installation identified (uv > pipx > apt)
  - PATH ordering ensures preferred version is active
  - Option to remove non-preferred installations

US-5: CI pipeline ensures reproducible builds

As a CI pipeline,
I want to install exact tool versions from a snapshot,
So that builds are reproducible and deterministic.

Acceptance:
  - Environment detected as CI automatically
  - Snapshot loaded: tools_snapshot.json
  - Exact versions installed (no "latest")
  - Installation completes in <3 minutes (cached)
  - Tools verified before build starts

Phase 3: Future Enhancements (Vision)

Automated Maintenance

Proactive Monitoring:

Scheduled version checks (daily/weekly)
Email/Slack notifications for outdated tools
Security vulnerability alerts (integrate with CVE databases)

Auto-Upgrade Schedules:

schedules:
  python:
    check: daily
    upgrade: patch  # Auto-upgrade patch versions

  ripgrep:
    check: weekly
    upgrade: minor  # Auto-upgrade minor versions

Advanced Features

Tool Usage Analytics:

Track which tools are actually used by AI agents
Recommend removing unused tools
Suggest missing tools based on project analysis

Dependency Analysis:

Detect tool dependencies (e.g., black requires Python)
Install dependencies automatically
Warn about version incompatibilities

Multi-Project Support:

Per-project tool configurations
Project-local installations (./tools/)
Workspace-wide tool sharing

Plugin System:

Custom tool definitions (user-defined TOOLS)
External tool registries
Community-contributed tool configs

Integration Opportunities

IDE Integration:

VS Code extension for visual tool management
IntelliJ IDEA plugin
Real-time status in editor

AI Agent Integration:

Claude Code built-in tool checker
Auto-install missing tools during agent sessions
Tool readiness scoring (0-100%)

CI/CD Integration:

GitHub Action for tool auditing
GitLab CI template
Pre-commit hooks for tool version locking

Risk Assessment

High-Risk Areas

1. Breaking Changes from Upgrades

Risk: Major version upgrades break user workflows
Mitigation: Always-warn policy for major upgrades, rollback capability
Impact: High (workflow disruption)
Probability: Medium (depends on tool ecosystem)

2. Installation Failures

Risk: Network errors, permission issues, corrupted downloads
Mitigation: Retry logic, checksum verification, clear error messages
Impact: Medium (requires manual intervention)
Probability: Medium (network/permission variability)

3. PATH Conflicts

Risk: Multiple installations create confusion, wrong version used
Mitigation: Parallel reconciliation, PATH management, clear reporting
Impact: Medium (wrong tool version used)
Probability: High (common in real environments)

Medium-Risk Areas

4. Dependency Conflicts

Risk: Tool A requires Python 3.11, Tool B requires Python 3.12
Mitigation: Version range detection, conflict warnings, user resolution
Impact: Medium (manual resolution required)
Probability: Low (rare in practice)

5. Disk Space Exhaustion

Risk: Multiple installations consume excessive disk space
Mitigation: Pre-check disk space, aggressive reconciliation option
Impact: Low (easy to resolve)
Probability: Low (tools are small)

6. Security Vulnerabilities

Risk: Compromised upstream sources, MITM attacks
Mitigation: HTTPS-only, checksum verification, trusted sources
Impact: High (system compromise)
Probability: Very Low (trusted ecosystems)

Low-Risk Areas

7. Configuration Errors

Risk: Malformed .cli-audit.yml breaks operations
Mitigation: Schema validation, clear error messages
Impact: Low (easy to fix)
Probability: Low (validation catches most issues)

8. Performance Degradation

Risk: Parallel installs overwhelm system resources
Mitigation: Configurable max_workers, resource monitoring
Impact: Low (temporary slowness)
Probability: Low (16 workers is conservative)

Success Criteria (Phase 2)

Functional Requirements

✅ Must Have:

Install missing tools with single command
Upgrade outdated tools with breaking change warnings
Context-aware installation (workstation vs server)
Parallel reconciliation (keep both installations)
Configuration file support (.cli-audit.yml)
Dry-run mode for all operations
Rollback capability for upgrades

🟡 Should Have:

Bulk operations (install all missing, upgrade all outdated)
Preset-based installs (agent-core, python-core)
Progress reporting for long operations
Checksum verification for downloads

🔵 Could Have:

Aggressive reconciliation (auto-remove non-preferred)
Advisory locks for server coordination
Installation script generation
Snapshot-based CI installs

Non-Functional Requirements

Performance:

Single tool install: <30s (excluding download)
Bulk install (10 tools): <5 minutes
Environment detection: <100ms

Reliability:

90%+ first-attempt installation success rate
95%+ version detection accuracy
Zero data loss during rollbacks

Usability:

Clear error messages with resolution guidance
Consistent CLI interface
Comprehensive documentation

Maintainability:

Code coverage >80% (when tests added)
Modular architecture (separate concerns)
Clear ADRs documenting decisions

Appendices

Appendix A: Tool Categories

See TOOL_ECOSYSTEM.md for complete 50+ tool catalog.

Categories:

Runtimes (Python, Node.js, Go, Rust)
Search and Filters (ripgrep, fd, fzf, jq, yq, ast-grep)
Editors and Pagers (neovim, bat, delta)
Security (gitleaks, semgrep, bandit, trivy, osv-scanner)
Git and VCS (git, gh, glab)
Formatters and Linters (black, ruff, prettier, eslint)
HTTP and APIs (httpie, curl)
Automation (just, make)
Cloud and Infrastructure (aws-cli, kubectl, terraform, docker)
Package Managers (uv, pipx, poetry, npm, yarn, pnpm, cargo)

Appendix B: Installation Methods

Tool	Preferred Method	Fallback	System
python	uv	pipx	apt/brew
ripgrep	cargo	GitHub release	apt/brew
fd	cargo	GitHub release	apt/brew
node	nvm	-	apt/brew
rust	rustup	-	apt/brew
black	uv/pipx	pip	-
prettier	npm	-	apt/brew

Appendix C: Configuration Schema

See CONFIGURATION_SPEC.md for complete schema documentation.

Appendix D: Related Documentation

ARCHITECTURE.md - System design and implementation details
API_REFERENCE.md - Function reference and environment variables
DEVELOPER_GUIDE.md - Contributing guide
DEPLOYMENT.md - Operations and CI/CD integration
TROUBLESHOOTING.md - Common issues and solutions
adr/README.md - Architecture Decision Records

Document History:

Version	Date	Author	Changes
1.0	2025-10-09	Claude Code	Initial PRD with Phase 1 summary and Phase 2 specification

Review Status: Draft - Awaiting stakeholder review

Approvers:

Technical Lead
Product Owner
Security Review
User Representatives

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Product Requirements Document - AI CLI Preparation

Executive Summary

Vision

Problem Statement

Solution Overview

Success Metrics

Phase 1: Detection and Auditing (Complete)

Capabilities Delivered

Architecture Highlights

Current Limitations (Phase 1)

Phase 2: Installation and Upgrade Management (Specification)

Objectives

Target Environments

Core Capabilities

1. Context-Aware Installation

2. Package Manager Hierarchy

3. Parallel Installation Approach (Reconciliation)

4. Always-Latest Version Policy

5. Installation Execution

6. Configuration File Format

Implementation Phases

Phase 2.1: Foundation (Week 1-2, 8-10 days)

Phase 2.2: Core Installation (Week 3-4, 10-12 days)

Phase 2.3: Bulk Operations (Week 5, 5-6 days)

Phase 2.4: Upgrade Management (Week 6, 5-6 days)

Phase 2.5: Reconciliation (Week 7-8, 6-8 days)

Technical Requirements

Configuration Examples

User Stories

Phase 3: Future Enhancements (Vision)

Automated Maintenance

Advanced Features

Integration Opportunities

Risk Assessment

High-Risk Areas

Medium-Risk Areas

Low-Risk Areas

Success Criteria (Phase 2)

Functional Requirements

Non-Functional Requirements

Appendices

Appendix A: Tool Categories

Appendix B: Installation Methods

Appendix C: Configuration Schema

Appendix D: Related Documentation