Skip to content

Agent Bundle Generator - Requirements Document

Executive Summary

The Agent Bundle Generator transforms natural language descriptions of desired agentic behaviors into standalone, zero-install executable agent bundles by creating specialized copies of the Amplihack framework with custom configurations, prompts, and tools tailored to specific use cases.

User Story

As a developer, I want to describe an agentic behavior in natural language so that I can instantly get a specialized, zero-install agent system that performs that specific task without manual configuration.

Functional Requirements

Core Features

FR-1: Natural Language Input Processing

  • System SHALL accept natural language prompts describing desired agentic behavior
  • System SHALL parse and extract intent from user prompts using NLP techniques:
  • Entity extraction: Identify agent types, tools, languages, frameworks
  • Intent classification: Map to predefined categories (monitoring, testing, development, etc.)
  • Confidence scoring: Assign 0-100% confidence to extracted intents
  • System SHALL handle ambiguous prompts through clarification dialogue: 
    # Example clarification flow
if confidence_score < 70:
    options = suggest_similar_intents(parsed_prompt)
    user_choice = prompt_user_selection(options)
  • System SHALL support multiple input formats:
  • CLI: amplihack bundle generate "your prompt"
  • API: POST /api/v1/bundles/generate {"prompt": "..."}
  • File: amplihack bundle generate --file requirements.txt

FR-2: Agent Generation

  • System SHALL generate specialized agent definitions with this schema: 
    # Agent definition format
name: security-scanner
type: specialized
base: analyzer # Inherits from base agent
capabilities:
  - vulnerability_detection
  - code_analysis
  - report_generation
prompts:
  system: "You are a security analysis expert..."
  user_template: "Analyze {code} for {vulnerability_types}"
tools:
  - semgrep
  - bandit
  • System SHALL create custom prompts using template substitution: 
    template = load_template(agent_type)
prompt = template.substitute(
    domain=extracted_domain,
    tools=selected_tools,
    constraints=user_constraints
)
  • System SHALL generate workflows with dependency management: 
    workflow:
  - step: scan
    agent: security-scanner
    depends_on: []
  - step: analyze
    agent: vulnerability-analyzer
    depends_on: [scan]
  - step: report
    agent: report-generator
    depends_on: [analyze]
  • System SHALL produce test suites with 80% coverage minimum

FR-3: Bundle Creation

  • System SHALL create self-contained copies of the Amplihack framework
  • System SHALL include all required dependencies and tools
  • System SHALL preserve base Amplihack capabilities while adding specializations
  • System SHALL generate documentation for the specialized bundle

FR-4: Distribution and Execution

  • System SHALL package bundles for zero-install uvx execution
  • System SHALL publish bundles to GitHub repositories
  • System SHALL enable direct execution from GitHub URLs
  • System SHALL support versioning and updates

Integration Requirements

IR-1: Claude Integration

  • System SHALL include claude-trace debugging capabilities:
# Claude-trace integration
from amplihack.utils import claude_trace

@claude_trace.trace("bundle_generation")
def generate_bundle(prompt: str) -> Bundle:
    # Automatic tracing of generation process
    pass
  • System SHALL integrate with Claude Code SDK:
# SDK integration pattern
from claude_code import Client

client = Client(
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
    timeout=30,
    max_retries=3
)
  • System SHALL maintain API key security through:
  • Environment variables: Never hardcode keys
  • Key rotation: Support automatic rotation every 90 days
  • Scoped access: Separate keys for different environments
  • Audit logging: Track all API key usage

IR-2: GitHub Integration

  • System SHALL create and manage GitHub repositories
  • System SHALL handle authentication and permissions
  • System SHALL support both public and private repositories

Non-Functional Requirements

Performance Requirements

  • NFR-1: Bundle generation performance targets:
  • Single agent bundle: < 5 seconds (p50), < 8 seconds (p95), < 12 seconds (p99)
  • Multi-agent bundle (2-5 agents): < 15 seconds (p50), < 20 seconds (p95), < 25 seconds (p99)
  • Complex bundle (>5 agents): < 30 seconds (p50), < 45 seconds (p95), < 60 seconds (p99)
  • Memory usage: < 500MB RAM increase during generation
  • Disk I/O: < 100MB temporary files
  • NFR-2: Bundle execution performance:
  • Cold start: < 10 seconds from uvx invocation to first output
  • Warm start: < 2 seconds with cached dependencies
  • Memory footprint: < 200MB base + 50MB per agent
  • CPU usage: < 25% of single core during idle
  • NFR-3: Parallel generation capacity:
  • Concurrent bundles: Support up to 10 simultaneous generations
  • Resource isolation: Each generation in separate process
  • Throughput: 100 bundles/hour on 8-core machine
  • Queue management: FIFO with priority override

Security Requirements

  • NFR-4: Secret protection mechanisms:
# Automatic secret detection and removal
SECRET_PATTERNS = [
    r'api[_-]?key["\']?\s*[:=]\s*["\']?[\w-]+',
    r'token["\']?\s*[:=]\s*["\']?[\w-]+',
    r'password["\']?\s*[:=]\s*["\']?[\w-]+'
]

def sanitize_bundle(bundle_path: Path):
    for pattern in SECRET_PATTERNS:
        scan_and_remove(bundle_path, pattern)
  • NFR-5: Code validation pipeline:
  • Static analysis: semgrep with OWASP ruleset
  • Dependency scanning: Check for CVEs in dependencies
  • AST validation: Prevent dangerous patterns (eval, exec)
  • Import restrictions: Whitelist allowed modules 
    # Validation command
semgrep --config=auto --severity=ERROR bundle/
safety check --json bundle/requirements.txt
  • NFR-6: Prompt injection prevention: 
    # Input sanitization
def sanitize_prompt(prompt: str) -> str:
    # Remove command injection attempts
    prompt = re.sub(r'[;&|`$]', '', prompt)
    # Escape special characters
    prompt = html.escape(prompt)
    # Length limit
    return prompt[:1000]

Compatibility Requirements

  • NFR-7: Bundles SHALL work cross-platform (Windows, macOS, Linux)
  • NFR-8: System SHALL support Python 3.9+
  • NFR-9: Bundles SHALL be backward compatible with base Amplihack

Quality Requirements

  • NFR-10: Generated code SHALL follow Amplihack philosophy (ruthless simplicity)
  • NFR-11: System SHALL maintain >80% test coverage
  • NFR-12: Bundles SHALL be reproducible from specifications

Acceptance Criteria

Bundle Generation

  • Accept natural language prompts via CLI and API
  • Generate complete Amplihack framework copy
  • Create specialized agents matching described behavior
  • Generate custom prompts and workflows
  • Include appropriate test suites

Distribution

  • Package bundles for uvx execution
  • Publish to GitHub repositories
  • Enable zero-install execution via uvx <github-url>
  • Support bundle versioning

Quality Assurance

  • Generated bundles pass all base Amplihack tests
  • Bundle-specific tests validate custom behavior
  • Documentation explains specialized features
  • Security scanning passes without critical issues

Use Case Validation

Successfully generate and execute bundles for:

  • Daily development environment maintenance
  • GitHub issue triage and management
  • Code review automation
  • Documentation generation
  • Security audit automation

Use Cases

Use Case 1: Development Environment Maintenance

Input:

"create an agent I can run every day to reason over my dev system and keep it up to date for development in c++, golang, and rust"

Expected Output:

  • Bundle with agents for:
  • Checking and updating compiler versions
  • Managing language toolchains
  • Updating package dependencies
  • Cleaning build artifacts
  • Optimizing IDE configurations

Execution:

uvx --from github.com/user/dev-maintenance-agent maintain

Use Case 2: GitHub Issue Triage

Input:

"create an agent that can triage all the issues in my gh repo"

Expected Output:

  • Bundle with agents for:
  • Analyzing issue content
  • Applying appropriate labels
  • Assigning priority based on impact
  • Identifying duplicate issues
  • Suggesting assignees
  • Generating triage reports

Execution:

uvx --from github.com/user/issue-triage-agent triage --repo myrepo

Use Case 3: Code Review Automation

Input:

"create an agent that reviews PRs for security vulnerabilities and code quality"

Expected Output:

  • Bundle with agents for:
  • Security vulnerability scanning
  • Code quality analysis
  • Style consistency checking
  • Test coverage validation
  • Review comment generation

Execution:

uvx --from github.com/user/code-review-agent review --pr 123

Constraints and Assumptions

Constraints

  • Must maintain backward compatibility with base Amplihack
  • Bundle size should not exceed 50MB
  • Must work within GitHub API rate limits
  • Cannot modify core Amplihack philosophy

Assumptions

  • Users have GitHub accounts
  • Users understand basic agent concepts
  • Claude API is available
  • uvx is installable on target systems
  • Internet connectivity for bundle download

Success Metrics

Quantitative Metrics

  • Bundle generation success rate > 95%
  • Zero-install execution success rate = 100%
  • Bundle generation time < 30 seconds
  • Bundle download/execution time < 10 seconds
  • Test coverage > 80%

Qualitative Metrics

  • User satisfaction with generated agents
  • Code quality of generated bundles
  • Documentation clarity and completeness
  • Community adoption rate

Risk Analysis

Technical Risks

  • Risk: Prompt ambiguity leads to incorrect agents

  • Mitigation: Implement clarification dialogue system

  • Risk: Bundle size affects performance

  • Mitigation: Use compression and selective inclusion

  • Risk: Version conflicts between components

  • Mitigation: Pin versions and test compatibility

Security Risks

  • Risk: Malicious prompt injection

  • Mitigation: Input validation and sandboxing

  • Risk: Secret exposure in bundles

  • Mitigation: Automated secret scanning

Operational Risks

  • Risk: GitHub API rate limits

  • Mitigation: Implement caching and throttling

  • Risk: Bundle distribution failures

  • Mitigation: Multiple distribution channels

Dependencies

External Dependencies

  • Amplihack framework
  • Claude Code SDK
  • claude-trace
  • uvx package manager
  • GitHub API
  • Python packaging tools

Internal Dependencies

  • Prompt parser module
  • Template engine
  • Agent generator
  • Bundle packager
  • Distribution system

Implementation Phases

Phase 1: Core Infrastructure

Dependencies: None Success Criteria: Basic bundle structure can be generated from hardcoded template

  • Bundle generator module with plugin architecture
  • YAML configuration parser with schema validation
  • Template system with Jinja2 for variable substitution
  • File system operations with atomic writes

Phase 2: Agent Generation

Dependencies: Phase 1 complete, template library available Success Criteria: Generate working agent from natural language prompt

  • Prompt parser using spaCy for NLP
  • Intent extraction with confidence scoring
  • Agent template library with 10+ base templates
  • Custom agent generation through template composition

Phase 3: Packaging & Distribution

Dependencies: Phase 2 complete, agents generate successfully Success Criteria: Bundle executes via uvx from GitHub

  • uvx packaging with pyproject.toml generation
  • GitHub API integration for repository creation
  • Zero-install execution with dependency bundling
  • Version management and update mechanisms

Phase 4: Testing & Validation

Dependencies: Phase 3 complete, end-to-end flow works Success Criteria: 80% test coverage, all security scans pass

  • Unit tests for all modules (pytest)
  • Integration tests for complete workflows
  • Security validation pipeline
  • Performance benchmarking suite
  • Documentation generation (Sphinx)

Appendices

A. Glossary

  • Agent Bundle: Self-contained package of agents, prompts, and tools
  • Zero-Install: Execution without local installation requirements
  • uvx: Universal package executor for Python applications
  • Bundle Generator: System that creates agent bundles from descriptions

B. References

  • Amplihack Documentation
  • Claude Code SDK Documentation
  • uvx Documentation
  • GitHub API Documentation

Document Version: 1.0 Last Updated: 2025-01-28 Author: Amplihack UltraThink Workflow