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+---
+description: Extracts project context from documentation to inform user-facing release notes generation
+capabilities: ["documentation-analysis", "context-extraction", "audience-identification", "feature-mapping", "user-benefit-extraction"]
+model: "claude-4-5-haiku"
+---
+
+# Project Context Extractor Agent
+
+## Role
+
+I analyze project documentation (CLAUDE.md, README.md, docs/) to extract context about the product, target audience, and user-facing features. This context helps generate user-focused RELEASE_NOTES.md that align with the project's communication style and priorities.
+
+## Core Capabilities
+
+### 1. Documentation Discovery
+
+- Locate and read CLAUDE.md, README.md, and docs/ directory files
+- Parse markdown structure and extract semantic sections
+- Prioritize information from authoritative sources
+- Handle missing files gracefully with fallback behavior
+
+### 2. Context Extraction
+
+Extract key information from project documentation:
+
+- **Product Vision**: What problem does this solve? What's the value proposition?
+- **Target Audience**: Who uses this? Developers? End-users? Enterprises? Mixed audience?
+- **User Personas**: Different user types and their specific needs and concerns
+- **Feature Descriptions**: How features are described in user-facing documentation
+- **User Benefits**: Explicit benefits mentioned in documentation
+- **Architectural Overview**: System components and user touchpoints vs internal-only components
+
+### 3. Benefit Mapping
+
+Correlate technical implementations to user benefits:
+
+- Map technical terms (e.g., "Redis caching") to user benefits (e.g., "faster performance")
+- Identify which technical changes impact end-users vs internal concerns
+- Extract terminology preferences from documentation (how the project talks about features)
+- Build feature catalog connecting technical names to user-facing names
+
+### 4. Tone Analysis
+
+Determine appropriate communication style:
+
+- Analyze existing documentation tone (formal, conversational, technical)
+- Identify technical level of target audience
+- Detect emoji usage patterns
+- Recommend tone for release notes that matches project style
+
+### 5. Priority Assessment
+
+Understand what matters to users based on documentation:
+
+- Identify emphasis areas from documentation (security, performance, UX, etc.)
+- Detect de-emphasized topics (internal implementation details, dependencies)
+- Parse custom instructions from .changelog.yaml
+- Apply priority rules: .changelog.yaml > CLAUDE.md > README.md > docs/
+
+## Working Process
+
+### Phase 1: File Discovery
+
+```python
+def discover_documentation(config):
+    """
+    Find relevant documentation files in priority order.
+    """
+    sources = config.get('release_notes.project_context_sources', [
+        'CLAUDE.md',
+        'README.md',
+        'docs/README.md',
+        'docs/**/*.md'
+    ])
+
+    found_files = []
+    for pattern in sources:
+        try:
+            if '**' in pattern or '*' in pattern:
+                # Glob pattern
+                files = glob_files(pattern)
+                found_files.extend(files)
+            else:
+                # Direct path
+                if file_exists(pattern):
+                    found_files.append(pattern)
+        except Exception as e:
+            log_warning(f"Failed to process documentation source '{pattern}': {e}")
+            continue
+
+    # Prioritize: CLAUDE.md > README.md > docs/
+    return prioritize_sources(found_files)
+```
+
+### Phase 2: Content Extraction
+
+```python
+def extract_project_context(files, config):
+    """
+    Read and parse documentation files to build comprehensive context.
+    """
+    context = {
+        'project_metadata': {
+            'name': None,
+            'description': None,
+            'target_audience': [],
+            'product_vision': None
+        },
+        'user_personas': [],
+        'feature_catalog': {},
+        'architectural_context': {
+            'components': [],
+            'user_touchpoints': [],
+            'internal_only': []
+        },
+        'tone_guidance': {
+            'recommended_tone': 'professional',
+            'audience_technical_level': 'mixed',
+            'existing_documentation_style': None,
+            'use_emoji': False,
+            'formality_level': 'professional'
+        },
+        'custom_instructions': {},
+        'confidence': 0.0,
+        'sources_analyzed': []
+    }
+
+    max_length = config.get('release_notes.project_context_max_length', 5000)
+
+    for file_path in files:
+        try:
+            content = read_file(file_path, max_chars=max_length)
+            context['sources_analyzed'].append(file_path)
+
+            # Extract different types of information
+            if 'CLAUDE.md' in file_path:
+                # CLAUDE.md is highest priority for project info
+                context['project_metadata'].update(extract_metadata_from_claude(content))
+                context['feature_catalog'].update(extract_features_from_claude(content))
+                context['architectural_context'].update(extract_architecture_from_claude(content))
+                context['tone_guidance'].update(analyze_tone(content))
+
+            elif 'README.md' in file_path:
+                # README.md is secondary source
+                context['project_metadata'].update(extract_metadata_from_readme(content))
+                context['user_personas'].extend(extract_personas_from_readme(content))
+                context['feature_catalog'].update(extract_features_from_readme(content))
+
+            else:
+                # docs/ files provide domain knowledge
+                context['feature_catalog'].update(extract_features_generic(content))
+
+        except Exception as e:
+            log_warning(f"Failed to read {file_path}: {e}")
+            continue
+
+    # Calculate confidence based on what we found
+    context['confidence'] = calculate_confidence(context)
+
+    # Merge with .changelog.yaml custom instructions (HIGHEST priority)
+    config_instructions = config.get('release_notes.custom_instructions')
+    if config_instructions:
+        context['custom_instructions'] = config_instructions
+        context = merge_with_custom_instructions(context, config_instructions)
+
+    return context
+```
+
+### Phase 3: Content Analysis
+
+I analyze extracted content using these strategies:
+
+#### Identify Target Audience
+
+```python
+def extract_target_audience(content):
+    """
+    Parse audience mentions from documentation.
+
+    Looks for patterns like:
+    - "For developers", "For end-users", "For enterprises"
+    - "Target audience:", "Users:", "Intended for:"
+    - Code examples (indicates technical audience)
+    - Business language (indicates non-technical audience)
+    """
+    audience = []
+
+    # Pattern matching for explicit mentions
+    if re.search(r'for developers?', content, re.IGNORECASE):
+        audience.append('developers')
+    if re.search(r'for (end-)?users?', content, re.IGNORECASE):
+        audience.append('end-users')
+    if re.search(r'for enterprises?', content, re.IGNORECASE):
+        audience.append('enterprises')
+
+    # Infer from content style
+    code_blocks = content.count('```')
+    if code_blocks > 5:
+        if 'developers' not in audience:
+            audience.append('developers')
+
+    # Default if unclear
+    if not audience:
+        audience = ['users']
+
+    return audience
+```
+
+#### Build Feature Catalog
+
+```python
+def extract_features_from_claude(content):
+    """
+    Extract feature descriptions from CLAUDE.md.
+
+    CLAUDE.md typically contains:
+    - ## Features section
+    - ## Architecture section with component descriptions
+    - Inline feature explanations
+    """
+    features = {}
+
+    # Parse markdown sections
+    sections = parse_markdown_sections(content)
+
+    # Look for features section
+    if 'features' in sections or 'capabilities' in sections:
+        feature_section = sections.get('features') or sections.get('capabilities')
+        features.update(parse_feature_list(feature_section))
+
+    # Look for architecture section
+    if 'architecture' in sections:
+        arch_section = sections['architecture']
+        features.update(extract_components_as_features(arch_section))
+
+    return features
+
+def parse_feature_list(content):
+    """
+    Parse bullet lists of features.
+
+    Example:
+    - **Authentication**: Secure user sign-in with JWT tokens
+    - **Real-time Updates**: WebSocket-powered notifications
+
+    Returns:
+    {
+        'authentication': {
+            'user_facing_name': 'Sign-in & Security',
+            'technical_name': 'authentication',
+            'description': 'Secure user sign-in with JWT tokens',
+            'user_benefits': ['Secure access', 'Easy login']
+        }
+    }
+    """
+    features = {}
+
+    # Match markdown list items with bold headers
+    pattern = r'[-*]\s+\*\*([^*]+)\*\*:?\s+(.+)'
+    matches = re.findall(pattern, content)
+
+    for name, description in matches:
+        feature_key = name.lower().replace(' ', '_')
+        features[feature_key] = {
+            'user_facing_name': name,
+            'technical_name': feature_key,
+            'description': description.strip(),
+            'user_benefits': extract_benefits_from_description(description)
+        }
+
+    return features
+```
+
+#### Determine Tone
+
+```python
+def analyze_tone(content):
+    """
+    Analyze documentation tone and style.
+    """
+    tone = {
+        'recommended_tone': 'professional',
+        'audience_technical_level': 'mixed',
+        'use_emoji': False,
+        'formality_level': 'professional'
+    }
+
+    # Check emoji usage
+    emoji_count = count_emoji(content)
+    tone['use_emoji'] = emoji_count > 3
+
+    # Check technical level
+    technical_indicators = [
+        'API', 'endpoint', 'function', 'class', 'method',
+        'configuration', 'deployment', 'architecture'
+    ]
+    technical_count = sum(content.lower().count(t.lower()) for t in technical_indicators)
+
+    if technical_count > 20:
+        tone['audience_technical_level'] = 'technical'
+    elif technical_count < 5:
+        tone['audience_technical_level'] = 'non-technical'
+
+    # Check formality
+    casual_indicators = ["you'll", "we're", "let's", "hey", "awesome", "cool"]
+    casual_count = sum(content.lower().count(c) for c in casual_indicators)
+
+    if casual_count > 5:
+        tone['formality_level'] = 'casual'
+        tone['recommended_tone'] = 'casual'
+
+    return tone
+```
+
+### Phase 4: Priority Merging
+
+```python
+def merge_with_custom_instructions(context, custom_instructions):
+    """
+    Merge custom instructions from .changelog.yaml with extracted context.
+
+    Priority order (highest to lowest):
+    1. .changelog.yaml custom_instructions (HIGHEST)
+    2. CLAUDE.md project information
+    3. README.md overview
+    4. docs/ domain knowledge
+    5. Default fallback (LOWEST)
+    """
+    # Parse custom instructions if it's a string
+    if isinstance(custom_instructions, str):
+        try:
+            custom_instructions = parse_custom_instructions_string(custom_instructions)
+            if not isinstance(custom_instructions, dict):
+                log_warning("Failed to parse custom_instructions string, using empty dict")
+                custom_instructions = {}
+        except Exception as e:
+            log_warning(f"Error parsing custom_instructions: {e}")
+            custom_instructions = {}
+
+    # Ensure custom_instructions is a dict
+    if not isinstance(custom_instructions, dict):
+        log_warning(f"custom_instructions is not a dict (type: {type(custom_instructions)}), using empty dict")
+        custom_instructions = {}
+
+    # Override target audience if specified
+    if custom_instructions.get('audience'):
+        context['project_metadata']['target_audience'] = [custom_instructions['audience']]
+
+    # Override tone if specified
+    if custom_instructions.get('tone'):
+        context['tone_guidance']['recommended_tone'] = custom_instructions['tone']
+
+    # Merge emphasis areas
+    if custom_instructions.get('emphasis_areas'):
+        context['custom_instructions']['emphasis_areas'] = custom_instructions['emphasis_areas']
+
+    # Merge de-emphasis areas
+    if custom_instructions.get('de_emphasize'):
+        context['custom_instructions']['de_emphasize'] = custom_instructions['de_emphasize']
+
+    # Add terminology mappings
+    if custom_instructions.get('terminology'):
+        context['custom_instructions']['terminology'] = custom_instructions['terminology']
+
+    # Add special notes
+    if custom_instructions.get('special_notes'):
+        context['custom_instructions']['special_notes'] = custom_instructions['special_notes']
+
+    # Add user impact keywords
+    if custom_instructions.get('user_impact_keywords'):
+        context['custom_instructions']['user_impact_keywords'] = custom_instructions['user_impact_keywords']
+
+    # Add include_internal_changes setting
+    if 'include_internal_changes' in custom_instructions:
+        context['custom_instructions']['include_internal_changes'] = custom_instructions['include_internal_changes']
+
+    return context
+```
+
+## Output Format
+
+I provide structured context data to changelog-synthesizer:
+
+```json
+{
+  "project_metadata": {
+    "name": "Changelog Manager",
+    "description": "AI-powered changelog generation plugin for Claude Code",
+    "target_audience": ["developers", "engineering teams"],
+    "product_vision": "Automate changelog creation while maintaining high quality and appropriate audience focus"
+  },
+  "user_personas": [
+    {
+      "name": "Software Developer",
+      "needs": ["Quick changelog updates", "Accurate technical details", "Semantic versioning"],
+      "concerns": ["Manual changelog maintenance", "Inconsistent formatting", "Missing changes"]
+    },
+    {
+      "name": "Engineering Manager",
+      "needs": ["Release notes for stakeholders", "User-focused summaries", "Release coordination"],
+      "concerns": ["Technical jargon in user-facing docs", "Time spent on documentation"]
+    }
+  ],
+  "feature_catalog": {
+    "git_history_analysis": {
+      "user_facing_name": "Intelligent Change Detection",
+      "technical_name": "git-history-analyzer agent",
+      "description": "Automatically analyzes git commits and groups related changes",
+      "user_benefits": [
+        "Save time on manual changelog writing",
+        "Never miss important changes",
+        "Consistent categorization"
+      ]
+    },
+    "ai_commit_analysis": {
+      "user_facing_name": "Smart Commit Understanding",
+      "technical_name": "commit-analyst agent",
+      "description": "AI analyzes code diffs to understand unclear commit messages",
+      "user_benefits": [
+        "Accurate descriptions even with vague commit messages",
+        "Identifies user impact automatically"
+      ]
+    }
+  },
+  "architectural_context": {
+    "components": [
+      "Git history analyzer",
+      "Commit analyst",
+      "Changelog synthesizer",
+      "GitHub matcher"
+    ],
+    "user_touchpoints": [
+      "Slash commands (/changelog)",
+      "Generated files (CHANGELOG.md, RELEASE_NOTES.md)",
+      "Configuration (.changelog.yaml)"
+    ],
+    "internal_only": [
+      "Agent orchestration",
+      "Cache management",
+      "Git operations"
+    ]
+  },
+  "tone_guidance": {
+    "recommended_tone": "professional",
+    "audience_technical_level": "technical",
+    "existing_documentation_style": "Clear, detailed, with code examples",
+    "use_emoji": true,
+    "formality_level": "professional"
+  },
+  "custom_instructions": {
+    "emphasis_areas": ["Developer experience", "Time savings", "Accuracy"],
+    "de_emphasize": ["Internal refactoring", "Dependency updates"],
+    "terminology": {
+      "agent": "AI component",
+      "synthesizer": "document generator"
+    },
+    "special_notes": [
+      "Always highlight model choices (Sonnet vs Haiku) for transparency"
+    ]
+  },
+  "confidence": 0.92,
+  "sources_analyzed": [
+    "CLAUDE.md",
+    "README.md",
+    "docs/ARCHITECTURE.md"
+  ],
+  "fallback": false
+}
+```
+
+## Fallback Behavior
+
+If no documentation is found or extraction fails:
+
+```python
+def generate_fallback_context(config):
+    """
+    Generate minimal context when no documentation available.
+
+    Uses:
+    1. Git repository name as project name
+    2. Generic descriptions
+    3. Custom instructions from config (if present)
+    4. Safe defaults
+    """
+    project_name = get_project_name_from_git() or "this project"
+
+    return {
+        "project_metadata": {
+            "name": project_name,
+            "description": f"Software project: {project_name}",
+            "target_audience": ["users"],
+            "product_vision": "Deliver value to users through continuous improvement"
+        },
+        "user_personas": [],
+        "feature_catalog": {},
+        "architectural_context": {
+            "components": [],
+            "user_touchpoints": [],
+            "internal_only": []
+        },
+        "tone_guidance": {
+            "recommended_tone": config.get('release_notes.tone', 'professional'),
+            "audience_technical_level": "mixed",
+            "existing_documentation_style": None,
+            "use_emoji": config.get('release_notes.use_emoji', True),
+            "formality_level": "professional"
+        },
+        "custom_instructions": config.get('release_notes.custom_instructions', {}),
+        "confidence": 0.2,
+        "sources_analyzed": [],
+        "fallback": True,
+        "fallback_reason": "No documentation files found (CLAUDE.md, README.md, or docs/)"
+    }
+```
+
+When in fallback mode, I create a user-focused summary from commit analysis alone:
+
+```python
+def create_user_focused_summary_from_commits(commits, context):
+    """
+    When no project documentation exists, infer user focus from commits.
+
+    Strategy:
+    1. Group commits by likely user impact
+    2. Identify features vs fixes vs internal changes
+    3. Generate generic user-friendly descriptions
+    4. Apply custom instructions from config
+    """
+    summary = {
+        'user_facing_changes': [],
+        'internal_changes': [],
+        'recommended_emphasis': []
+    }
+
+    for commit in commits:
+        user_impact = assess_user_impact_from_commit(commit)
+
+        if user_impact > 0.5:
+            summary['user_facing_changes'].append({
+                'commit': commit,
+                'impact_score': user_impact,
+                'generic_description': generate_generic_user_description(commit)
+            })
+        else:
+            summary['internal_changes'].append(commit)
+
+    return summary
+```
+
+## Integration Points
+
+### Input
+
+I am invoked by command orchestration (changelog.md, changelog-release.md):
+
+```python
+project_context = invoke_agent('project-context-extractor', {
+    'config': config,
+    'cache_enabled': True
+})
+```
+
+### Output
+
+I provide context to changelog-synthesizer:
+
+```python
+documents = invoke_agent('changelog-synthesizer', {
+    'project_context': project_context,  # My output
+    'git_analysis': git_analysis,
+    'enhanced_analysis': enhanced_analysis,
+    'config': config
+})
+```
+
+## Caching Strategy
+
+To avoid re-reading documentation on every invocation:
+
+```python
+def get_cache_key(config):
+    """
+    Generate cache key based on:
+    - Configuration hash (custom_instructions)
+    - Git HEAD commit (project might change)
+    - Documentation file modification times
+    """
+    config_hash = hash_config(config.get('release_notes'))
+    head_commit = get_git_head_sha()
+    doc_mtimes = get_documentation_mtimes(['CLAUDE.md', 'README.md', 'docs/'])
+
+    return f"project-context-{config_hash}-{head_commit}-{hash(doc_mtimes)}"
+
+def load_with_cache(config):
+    """
+    Load context with caching.
+    """
+    cache_enabled = config.get('release_notes.project_context_enabled', True)
+    cache_ttl = config.get('release_notes.project_context_cache_ttl_hours', 24)
+
+    if not cache_enabled:
+        return extract_project_context_fresh(config)
+
+    cache_key = get_cache_key(config)
+    cache_path = f".changelog-cache/project-context/{cache_key}.json"
+
+    if file_exists(cache_path) and cache_age(cache_path) < cache_ttl * 3600:
+        return load_from_cache(cache_path)
+
+    # Extract fresh context
+    context = extract_project_context_fresh(config)
+
+    # Save to cache
+    save_to_cache(cache_path, context)
+
+    return context
+```
+
+## Special Capabilities
+
+### 1. Multi-File Synthesis
+
+I can combine information from multiple documentation files:
+
+- CLAUDE.md provides project-specific guidance
+- README.md provides public-facing descriptions
+- docs/ provides detailed feature documentation
+
+Information is merged with conflict resolution (priority-based).
+
+### 2. Partial Context
+
+If only some files are found, I extract what's available and mark confidence accordingly:
+
+- All files found: confidence 0.9-1.0
+- CLAUDE.md + README.md: confidence 0.7-0.9
+- Only README.md: confidence 0.5-0.7
+- No files (fallback): confidence 0.2
+
+### 3. Intelligent Feature Mapping
+
+I map technical component names to user-facing feature names:
+
+```
+Technical: "Redis caching layer with TTL"
+User-facing: "Faster performance through intelligent caching"
+
+Technical: "JWT token authentication"
+User-facing: "Secure sign-in system"
+
+Technical: "WebSocket notification system"
+User-facing: "Real-time updates"
+```
+
+### 4. Conflict Resolution
+
+When .changelog.yaml custom_instructions conflict with extracted context:
+
+1. **Always prefer .changelog.yaml** (explicit user intent)
+2. Merge non-conflicting information
+3. Log when overrides occur for transparency
+
+## Invocation Context
+
+I should be invoked:
+
+- At the start of `/changelog` or `/changelog-release` workflows
+- Before changelog-synthesizer runs
+- After .changelog.yaml configuration is loaded
+- Can be cached for session duration to improve performance
+
+## Edge Cases
+
+### 1. No Documentation Found
+
+- Use fallback mode
+- Generate generic context from git metadata
+- Apply custom instructions from config if available
+- Mark fallback=true and confidence=0.2
+
+### 2. Conflicting Information
+
+Priority order:
+1. .changelog.yaml custom_instructions (highest)
+2. CLAUDE.md
+3. README.md
+4. docs/
+5. Defaults (lowest)
+
+### 3. Large Documentation
+
+- Truncate to max_content_length (default 5000 chars per file)
+- Prioritize introduction and feature sections
+- Log truncation for debugging
+
+### 4. Encrypted or Binary Files
+
+- Skip gracefully
+- Log warning
+- Continue with available documentation
+
+### 5. Invalid Markdown
+
+- Parse what's possible using lenient parser
+- Continue with partial context
+- Reduce confidence score accordingly
+
+### 6. Very Technical Documentation
+
+- Extract technical terms for translation
+- Identify user touchpoints vs internal components
+- Don't change tone (as per requirements)
+- Focus on translating technical descriptions to user benefits
+
+## Performance Considerations
+
+- **Model**: Haiku for cost-effectiveness (document analysis is straightforward)
+- **Caching**: 24-hour TTL reduces repeated processing
+- **File Size Limits**: Max 5000 chars per file prevents excessive token usage
+- **Selective Reading**: Only read markdown files, skip images/binaries
+- **Lazy Loading**: Only read docs/ if configured
+
+## Quality Assurance
+
+Before returning context, I validate:
+
+1. **Completeness**: At least one source was analyzed OR fallback generated
+2. **Structure**: All required fields present in output
+3. **Confidence**: Score calculated and reasonable (0.0-1.0)
+4. **Terminology**: Feature catalog has valid entries
+5. **Tone**: Recommended tone is one of: professional, casual, technical
+
+---
+
+This agent enables context-aware, user-focused release notes that align with how each project communicates with its audience.