Content Generation Pipelines Specification
Overview
Content generation pipelines automate the creation of high-quality technical content including documentation, tutorials, blog posts, and changelogs. These pipelines leverage AI capabilities to produce consistent, accurate, and engaging content while maintaining brand voice and technical precision.
Pipeline Categories
1. Blog Generation Pipeline
Purpose
Generate engaging technical blog posts from various sources including code changes, documentation updates, or topic outlines while maintaining consistent voice and SEO optimization.
Configuration Structure
name: blog_generation_pipeline
version: "2.0"
type: content_generation
description: "AI-powered technical blog post generation with SEO optimization"
metadata:
category: content
sub_category: blog
content_types: ["technical", "tutorial", "announcement", "deep-dive"]
seo_enabled: true
inputs:
topic:
type: string
required: true
description: "Blog post topic or title"
validation:
min_length: 10
max_length: 200
content_type:
type: string
enum: ["technical", "tutorial", "announcement", "deep-dive", "comparison"]
default: "technical"
description: "Type of blog post to generate"
source_material:
type: object
properties:
code_changes:
type: array
items:
type: string
description: "Paths to relevant code changes"
documentation:
type: array
items:
type: string
description: "Documentation files to reference"
research_links:
type: array
items:
type: string
description: "External research sources"
target_audience:
type: string
enum: ["beginner", "intermediate", "advanced", "mixed"]
default: "intermediate"
brand_voice:
type: object
properties:
tone:
type: string
enum: ["professional", "conversational", "educational", "inspiring"]
default: "professional"
personality_traits:
type: array
items:
type: string
default: ["knowledgeable", "helpful", "clear"]
steps:
- name: research_topic
type: research_aggregator
inputs:
topic: "{{ topic }}"
sources: "{{ source_material }}"
config:
research_depth: "comprehensive"
include_code_analysis: true
extract_key_concepts: true
identify_related_topics: true
outputs:
- research_summary
- key_concepts
- code_examples
- related_topics
- name: analyze_audience_needs
type: audience_analyzer
inputs:
topic: "{{ topic }}"
audience: "{{ target_audience }}"
content_type: "{{ content_type }}"
config:
consider_factors:
- technical_level
- prior_knowledge
- learning_objectives
- pain_points
outputs:
- audience_profile
- content_requirements
- name: generate_outline
type: llm_outliner
inputs:
topic: "{{ topic }}"
research: "{{ research_summary }}"
audience: "{{ audience_profile }}"
content_type: "{{ content_type }}"
prompt: |
Create a detailed blog post outline for:
Topic: {{ topic }}
Type: {{ content_type }}
Audience: {{ target_audience }} level
Research summary:
{{ research_summary }}
Key concepts to cover:
{{ key_concepts | to_yaml }}
Generate an outline with:
1. Compelling introduction hook
2. Logical flow of main sections (3-5)
3. Code examples placement
4. Visual/diagram opportunities
5. Actionable takeaways
6. Strong conclusion with CTA
Ensure the outline matches {{ content_type }} style.
outputs:
- blog_outline
- section_purposes
- name: generate_content_sections
type: parallel_content_generator
inputs:
outline: "{{ blog_outline }}"
research: "{{ research_summary }}"
code_examples: "{{ code_examples }}"
voice: "{{ brand_voice }}"
config:
parallel_sections: true
maintain_consistency: true
section_prompts:
introduction: |
Write an engaging introduction for this section:
{{ section }}
Hook the reader and clearly state the value proposition.
Tone: {{ brand_voice.tone }}
technical_section: |
Write the technical content for:
{{ section }}
Include:
- Clear explanations
- Relevant code examples
- Best practices
- Common pitfalls
Maintain {{ target_audience }} level complexity.
conclusion: |
Write a strong conclusion that:
- Summarizes key points
- Provides actionable next steps
- Includes a call-to-action
- Leaves lasting impression
outputs:
- content_sections
- code_snippets
- name: optimize_code_examples
type: code_optimizer
inputs:
snippets: "{{ code_snippets }}"
language_context: "{{ research.primary_language }}"
config:
ensure_runnable: true
add_comments: true
follow_conventions: true
include_imports: true
outputs:
- optimized_code
- execution_notes
- name: seo_optimization
type: seo_enhancer
when: "{{ metadata.seo_enabled }}"
inputs:
content: "{{ content_sections }}"
topic: "{{ topic }}"
keywords: "{{ key_concepts }}"
config:
optimization_targets:
- title_tag
- meta_description
- headers_hierarchy
- keyword_density
- internal_linking
- image_alt_text
readability_score: "60-70"
outputs:
- seo_content
- seo_metadata
- readability_metrics
- name: generate_visuals_prompts
type: visual_prompt_generator
inputs:
outline: "{{ blog_outline }}"
content: "{{ content_sections }}"
config:
visual_types:
- architecture_diagrams
- flow_charts
- code_comparisons
- infographics
style_guide: "technical-modern"
outputs:
- visual_prompts
- diagram_specifications
- name: assemble_final_post
type: content_assembler
inputs:
sections: "{{ seo_content | default(content_sections) }}"
code: "{{ optimized_code }}"
visuals: "{{ visual_prompts }}"
metadata: "{{ seo_metadata | default({}) }}"
config:
format: "markdown"
include_frontmatter: true
add_table_of_contents: true
code_syntax_highlighting: true
outputs:
- final_blog_post
- publishing_metadata
- name: quality_review
type: llm_reviewer
inputs:
content: "{{ final_blog_post }}"
requirements: "{{ content_requirements }}"
voice: "{{ brand_voice }}"
prompt: |
Review this blog post for:
1. Technical accuracy
2. Audience appropriateness ({{ target_audience }})
3. Brand voice consistency ({{ brand_voice.tone }})
4. Engagement and readability
5. Completeness and value delivery
Content:
{{ content }}
Provide:
- Quality score (0-100)
- Specific improvements needed
- Fact-checking concerns
- Final approval status
outputs:
- quality_score
- improvement_suggestions
- approval_status
outputs:
blog_post:
type: markdown_document
includes:
- final_content
- metadata
- seo_tags
- visual_prompts
publishing_package:
type: structured_output
includes:
- blog_post
- social_media_snippets
- email_summary
- visual_assets_list
metrics:
type: content_metrics
includes:
- word_count
- readability_score
- seo_score
- estimated_read_time
2. Tutorial Generation Pipeline
Purpose
Create comprehensive, step-by-step tutorials that guide users through technical concepts or implementation processes with clear examples and progressive learning paths.
Configuration Structure
name: tutorial_generation_pipeline
version: "2.0"
type: content_generation
description: "Interactive tutorial creation with code examples and exercises"
metadata:
category: content
sub_category: tutorial
learning_formats: ["step-by-step", "workshop", "video-script", "interactive"]
inputs:
tutorial_topic:
type: string
required: true
description: "What the tutorial will teach"
learning_objectives:
type: array
required: true
items:
type: string
description: "Specific skills/knowledge users will gain"
difficulty_level:
type: string
enum: ["beginner", "intermediate", "advanced"]
required: true
tutorial_format:
type: string
enum: ["step-by-step", "workshop", "video-script", "interactive"]
default: "step-by-step"
prerequisites:
type: array
items:
type: string
description: "Required knowledge/skills"
technology_stack:
type: array
items:
type: object
properties:
name: string
version: string
role: string
steps:
- name: design_learning_path
type: curriculum_designer
inputs:
objectives: "{{ learning_objectives }}"
difficulty: "{{ difficulty_level }}"
prerequisites: "{{ prerequisites }}"
config:
pedagogy_approach: "progressive_disclosure"
include_checkpoints: true
scaffold_complexity: true
outputs:
- learning_path
- milestone_checkpoints
- complexity_progression
- name: create_project_scaffold
type: project_generator
inputs:
topic: "{{ tutorial_topic }}"
stack: "{{ technology_stack }}"
format: "{{ tutorial_format }}"
config:
include_starter_code: true
create_branches: true # for different stages
setup_testing: true
include_solutions: true
outputs:
- project_structure
- starter_files
- solution_files
- test_suites
- name: generate_tutorial_steps
type: step_generator
inputs:
learning_path: "{{ learning_path }}"
project: "{{ project_structure }}"
objectives: "{{ learning_objectives }}"
config:
step_template: |
## Step {{ step_number }}: {{ step_title }}
### What You'll Learn
{{ learning_outcomes }}
### Background
{{ conceptual_explanation }}
### Implementation
{{ implementation_guide }}
### Code Example
```{{ language }}
{{ code_example }}
```
### Try It Yourself
{{ exercise }}
### Common Issues
{{ troubleshooting }}
### Check Your Understanding
{{ comprehension_questions }}
include_for_each_step:
- clear_objective
- conceptual_intro
- hands_on_code
- practice_exercise
- self_check
outputs:
- tutorial_steps
- exercise_definitions
- checkpoint_tests
- name: create_code_examples
type: example_generator
inputs:
steps: "{{ tutorial_steps }}"
stack: "{{ technology_stack }}"
difficulty: "{{ difficulty_level }}"
config:
example_principles:
- runnable
- well_commented
- idiomatic
- progressive_complexity
include_variants:
- basic_implementation
- error_handling
- optimized_version
outputs:
- code_examples
- example_variations
- anti_patterns
- name: design_exercises
type: exercise_creator
inputs:
learning_objectives: "{{ learning_objectives }}"
code_examples: "{{ code_examples }}"
difficulty: "{{ difficulty_level }}"
config:
exercise_types:
- code_completion
- bug_fixing
- feature_addition
- refactoring
- design_challenge
difficulty_progression: true
include_hints: true
solution_walkthroughs: true
outputs:
- exercises
- hints_system
- solutions_guide
- name: create_visual_aids
type: tutorial_visual_generator
inputs:
steps: "{{ tutorial_steps }}"
concepts: "{{ learning_path.key_concepts }}"
config:
visual_types:
- concept_diagrams
- architecture_flows
- state_transitions
- code_execution_traces
- decision_trees
style: "educational-clean"
include_annotations: true
outputs:
- visual_aids
- diagram_descriptions
- name: generate_supporting_content
type: support_content_creator
inputs:
tutorial: "{{ tutorial_steps }}"
exercises: "{{ exercises }}"
config:
create:
- quick_reference_card
- troubleshooting_guide
- further_reading_list
- project_ideas
- skill_assessment
outputs:
- reference_materials
- troubleshooting_faq
- next_steps_guide
- name: format_for_platform
type: platform_formatter
inputs:
content: "{{ tutorial_steps }}"
exercises: "{{ exercises }}"
visuals: "{{ visual_aids }}"
format: "{{ tutorial_format }}"
config:
platforms:
step-by-step:
- markdown_with_frontmatter
- syntax_highlighting
- copy_buttons
- progress_tracking
workshop:
- presenter_notes
- timing_guides
- participant_handouts
video-script:
- scene_descriptions
- voiceover_script
- screen_recordings
interactive:
- jupyter_notebooks
- codesandbox_embeds
- live_coding_env
outputs:
- formatted_tutorial
- platform_assets
- name: validate_tutorial
type: tutorial_validator
inputs:
tutorial: "{{ formatted_tutorial }}"
code: "{{ code_examples }}"
exercises: "{{ exercises }}"
config:
validation_checks:
- code_execution
- link_validity
- prerequisite_coverage
- objective_alignment
- difficulty_consistency
- time_estimates
outputs:
- validation_report
- estimated_duration
- difficulty_metrics
outputs:
tutorial_package:
type: comprehensive_tutorial
includes:
- formatted_content
- code_repository
- exercise_bank
- visual_assets
- supporting_materials
instructor_resources:
type: teaching_package
when: "{{ tutorial_format == 'workshop' }}"
includes:
- presenter_guide
- timing_schedule
- discussion_prompts
- assessment_rubrics
learner_metrics:
type: learning_analytics
includes:
- estimated_time
- skill_coverage
- difficulty_curve
- checkpoint_locations
3. API Documentation Pipeline
Purpose
Automatically generate comprehensive API documentation from code, including examples, schemas, and interactive components while maintaining accuracy and completeness.
Configuration Structure
name: api_documentation_pipeline
version: "2.0"
type: content_generation
description: "Automated API documentation with examples and interactive features"
metadata:
category: content
sub_category: api_docs
output_formats: ["openapi", "markdown", "html", "postman"]
api_types: ["rest", "graphql", "grpc", "websocket"]
inputs:
source_code_path:
type: string
required: true
description: "Path to API source code"
api_type:
type: string
enum: ["rest", "graphql", "grpc", "websocket"]
required: true
documentation_style:
type: string
enum: ["reference", "tutorial", "cookbook", "complete"]
default: "complete"
include_examples:
type: boolean
default: true
branding:
type: object
properties:
company_name: string
logo_url: string
color_scheme: object
custom_css: string
steps:
- name: extract_api_structure
type: api_parser
inputs:
source_path: "{{ source_code_path }}"
api_type: "{{ api_type }}"
config:
extract:
- endpoints
- methods
- parameters
- request_bodies
- response_schemas
- authentication
- rate_limits
- deprecations
parse_comments: true
infer_types: true
outputs:
- api_structure
- endpoint_metadata
- type_definitions
- name: analyze_api_patterns
type: pattern_analyzer
inputs:
structure: "{{ api_structure }}"
config:
identify:
- resource_patterns
- naming_conventions
- versioning_strategy
- error_patterns
- pagination_style
outputs:
- api_patterns
- consistency_report
- name: generate_openapi_spec
type: openapi_generator
inputs:
structure: "{{ api_structure }}"
metadata: "{{ endpoint_metadata }}"
patterns: "{{ api_patterns }}"
config:
openapi_version: "3.1.0"
include_extensions:
- x-code-samples
- x-readme
- x-logo
security_schemes: auto_detect
example_generation: true
outputs:
- openapi_spec
- validation_warnings
- name: create_endpoint_documentation
type: endpoint_documenter
inputs:
endpoints: "{{ api_structure.endpoints }}"
patterns: "{{ api_patterns }}"
style: "{{ documentation_style }}"
config:
per_endpoint_sections:
- description
- parameters_table
- request_examples
- response_examples
- error_codes
- rate_limits
- authentication
- related_endpoints
example_languages:
- curl
- javascript
- python
- go
- java
outputs:
- endpoint_docs
- code_examples
- name: generate_type_documentation
type: type_documenter
inputs:
types: "{{ type_definitions }}"
api_type: "{{ api_type }}"
config:
include:
- field_descriptions
- validation_rules
- example_values
- relationships
- versioning_info
format_schemas: true
generate_diagrams: true
outputs:
- type_docs
- schema_diagrams
- name: create_authentication_guide
type: auth_guide_generator
inputs:
auth_methods: "{{ api_structure.authentication }}"
api_patterns: "{{ api_patterns }}"
config:
guide_sections:
- overview
- setup_steps
- token_management
- security_best_practices
- troubleshooting
include_flow_diagrams: true
example_implementations: true
outputs:
- auth_documentation
- security_checklist
- name: generate_usage_examples
type: example_generator
when: "{{ include_examples }}"
inputs:
endpoints: "{{ api_structure.endpoints }}"
patterns: "{{ api_patterns }}"
config:
example_scenarios:
- basic_usage
- authentication_flow
- error_handling
- pagination
- filtering_sorting
- bulk_operations
- webhooks
languages: ["curl", "javascript", "python"]
include_responses: true
runnable_examples: true
outputs:
- usage_examples
- tutorial_sequences
- name: create_interactive_components
type: interactive_generator
inputs:
openapi: "{{ openapi_spec }}"
examples: "{{ usage_examples }}"
config:
components:
- api_explorer
- request_builder
- response_viewer
- auth_tester
frameworks:
- swagger_ui
- redoc
- custom_react
outputs:
- interactive_components
- component_config
- name: generate_sdks
type: sdk_generator
inputs:
openapi: "{{ openapi_spec }}"
patterns: "{{ api_patterns }}"
config:
languages:
- typescript
- python
- go
- java
include:
- client_libraries
- type_definitions
- examples
- tests
outputs:
- sdk_packages
- sdk_documentation
- name: create_migration_guides
type: migration_guide_creator
inputs:
api_structure: "{{ api_structure }}"
deprecations: "{{ api_structure.deprecations }}"
config:
guide_types:
- version_migration
- breaking_changes
- deprecation_timeline
- compatibility_matrix
outputs:
- migration_guides
- compatibility_notes
- name: assemble_documentation
type: doc_assembler
inputs:
openapi: "{{ openapi_spec }}"
endpoint_docs: "{{ endpoint_docs }}"
type_docs: "{{ type_docs }}"
auth_guide: "{{ auth_documentation }}"
examples: "{{ usage_examples }}"
interactive: "{{ interactive_components }}"
branding: "{{ branding }}"
config:
output_formats: "{{ metadata.output_formats }}"
navigation_structure: auto_generate
search_indexing: true
version_switcher: true
outputs:
- complete_documentation
- static_site
- search_index
outputs:
api_documentation:
type: multi_format_docs
formats:
- openapi_spec
- markdown_files
- html_site
- pdf_reference
developer_resources:
type: resource_package
includes:
- sdk_packages
- postman_collection
- insomnia_workspace
- example_projects
deployment_package:
type: static_site
includes:
- html_files
- assets
- search_index
- interactive_components
4. Changelog Generation Pipeline
Purpose
Automatically generate comprehensive changelogs from git history, issue trackers, and pull requests while categorizing changes and maintaining consistent formatting.
Configuration Structure
name: changelog_generation_pipeline
version: "2.0"
type: content_generation
description: "Intelligent changelog generation with semantic categorization"
metadata:
category: content
sub_category: changelog
versioning_schemes: ["semver", "calver", "custom"]
output_formats: ["markdown", "json", "html", "rss"]
inputs:
repository_path:
type: string
required: true
description: "Path to git repository"
version_range:
type: object
properties:
from:
type: string
description: "Starting version/tag/commit"
to:
type: string
description: "Ending version/tag/commit"
default: "HEAD"
categorization_rules:
type: object
properties:
breaking_changes:
type: array
items:
type: string
default: ["BREAKING:", "!:", "breaking change"]
features:
type: array
items:
type: string
default: ["feat:", "feature:", "add:"]
fixes:
type: array
items:
type: string
default: ["fix:", "bugfix:", "patch:"]
include_contributors:
type: boolean
default: true
include_stats:
type: boolean
default: true
steps:
- name: collect_commits
type: git_history_collector
inputs:
repo: "{{ repository_path }}"
range: "{{ version_range }}"
config:
include_merge_commits: false
fetch_full_messages: true
extract_metadata:
- author
- date
- files_changed
- insertions
- deletions
outputs:
- commit_list
- commit_metadata
- name: collect_issues_prs
type: issue_tracker_collector
inputs:
repo: "{{ repository_path }}"
date_range: "{{ commit_metadata.date_range }}"
config:
sources:
- github
- gitlab
- jira
fetch:
- closed_issues
- merged_prs
- labels
- milestones
outputs:
- issues_list
- pull_requests
- cross_references
- name: analyze_changes
type: change_analyzer
inputs:
commits: "{{ commit_list }}"
issues: "{{ issues_list }}"
prs: "{{ pull_requests }}"
config:
analysis_depth: "semantic"
detect:
- breaking_changes
- new_features
- bug_fixes
- performance_improvements
- dependency_updates
- documentation_changes
link_commits_to_issues: true
outputs:
- categorized_changes
- impact_analysis
- dependency_changes
- name: extract_release_notes
type: release_note_extractor
inputs:
prs: "{{ pull_requests }}"
issues: "{{ issues_list }}"
config:
extract_from:
- pr_descriptions
- issue_descriptions
- special_comments
markers:
- "Release Notes:"
- "Changelog:"
- "@changelog"
outputs:
- manual_release_notes
- highlighted_changes
- name: generate_change_descriptions
type: llm_description_generator
inputs:
changes: "{{ categorized_changes }}"
context: "{{ cross_references }}"
manual_notes: "{{ manual_release_notes }}"
prompt: |
Generate clear, user-focused descriptions for these changes:
Changes:
{{ changes | to_yaml }}
Context from issues/PRs:
{{ context | to_yaml }}
Manual notes:
{{ manual_notes }}
For each change:
1. Write a concise, user-friendly description
2. Highlight the benefit or impact
3. Include relevant issue/PR numbers
4. Note any migration requirements
5. Keep technical jargon minimal
Group by category and prioritize by impact.
outputs:
- change_descriptions
- migration_notes
- name: calculate_version_bump
type: version_calculator
inputs:
changes: "{{ categorized_changes }}"
current_version: "{{ version_range.from }}"
scheme: "{{ metadata.versioning_schemes[0] }}"
config:
rules:
major: ["breaking_changes"]
minor: ["features", "enhancements"]
patch: ["fixes", "performance", "docs"]
respect_prereleases: true
outputs:
- suggested_version
- version_justification
- name: generate_statistics
type: release_stats_generator
when: "{{ include_stats }}"
inputs:
commits: "{{ commit_metadata }}"
changes: "{{ categorized_changes }}"
contributors: "{{ commit_metadata.unique_authors }}"
config:
calculate:
- total_commits
- contributors_count
- lines_changed
- files_affected
- average_pr_time
- issue_closure_rate
visualize:
- contribution_graph
- change_distribution
- activity_timeline
outputs:
- release_statistics
- statistics_visuals
- name: format_changelog
type: changelog_formatter
inputs:
version: "{{ suggested_version }}"
date: "{{ version_range.to_date }}"
descriptions: "{{ change_descriptions }}"
stats: "{{ release_statistics | default({}) }}"
contributors: "{{ commit_metadata.unique_authors }}"
config:
template: |
# {{ version }} ({{ date }})
{{ release_summary }}
## ⚠️ Breaking Changes
{{ breaking_changes }}
## ✨ New Features
{{ features }}
## 🐛 Bug Fixes
{{ fixes }}
## 🚀 Performance Improvements
{{ performance }}
## 📦 Dependency Updates
{{ dependencies }}
## 📚 Documentation
{{ documentation }}
{{ #if include_stats }}
## 📊 Release Statistics
{{ statistics }}
{{ /if }}
{{ #if include_contributors }}
## 👥 Contributors
{{ contributors_list }}
{{ /if }}
{{ migration_guide }}
group_by_importance: true
include_compare_link: true
outputs:
- formatted_changelog
- section_counts
- name: generate_migration_guide
type: migration_guide_generator
when: "{{ categorized_changes.breaking_changes | length > 0 }}"
inputs:
breaking_changes: "{{ categorized_changes.breaking_changes }}"
old_version: "{{ version_range.from }}"
new_version: "{{ suggested_version }}"
config:
include:
- step_by_step_guide
- code_examples
- deprecation_timeline
- rollback_instructions
outputs:
- migration_guide
- deprecation_warnings
- name: create_release_assets
type: release_asset_generator
inputs:
changelog: "{{ formatted_changelog }}"
version: "{{ suggested_version }}"
stats: "{{ statistics_visuals }}"
config:
generate:
- release_notes_pdf
- announcement_email
- social_media_posts
- blog_post_draft
branding: true
outputs:
- release_assets
- announcement_content
outputs:
changelog:
type: versioned_document
formats: "{{ metadata.output_formats }}"
includes:
- formatted_content
- version_metadata
- contributor_list
release_package:
type: release_bundle
includes:
- changelog
- migration_guide
- release_assets
- statistics_report
ci_artifacts:
type: automation_outputs
includes:
- version_bump_config
- release_notes_json
- changelog_feed_rss
Reusable Components
1. Content Analysis Components
Readability Analyzer
component: readability_analyzer
type: content_analysis
description: "Analyze content readability and complexity"
inputs:
content: string
target_audience: string
config:
metrics:
- flesch_reading_ease
- flesch_kincaid_grade
- gunning_fog
- average_sentence_length
- vocabulary_complexity
outputs:
readability_scores: object
improvement_suggestions: array
SEO Analyzer
component: seo_analyzer
type: content_analysis
description: "Analyze and optimize content for SEO"
inputs:
content: string
target_keywords: array
competitor_analysis: object
config:
analyze:
- keyword_density
- meta_tags
- header_structure
- internal_links
- content_length
- semantic_relevance
outputs:
seo_score: number
optimization_tasks: array
keyword_suggestions: array
2. Content Generation Components
Code Example Generator
component: code_example_generator
type: content_generation
description: "Generate runnable code examples"
inputs:
concept: string
language: string
complexity: string
config:
ensure:
- syntactic_correctness
- best_practices
- error_handling
- comments
- imports
outputs:
code_example: string
explanation: string
common_variations: array
Visual Prompt Generator
component: visual_prompt_generator
type: content_generation
description: "Generate prompts for diagram/visual creation"
inputs:
concept: string
visual_type: string
style_guide: object
config:
prompt_elements:
- layout_description
- component_list
- relationship_mapping
- style_specifications
- annotation_requirements
outputs:
visual_prompt: string
mermaid_diagram: string
svg_specification: object
3. Content Enhancement Components
Tone Adjuster
component: tone_adjuster
type: content_enhancement
description: "Adjust content tone and voice"
inputs:
content: string
target_tone: string
brand_voice: object
config:
preserve:
- technical_accuracy
- key_information
- structure
adjust:
- vocabulary
- sentence_structure
- formality_level
outputs:
adjusted_content: string
tone_analysis: object
Integration Patterns
1. CMS Integration
integration: cms
supported_platforms:
- wordpress
- contentful
- strapi
- gatsby
configuration:
auto_publish: false
draft_creation: true
metadata_mapping:
title: "post_title"
content: "post_content"
tags: "post_tags"
seo: "yoast_meta"
2. Documentation Platforms
integration: documentation_platforms
supported:
- gitbook
- readthedocs
- docusaurus
- mkdocs
features:
- auto_deployment
- version_management
- search_indexing
- multi_language
Performance Considerations
1. Content Caching
- Cache generated outlines for reuse
- Store common code examples
- Maintain template library
- Cache SEO analysis results
2. Parallel Generation
- Generate sections concurrently
- Parallel visual prompt creation
- Distributed example generation
- Concurrent quality checks
3. Incremental Updates
- Detect changed sections only
- Incremental changelog generation
- Partial documentation updates
- Smart cache invalidation
Quality Assurance
1. Content Validation
validation_checks:
technical_accuracy:
- code_execution
- fact_checking
- link_validation
consistency:
- terminology_check
- style_guide_adherence
- voice_consistency
completeness:
- objective_coverage
- example_presence
- section_completion
2. Automated Testing
- Grammar and spell checking
- Code snippet validation
- SEO guideline compliance
- Accessibility standards
- Cross-reference validation
Future Enhancements
1. Multi-Modal Content
- Video script generation
- Podcast outline creation
- Infographic data preparation
- Interactive tutorial building
2. Personalization
- Audience-specific variations
- Dynamic content adaptation
- Learning path customization
- Preference-based formatting
3. Analytics Integration
- Content performance tracking
- Engagement metrics collection
- A/B testing support
- Reader feedback incorporation