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Overview

The standardise command provides tools for managing documentation and examples consistency across the PraisonAI project. It implements the Feature Docs/Examples Protocol (FDEP) to ensure all features have proper documentation and examples.

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Commands

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Check

Check for standardisation issues without making changes. 
praisonai standardise check [OPTIONS]
Options:
OptionTypeDefaultDescription
--path, -pstring.Project root path
--featurestring-Specific feature slug to check
--scopechoiceallScope: all, docs, examples, sdk, cli
--ciflagfalseCI mode with exit codes
Example: 
# Check all features
praisonai standardise check

# Check specific feature
praisonai standardise check --feature guardrails

# CI mode (returns exit code 1 if issues found)
praisonai standardise check --ci

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Report

Generate a detailed standardisation report. 
praisonai standardise report [OPTIONS]
Options:
OptionTypeDefaultDescription
--path, -pstring.Project root path
--format, -fchoicetextFormat: text, json, markdown
--output, -ostring-Output file path
Example: 
# Text report to stdout
praisonai standardise report

# Markdown report to file
praisonai standardise report --format markdown --output report.md

# JSON report for automation
praisonai standardise report --format json

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Fix

Fix standardisation issues by creating missing artifacts. 
praisonai standardise fix [OPTIONS]
Options:
OptionTypeDefaultDescription
--path, -pstring.Project root path
--featurestring-Specific feature slug to fix
--applyflagfalseActually apply changes (default: dry-run)
--no-backupflagfalseDon’t create backups
Example: 
# Preview what would be fixed (dry-run)
praisonai standardise fix --feature guardrails

# Actually apply fixes
praisonai standardise fix --feature guardrails --apply

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Init

Initialise a new feature with all required artifacts. 
praisonai standardise init FEATURE [OPTIONS]
Arguments:
ArgumentDescription
FEATUREFeature slug to initialise
Options:
OptionTypeDefaultDescription
--path, -pstring.Project root path
--applyflagfalseActually create files
Example: 
# Preview what would be created
praisonai standardise init my-feature

# Create the files
praisonai standardise init my-feature --apply

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AI

AI-powered generation of documentation and examples using LLM.
Real, Runnable Examples: The AI generator creates actual working code, not templates. Examples are verified by execution before being written - if the code doesn’t run, it won’t be saved.
praisonai standardise ai FEATURE [OPTIONS]
Arguments:
ArgumentDescription
FEATUREFeature slug to generate content for
Options:
OptionTypeDefaultDescription
--type, -tchoiceallType: docs, examples, all
--applyflagfalseActually create files
--verifyflagfalseAdditional AI content review
--modelstringgpt-4o-miniLLM model to use
--path, -pstring.Project root path
Features:
  • Real Examples: Generates working code with mock data, not placeholder templates
  • Execution Verification: Examples are run before writing to ensure they work
  • Auto-Retry: If code fails, the AI attempts to fix it (up to 2 retries)
  • External Library Handling: Examples requiring external libraries are marked but still saved
Example: 
# Preview AI-generated docs
praisonai standardise ai guardrails --type docs

# Generate and apply examples with verification
praisonai standardise ai guardrails --type examples --apply --verify

# Use a different model
praisonai standardise ai guardrails --model gpt-4o --apply
Verification Output: 
📝 Generating example_basic...
  ✅ Code verified: runs successfully
  ✓ Created: examples/guardrails/guardrails-basic.py

📝 Generating example_advanced...
  ✅ Code verified: runs successfully
  ✓ Created: examples/guardrails/guardrails-advanced.py

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Checkpoint

Create an undo checkpoint before making changes. 
praisonai standardise checkpoint [OPTIONS]
Options:
OptionTypeDefaultDescription
--message, -mstring-Checkpoint message
--path, -pstring.Repository path
Example: 
# Create checkpoint with message
praisonai standardise checkpoint -m "Before AI generation"

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Undo

Undo to a previous checkpoint. 
praisonai standardise undo [OPTIONS]
Options:
OptionTypeDefaultDescription
--checkpointstring-Specific checkpoint ID
--listflagfalseList available checkpoints
--path, -pstring.Repository path
Example: 
# List available checkpoints
praisonai standardise undo --list

# Undo to specific checkpoint
praisonai standardise undo --checkpoint standardise-checkpoint-20240101-120000

# Undo to previous checkpoint
praisonai standardise undo

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Redo

Redo after an undo operation. 
praisonai standardise redo [OPTIONS]
Options:
OptionTypeDefaultDescription
--path, -pstring.Repository path
Example: 
praisonai standardise redo

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Workflow Example

# 1. Check current state
praisonai standardise check

# 2. Create checkpoint before changes
praisonai standardise checkpoint -m "Before standardisation"

# 3. Generate missing examples with AI
praisonai standardise ai guardrails --type examples --apply --verify

# 4. If something went wrong, undo
praisonai standardise undo

# 5. Generate report for documentation
praisonai standardise report --format markdown --output STANDARDISATION.md

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Exit Codes

When using --ci mode:
CodeMeaning
0No issues found
1Issues found
2Error running check

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See Also