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Self-improving skills enable agents to create, edit, and manage their own capabilities dynamically, learning from user interactions and building persistent knowledge.

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Quick Start

1

Enable Skill Management

from praisonaiagents import Agent

agent = Agent(
    name="Skill Builder",
    instructions="When users teach you something, save it as a skill for next time.",
    tools=["skill_manage", "skills_list", "skill_view"]  # auto-injected in bots
)

agent.start("Create a skill called 'weekly-summary' that summarises the week's work.")
2

Agent Learns and Improves

# Agent creates skills automatically from user interactions
# User: "Create a weekly summary skill"
# Agent calls: skill_manage(action="create", name="weekly-summary", content="...")

# Later interaction
# User: "Update the summary to include metrics"
# Agent calls: skill_manage(action="patch", name="weekly-summary", old_string="...", new_string="...")

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How It Works

The skill management system provides six core actions for runtime skill manipulation:
ActionPurposeSecurity Constraints
createCreate new skills100KB SKILL.md limit, name validation
editReplace skill contentPreserves frontmatter, atomic writes
patchTargeted find/replaceString matching, traversal protection
deleteRemove skills completelyPath validation, atomic removal
write_fileAdd skill resources1MB limit, allowed subdirs only
remove_fileDelete skill filesContainment checks, safe removal

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Configuration Options

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Skill Management Actions

ActionRequired ArgsOptional ArgsWhat it does
createname, contentcategoryCreate a new skill with SKILL.md body
editname, content-Replace an existing skill’s SKILL.md body
patchname, old_string, new_stringfile_path, replace_allFuzzy find-and-replace within a skill file
deletename-Remove a skill entirely
write_filename, file_path, file_content-Add/overwrite a file inside the skill
remove_filename, file_path-Delete a file from within the skill

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Python API Reference

from praisonaiagents.skills import SkillManager

mgr = SkillManager()
mgr.discover()

# Create new skills
result = mgr.create_skill("weekly-summary", "# Weekly Summary\nSteps...", category="reporting")

# Edit existing skills
result = mgr.edit_skill("weekly-summary", "# Weekly Summary v2\n...")

# Apply targeted patches
result = mgr.patch_skill("weekly-summary", old_string="v2", new_string="v3")

# Manage skill files
result = mgr.write_skill_file("weekly-summary", "scripts/report.py", "print('Weekly report')")
result = mgr.remove_skill_file("weekly-summary", "scripts/report.py")

# Delete skills
result = mgr.delete_skill("weekly-summary")

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Security Guards

GuardPurposeImplementation
Size LimitsPrevent resource exhaustion100KB SKILL.md, 1MB files
Name ValidationSecure identifiers[a-z0-9][a-z0-9._-]* pattern, 64 char limit
Path ValidationPrevent traversalBlock .., absolute paths, encoded attacks
Atomic WritesPrevent corruptionTemp file + rename operations
Allowed SubdirsRestrict file placementreferences/, templates/, scripts/, assets/ only

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Common Patterns

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User Teaching Flow

# Typical interaction where agent learns from user
agent = Agent(
    name="Learning Assistant", 
    instructions="""
    When users teach you something new, save it as a skill.
    Use skill_manage to create persistent knowledge.
    """,
    tools=["skill_manage", "skills_list", "skill_view"]
)

# User: "Here's how to analyze CSV data: load with pandas, clean nulls, then summarize"
# Agent automatically calls:
# skill_manage(action="create", name="csv-analysis", 
#              content="# CSV Analysis\n1. Load with pandas\n2. Clean nulls\n3. Summarize data")

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Skill Evolution

# Agent improves existing skills based on feedback
# User: "The CSV skill should also handle missing headers"
# Agent calls:
# skill_manage(action="patch", name="csv-analysis", 
#              old_string="2. Clean nulls", 
#              new_string="2. Handle missing headers\n3. Clean nulls")

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Adding Executable Resources

# Agent creates supporting scripts for skills
# skill_manage(action="write_file", name="csv-analysis",
#              file_path="scripts/analyze.py", 
#              file_content="import pandas as pd\n# Analysis script...")

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Best Practices

All skill operations are workspace-contained and use atomic writes via temp files. Never bypass name validation or path checks. Skills inherit workspace security automatically.
Start with simple skills and let agents enhance them through patch operations. This creates more natural learning patterns than full rewrites.
Use meaningful categories and names. Skills are stored in ~/.praisonai/skills/ by default, with clean directory structure per skill.
All skill operations return detailed JSON results with success flags and error messages. Always check result["success"] before proceeding.

Skills (Concepts)

Understanding what skills are and how they work

Workspace

How workspace containment secures skill operations