Skill Invocation

Run agent skills via /slash commands, $ARGUMENTS substitution, and inline shell

Skills can be invoked three ways: auto-triggered by the LLM from the system-prompt listing, manually via /skill-name slash commands (browse them by typing / at the praisonai code prompt), or programmatically through SkillManager.invoke().

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    skills=["./skills/deploy"],
)
agent.start("/deploy staging prod")

The user sends a slash command or normal prompt; the agent routes to skill invoke or LLM handling.

Quick Start

1. Slash command

   from praisonaiagents import Agent
   
   agent = Agent(
       name="assistant",
       skills=["./skills/deploy"],
   )
   agent.start("/deploy staging prod")
   # Renders the skill body with $ARGUMENTS substituted, then continues the turn

2. Auto-trigger

   from praisonaiagents import Agent
   
   agent = Agent(
       name="assistant",
       skills=["./skills/csv-summary"],
   )
   agent.start("Summarise this CSV for me")
   # LLM sees skill descriptions in the system prompt and activates the matching skill

How It Works

Discover skills in the interactive menu

Inside praisonai code (the REPL and the async TUI), type / at the prompt to see every installed skill listed alongside built-ins and your custom .praisonai/commands/*.md files. Each entry uses the skill’s description from SKILL.md frontmatter, so a well-written description doubles as menu help. Editing a SKILL.md body or adding a new skill under any discovered skill root takes effect on the next prompt in praisonai code — the registry re-scans when the definition roots change.

> /
  /help
  /clear
  /deploy               Deploy the current branch
  /pr-summary           Summarise a pull request
  /csv-summary          Summarise a CSV file

> /deploy staging prod

Pressing Enter on /deploy staging prod runs the same code path as agent.skill_manager.invoke("deploy", raw_args="staging prod") — the SKILL.md body is rendered with $ARGUMENTS substituted and passed as the next turn’s prompt.

Argument Substitution

Inside SKILL.md bodies you can reference arguments passed after the skill name:

Deploy $ARGUMENTS now.

Migrate $ARGUMENTS[0] from $ARGUMENTS[1] to $ARGUMENTS[2].

Hello $0, meet $1.

Working inside ${PRAISON_SKILL_DIR}. Session ${PRAISON_SESSION_ID}.

Inline Shell Substitution

from praisonaiagents import SkillManager

mgr = SkillManager()
mgr.add_skill("./skills/pr-summary")
rendered = mgr.invoke("pr-summary", raw_args="42", shell_exec=True)

Current branch: !`git rev-parse --abbrev-ref HEAD`

```!
node --version
npm --version
```

Invocation Policy

Where each skill shows up

Two independent flags decide where a skill appears — one axis for the LLM, one for the user.

The interactive / menu is the completion list you see when you type / inside praisonai code (REPL or async TUI). “Absent from the / menu” means both completion and dispatch — typing the name falls through to “Unknown command”.

A /deploy skill (both flags default) and a model-only pricing-lookup skill (user-invocable: false) live side by side. Type / in praisonai code and only one shows:

> /
/deploy        Deploy to an environment
# pricing-lookup is absent — the LLM still reads it from the system prompt,
# but typing /pricing-lookup falls through to "Unknown command".

Flip the flag back off and the same list gains the model-only skill:

> /            # with user-invocable omitted on pricing-lookup
/deploy          Deploy to an environment
/pricing-lookup  Look up current pricing

Allowed Tools Pre-approval

Common Patterns

Programmatic invoke

from praisonaiagents import Agent

agent = Agent(name="assistant", skills=["./skills/deploy"])
rendered = agent.skill_manager.invoke("deploy", raw_args="staging prod")

YAML

# agents.yaml
agents:
  assistant:
    role: Helper
    goal: Help the user
    backstory: You are helpful.
    skills:
      - ./skills/deploy
      - ~/.praisonai/skills/csv-summary

CLI

praisonai skills list

praisonai "/deploy staging" --skills ./skills/deploy

# Either verb works — pick whichever reads more naturally.
praisonai skills add https://github.com/acme/my-skill
praisonai skills install ./local-skill --dest ~/.praisonai/skills

praisonai skills sync https://github.com/acme/skills-repo
praisonai skills sync                       # uses skills.urls from config

Precedence

Skills are discovered in this order (first wins on name collision):

1. Project

   ./.praisonai/skills/ or ./.claude/skills/

2. Ancestors

   Every .praisonai/skills or .claude/skills in a parent directory (monorepo support)

3. User

   ~/.praisonai/skills/

4. Remote cache

   ~/.praisonai/cache/remote-skills/<source>/current/ — populated by praisonai skills sync (see Remote Skill Sources)

5. System

   /etc/praison/skills/

Best Practices

Related