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Agent manager

Skill aida-core/aida-core-plugin/skills/agent-manager

Create, validate, version, and list AIDA-managed Claude Code subagent definition files (two-phase API). Use for CRUD on agent .md definitions, routed through /aida extension management. Do NOT use to run, dispatch, or consult a subagent — this only authors and maintains the definition files.From its SKILL.md

Install
npx -y skills add aida-core/aida-core-plugin --skill agent-manager

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SKILL.md

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<!-- SPDX-FileCopyrightText: 2026 The AIDA Core Authors --> <!-- SPDX-License-Identifier: MPL-2.0 -->

Agent Manager

Focused management interface for Claude Code agent (subagent) definitions. Handles the full lifecycle of agent .md files and their knowledge/ directories.

Activation

This skill activates when:

  • User invokes /aida agent [create|validate|version|list]
  • Agent management is needed

Operations

Command Routing

Parse the command to determine:

  1. Operation: create, validate, version, list
  2. Arguments: name, description, options

The component type is always agent -- no type selection is needed.

Create Operations

For create operations, use the three-phase orchestration pattern:

Phase 1: Gather Context (Python)

Run the script to infer metadata and get questions:

python {base_directory}/scripts/manage.py \
  --get-questions \
  --context='{"operation": "create", "description": "user description", "location": "user"}'

Returns:

{
  "inferred": {
    "name": "inferred-name",
    "version": "0.1.0",
    "tags": ["custom"],
    "base_path": "~/.claude/agents/inferred-name"
  },
  "questions": [],
  "project_context": {
    "languages": ["python"],
    "frameworks": ["fastapi"]
  }
}

Phase 2: Generate Content (Agent)

Spawn the claude-code-expert agent with complete context and output contract:

Operation: CREATE
Type: agent

Requirements:
  - Name: {inferred.name}
  - Description: {user_description}
  - Location: {inferred.base_path}
  - Project Context: {project_context}
  - User Answers: {answers to questions}

Output Format:
Return a JSON object with this exact structure:

{
  "validation": {
    "passed": true|false,
    "issues": [
      {
        "severity": "error|warning",
        "message": "description",
        "suggestion": "how to fix"
      }
    ]
  },
  "files": [
    {
      "path": "agents/{name}/{name}.md",
      "content": "..."
    },
    {
      "path": "agents/{name}/knowledge/index.md",
      "content": "..."
    }
  ],
  "summary": {
    "created": ["list of relative paths"],
    "next_steps": ["actionable items for user"]
  }
}

Handle agent response:

  1. Parse the JSON response
  2. If validation.passed is false with errors:
    • Report issues to user
    • Ask if they want to proceed or provide more info
  3. If validation passed (or user chose to proceed):
    • Continue to Phase 3

Phase 3: Write Files (Python)

Pass agent output to Python for file creation:

python {base_directory}/scripts/manage.py \
  --execute \
  --context='{"operation": "create", "agent_output": <agent_json>}'

The script:

  1. Validates file structure (required fields, frontmatter)
  2. Creates directories (including knowledge/)
  3. Writes files
  4. Returns success/failure

Validate Operations

For validate operations:

  1. Run validation script
  2. Report results with any errors

Script invocation:

# Validate specific agent
python {base_directory}/scripts/manage.py --execute \
  --context='{"operation": "validate", "name": "my-agent"}'

# Validate all agents
python {base_directory}/scripts/manage.py --execute \
  --context='{"operation": "validate", "all": true, "location": "all"}'

Version Operations

For version operations:

  1. Find the agent
  2. Bump version (major, minor, or patch)
  3. Update the file
  4. Report the version change

Script invocation:

python {base_directory}/scripts/manage.py --execute \
  --context='{"operation": "version", "name": "my-agent", "bump": "patch"}'

List Operations

For list operations:

  1. Search specified locations
  2. Parse agent metadata from frontmatter
  3. Return formatted list

Script invocation:

python {base_directory}/scripts/manage.py --execute \
  --context='{"operation": "list", "location": "all", "format": "table"}'

Path Resolution

Base Directory: Provided when skill loads via <command-message> tags.

Script Execution:

{base_directory}/scripts/manage.py

Reference Files:

{base_directory}/references/create-workflow.md
{base_directory}/references/validate-workflow.md
{base_directory}/references/schemas.md

Location Options

LocationPathUse Case
user~/.claude/Personal agents
project./.claude/Project-specific agents
pluginCustom pathPlugin development

Example Workflow

Creating an Agent (Full Flow)

User: /aida agent create "handles database migrations"

1. Parse: operation=create, description="handles database migrations"

2. Phase 1 (Python):
   python manage.py --get-questions --context='{...}'
   Returns:
   - inferred: name="database-migration", version="0.1.0"
   - questions: [location question]
   - project_context: {languages: ["python"]}

3. Ask user questions (if any):
   "Where should we create this agent?"

4. Phase 2 (Agent):
   Spawn claude-code-expert with context + output contract
   Agent returns JSON with files array

5. Phase 3 (Python):
   python manage.py --execute --context='{"operation": "create", "agent_output": {...}}'
   - Validates structure
   - Creates directories + knowledge/
   - Writes files

6. Report to user:
   "Created agent 'database-migration' with 3 files"

Resources

scripts/

  • manage.py - Entry point for two-phase API
  • operations/ - Operation modules
    • utils.py - Shared utilities (re-export shim)
    • extensions.py - Agent operations

references/

  • create-workflow.md - Agent creation workflow
  • validate-workflow.md - Agent validation rules
  • schemas.md - Agent frontmatter schema

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