Agent-Skills.md

Agent Skills: /agents:analyze

Analyze all plugins for sub-agent opportunities. Identifies skills with verbose output, gaps in agent coverage, and model selection improvements.

UncategorizedID: laurigates/claude-plugins/agents-analyze

Repository

laurigates/claude-plugins
laurigatesLicense: MIT
234

Install this agent skill to your local

pnpm dlx add-skill https://github.com/laurigates/claude-plugins/tree/HEAD/agents-plugin/skills/agents-analyze

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agents-plugin/skills/agents-analyze/SKILL.md

Skill Metadata

Name
agents-analyze
Description
Analyze all plugins for sub-agent opportunities. Identifies skills with verbose output, gaps in agent coverage, and model selection improvements.

/agents:analyze

Analyze the plugin collection to identify where sub-agents would improve workflows by isolating verbose output, enforcing constraints, or specializing behavior.

Context

  • Plugin directories: !find . -maxdepth 1 -type d -name '*-plugin'
  • Existing agents: !find agents-plugin/agents -maxdepth 1 -name '*.md'
  • Skills: !find . -path '*/skills/*/skill.md'
  • Skills (user-invocable): !find . -path '*/skills/*/SKILL.md' -not -path './agents-plugin/*'

Parameters

  • $1: Optional --focus <plugin-name> to analyze a single plugin in depth

Your Task

Perform a systematic analysis of the plugin collection to identify sub-agent opportunities.

Step 1: Inventory Current State

Scan the repository to build an inventory:

  1. List all plugins with their skill/command counts
  2. Read existing agents in agents-plugin/agents/ to understand current coverage
  3. If --focus is provided, restrict analysis to that plugin only

Step 2: Identify Sub-Agent Opportunities

For each plugin (or focused plugin), evaluate skills and commands against these criteria:

Context Isolation (Primary Value)

Operations that produce verbose output benefiting from isolation:

| Indicator | Examples | |-----------|----------| | Build tools | docker build, cargo build, webpack, tsc | | Infrastructure ops | terraform plan/apply, kubectl describe | | Test runners | Full test suite output, coverage reports | | Profiling tools | Flame graphs, benchmark results | | Security scanners | Vulnerability reports, audit output | | Log analysis | Application logs, system logs | | Package managers | Dependency trees, audit results |

Constraint Enforcement

Operations that should be limited to specific tools:

| Constraint | Rationale | |------------|-----------| | Read-only analysis | Security audit, code review - no writes | | No network | Pure code analysis tasks | | Limited bash | Tasks that shouldn't execute arbitrary commands |

Model Selection Opportunities

| Assign opus when... | Assign haiku when... | |---------------------|----------------------| | Complex reasoning required | Structured/mechanical task | | Security analysis | Status checks | | Architecture decisions | Output formatting | | Debugging methodology | Configuration generation | | Performance analysis | File operations |

Step 3: Gap Analysis

Compare identified opportunities against existing agents:

  1. Missing agents: Skills that have no corresponding agent
  2. Model mismatches: Agents using wrong model for their task complexity
  3. Tool over-permissions: Agents with tools they don't need
  4. Consolidation opportunities: Multiple agents that could be merged
  5. Delegation mapping: Check if /delegate references agents that don't exist

Step 4: Produce Recommendations

For each recommended new agent, specify:

### Proposed: <agent-name>

- **Model**: opus | haiku
- **Covers plugins**: <list>
- **Context value**: <what verbose output it isolates>
- **Tools**: <minimal set>
- **Constraint**: <read-only, no-network, etc.>
- **Priority**: HIGH | MEDIUM | LOW
- **Rationale**: <why this is better than inline execution>

For model/tool corrections to existing agents:

### Fix: <agent-name>

- **Current model**: X → **Recommended**: Y
- **Reason**: <why the change improves things>

Step 5: Implementation Check

If new agents are recommended, check:

  • [ ] Agent name doesn't conflict with existing
  • [ ] Agent fills a gap referenced by /delegate command
  • [ ] Model selection follows haiku-for-mechanical, opus-for-reasoning
  • [ ] Tool set is minimal (principle of least privilege)
  • [ ] Agent has clear "does / does NOT do" boundaries

Output Format

## Sub-Agent Analysis Report

**Scope**: [All plugins | focused plugin name]
**Date**: [today]
**Plugins analyzed**: N
**Existing agents**: N
**Skills without agent coverage**: N

### Current Coverage Map

| Domain | Agent | Skills Covered | Gaps |
|--------|-------|----------------|------|
| ... | ... | ... | ... |

### Recommended New Agents

[Proposals from Step 4]

### Recommended Fixes

[Model/tool corrections from Step 4]

### Delegation Mapping Updates

[Any updates needed for /delegate command's agent reference table]

### Priority Summary

| Priority | Count | Top Recommendation |
|----------|-------|-------------------|
| HIGH | N | ... |
| MEDIUM | N | ... |
| LOW | N | ... |

Post-Actions

After presenting the analysis:

  1. Ask the user if they want to implement any of the recommendations
  2. If yes, create the agent files following the existing patterns in agents-plugin/agents/
  3. Update agents-plugin/README.md with new agents
  4. Update /delegate command's agent reference table if needed