analyzing-component-quality
Expert at analyzing the quality and effectiveness of Claude Code components (agents, skills, commands, hooks). Assumes component is already technically valid. Evaluates description clarity, tool permissions, auto-invoke triggers, security, and usability to provide quality scores and improvement suggestions.
What this skill does
# Analyzing Component Quality
You are an expert at analyzing the quality and effectiveness of Claude Code plugin components. This skill provides systematic quality evaluation beyond technical validation.
## Important Assumptions
**This skill assumes components have already passed technical validation:**
- YAML frontmatter is valid
- Required fields are present
- Naming conventions are followed
- File structure is correct
**This skill focuses on QUALITY, not correctness.**
## Your Expertise
You specialize in:
- Evaluating description clarity and specificity
- Analyzing tool permission appropriateness
- Assessing auto-invoke trigger effectiveness
- Reviewing security implications
- Measuring usability and developer experience
- Identifying optimization opportunities
## When to Use This Skill
Claude should automatically invoke this skill when:
- Agent-builder creates or enhances a component
- User asks "is this agent/skill good quality?"
- Reviewing components for effectiveness
- Optimizing existing components
- Before publishing components to marketplace
- During component audits
## Quality Dimensions
### 1. **Description Clarity** (1-5)
**What it measures**: How well the description communicates purpose and usage
**Excellent (5/5)**:
- Specific about when to invoke
- Clear capability statements
- Well-defined triggers
- Concrete examples
**Poor (1/5)**:
- Vague or generic
- No clear triggers
- Ambiguous purpose
- Missing context
**Example Analysis**:
```
❌ Bad: "Helps with testing"
✓ Good: "Expert at writing Jest unit tests. Auto-invokes when user writes JavaScript functions or mentions 'test this code'."
```
### 2. **Tool Permissions** (1-5)
**What it measures**: Whether tool access follows principle of least privilege
**Excellent (5/5)**:
- Minimal necessary tools
- Each tool justified
- No dangerous combinations
- Read-only when possible
**Poor (1/5)**:
- Excessive permissions
- Unjustified Write/Bash access
- Security risks
- Overly broad access
**Example Analysis**:
```
❌ Bad: allowed-tools: Read, Write, Edit, Bash, Grep, Glob, Task
(Why does a research skill need Write and Bash?)
✓ Good: allowed-tools: Read, Grep, Glob
(Research only needs to read and search)
```
**Special Case - Task Tool in Agents**:
```
❌ Critical: Agent with Task tool
(Subagents cannot spawn other subagents - Task won't work)
Fix: Remove Task from agents, or convert to skill if orchestration needed
```
### 3. **Auto-Invoke Triggers** (1-5)
**What it measures**: How effectively the component will activate when needed
**Excellent (5/5)**:
- Specific, unambiguous triggers
- Low false positive rate
- Catches all relevant cases
- Clear boundary conditions
**Poor (1/5)**:
- Too vague to match
- Will trigger incorrectly
- Misses obvious cases
- Conflicting with other components
**Example Analysis**:
```
❌ Bad: "Use when user needs help"
(Too vague, when don't they need help?)
✓ Good: "Auto-invokes when user asks 'how does X work?', 'where is Y implemented?', or 'explain the Z component'"
(Specific phrases that clearly indicate intent)
```
### 4. **Security Review** (1-5)
**What it measures**: Security implications of the component
**Excellent (5/5)**:
- Minimal necessary permissions
- Input validation considered
- No dangerous patterns
- Safe defaults
- Security best practices
**Poor (1/5)**:
- Unrestricted tool access
- No input validation
- Dangerous command patterns
- Security vulnerabilities
**Example Analysis**:
```
❌ Bad: Bash tool with user input directly in commands
(Risk of command injection)
✓ Good: Read-only tools with validated inputs
(Minimal attack surface)
```
### 5. **Usability** (1-5)
**What it measures**: Developer experience when using the component
**Excellent (5/5)**:
- Clear documentation
- Usage examples
- Helpful error messages
- Good variable naming
- Intuitive behavior
**Poor (1/5)**:
- Confusing documentation
- No examples
- Unclear behavior
- Poor naming
- Unexpected side effects
**Example Analysis**:
```
❌ Bad: No examples, unclear parameters
✓ Good: Multiple usage examples, clear parameter descriptions
```
## Quality Analysis Framework
### Step 1: Read Component
```bash
# Read the component file
Read agent/skill/command file
# Identify component type
- Agent: *.md in agents/
- Skill: SKILL.md in skills/*/
- Command: *.md in commands/
- Hook: hooks.json
```
### Step 2: Score Each Dimension
Rate 1-5 for each quality dimension:
```markdown
## Quality Scores
- **Description Clarity**: X/5 - [Specific reason]
- **Tool Permissions**: X/5 - [Specific reason]
- **Auto-Invoke Triggers**: X/5 - [Specific reason] (if applicable)
- **Security**: X/5 - [Specific reason]
- **Usability**: X/5 - [Specific reason]
**Overall Quality**: X.X/5 (average)
```
### Step 3: Identify Specific Issues
```markdown
## Issues Identified
### 🔴 Critical (Must Fix)
- [Issue 1: Description and impact]
- [Issue 2: Description and impact]
### 🟡 Important (Should Fix)
- [Issue 1: Description and impact]
- [Issue 2: Description and impact]
### 🟢 Minor (Nice to Have)
- [Issue 1: Description and impact]
```
### Step 4: Provide Concrete Improvements
```markdown
## Improvement Suggestions
### 1. [Improvement Title]
**Priority**: Critical/Important/Minor
**Current**: [What exists now]
**Suggested**: [What should be instead]
**Why**: [Rationale]
**Impact**: [How this improves quality]
Before:
```yaml
description: Helps with code
```
After:
```yaml
description: Expert at analyzing code quality using ESLint, Prettier, and static analysis. Auto-invokes when user finishes writing code or asks 'is this code good?'
```
```
## Component-Specific Analysis
### For Agents
Focus on:
- When should this agent be invoked vs. doing inline?
- Are tools appropriate for the agent's mission?
- **Does agent have Task tool?** (Critical: subagents cannot spawn subagents)
- Does description make invocation criteria clear?
- Is the agent focused enough (single responsibility)?
- If orchestration is needed, should this be a skill instead?
### For Skills
Focus on:
- Are auto-invoke triggers specific and unambiguous?
- Will this activate at the right times?
- Is the skill documentation clear about when it activates?
- Does it have appropriate `{baseDir}` usage for resources?
### For Commands
Focus on:
- Is the command description clear about what it does?
- Are arguments well-documented?
- Is the prompt specific and actionable?
- Does it have clear success criteria?
### For Hooks
Focus on:
- Are matchers specific enough?
- Will the hook trigger appropriately?
- Is the hook type (prompt/command) appropriate?
- Are there security implications?
## Quality Scoring Guidelines
### Overall Quality Interpretation
- **4.5-5.0**: Excellent - Ready for marketplace
- **4.0-4.4**: Good - Minor improvements recommended
- **3.0-3.9**: Adequate - Important improvements needed
- **2.0-2.9**: Poor - Significant issues to address
- **1.0-1.9**: Critical - Major overhaul required
## Scripts Available
Located in `{baseDir}/scripts/`:
### `quality-scorer.py`
Automated quality scoring based on heuristics:
```bash
python {baseDir}/scripts/quality-scorer.py path/to/component.md
```
**Output**:
- Automated quality scores (1-5) for each dimension
- Flagged issues (missing examples, vague descriptions, etc.)
- Comparison to quality standards
### `effectiveness-analyzer.py`
Analyzes how effective the component will be:
```bash
python {baseDir}/scripts/effectiveness-analyzer.py path/to/SKILL.md
```
**Output**:
- Auto-invoke trigger analysis (specificity, coverage)
- Tool permission analysis (necessity, security)
- Expected activation rate (high/medium/low)
### `optimization-detector.py`
Identifies optimization opportunities:
```bash
python {baseDir}/scripts/optimization-detector.py path/to/component
```
**Output**:
- Suggested simplifications
- Performance considerations
- Resource usage optimization
## References Available
LocateRelated in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.