skill-capability-matcher

# Skill Capability Matcher

Match natural language requirements to available skills, designing an optimal workflow step sequence.

## Purpose

Parse user's workflow description, understand their intent, and recommend which skills should handle each part of the workflow. Output includes step IDs, skill names, missions, and data flow.

## Process

### Step 1: Parse Requirements

Extract from the user's description:

**Input Types:**
- video transcript
- audio transcript
- article/blog post
- documentation
- raw text
- structured data (JSON/YAML)

**Processing Goals:**
- analyze (deep understanding)
- summarize (condensation)
- generate (create new content)
- transform (change format)
- extract (pull specific data)
- validate (check correctness)

**Output Format:**
- JSON structure
- Markdown document
- Summary text
- Structured data

### Step 2: Load Registry

Read the plugin registry from registry-loader output (v0.7.0):

```json
{
  "plugins": [...],
  "summary": { "totalSkills": N, "installedSkills": N, "availableSkills": N }
}
```

Build a capability index from skill descriptions and inferred capabilities.
Include `installed` status when scoring - prefer installed skills for immediate execution.

### Step 3: Match Capabilities

Score each skill by:
1. **Description match** - Does skill description align with requirements?
2. **Capability overlap** - Do inferred capabilities match processing goals?
3. **Input/output compatibility** - Can skill handle input type and produce expected output?

### Step 4: Design Step Sequence

For each matched skill:
1. Create a step ID (kebab-case, descriptive)
2. Determine dependencies (`needs:`)
3. Write a mission description (what to accomplish)
4. Define data flow (input → output)

### Step 5: Recommend Sequence

Order skills logically:
1. Analysis skills first (understand content)
2. Generation/transformation skills second
3. Assembly/output skills last

Ensure proper data dependencies.

### Step 6: Flag Gaps

If requirements ca