wrps-wrapper-skill-design

name	wrps - Wrapper Skill Design
description	Guides the design of skills that enhance other skills by accepting their output, adding a layer of analysis or validation, and returning enriched results.
output	{"format":"prose"}

Wrapper Skill Design

Input: $ARGUMENTS

---

Step 1: Define What Gets Wrapped

A wrapper skill takes another skill's output and makes it better.

WRAPPER NAME: [abbreviation]
WRAPS: [what kind of skill output it enhances]
ADDS: [what new layer it provides]
PRESERVES: [what it keeps unchanged from the original]

Wrapper Type	What it adds	Example
Validator	Checks the output for errors	"Verify all claims have evidence"
Enricher	Adds missing information	"Add confidence scores to each item"
Challenger	Stress-tests the output	"Find the weakest point in the analysis"
Formatter	Restructures for a new audience	"Turn analysis into executive summary"
Synthesizer	Combines outputs from multiple skills	"Merge perspectives into unified view"

---

Step 2: Define the Wrapping Contract

Specify what goes in and what comes out.

WRAPPING CONTRACT:
- Accepts: [types of skill output this wrapper can handle]
- Minimum input: [what must be present for the wrapper to work]
- Rejects: [what it can't wrap — be explicit]

OUTPUT:
- Original output: [preserved in full / summarized / referenced]
- Added layer: [what the wrapper contributes]
- Combined format: [how original + new layer are presented]

Rule: The original output must always be recoverable. A wrapper adds — it never destroys.

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Step 3: Design the Enhancement Layer

Define what the wrapper does to the wrapped output.

ENHANCEMENT STEPS:
1. RECEIVE: [accept the wrapped skill's output]
2. PARSE: [identify the key elements to enhance]
3. ANALYZE: [apply the wrapper's specific lens]
   - [sub-analysis 1]
   - [sub-analysis 2]
   - [sub-analysis 3]
4. ANNOTATE: [attach findings to the original output]
5. DELIVER: [present original + annotations together]

Constraint: Steps 2-4 should be completable without re-running the wrapped skill.

---

Step 4: Handle Multiple Wrapped Skills

A good wrapper works with many different skill outputs.

COMPATIBILITY MATRIX:
| Wrapped Skill | What Wrapper Adds | Works Well? |
|--------------|-------------------|-------------|
| [skill 1] | [specific enhancement] | [yes/partial/no] |
| [skill 2] | [specific enhancement] | [yes/partial/no] |
| [skill 3] | [specific enhancement] | [yes/partial/no] |
| [skill 4] | [specific enhancement] | [yes/partial/no] |

If compatibility is "partial," note what's missing. If compatibility is "no," the wrapper scope may be too narrow.

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Step 5: Preserve and Append

Design the output format that shows original and enhancement clearly.

OUTPUT FORMAT:

## Original Output
[The wrapped skill's complete output, unchanged]

## [Wrapper Name] Enhancement
[The wrapper's added analysis/validation/enrichment]

### Key Findings
- [finding 1]
- [finding 2]
- [finding 3]

### Recommendations
- [action 1 based on wrapper analysis]
- [action 2 based on wrapper analysis]

Rule: A reader should be able to ignore the enhancement and still use the original output.

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Step 6: Test With Multiple Skills

Verify the wrapper adds value across different skill outputs.

WRAP TEST 1:
- Wrapped skill: [name]
- Original output quality: [assessment]
- After wrapping: [what improved]
- Value added: [concrete benefit]

WRAP TEST 2:
- Wrapped skill: [name]
- Original output quality: [assessment]
- After wrapping: [what improved]
- Value added: [concrete benefit]

WRAP TEST 3:
- Wrapped skill: [name]
- Original output quality: [assessment]
- After wrapping: [what improved]
- Value added: [concrete benefit]

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Integration

Use with:

• /chns -> Design the wrapper as a chainable post-processing step
• /injc -> Consider if the wrapper could also work as a lightweight injectable
• /stnl -> Ensure the wrapper is also useful when given raw input (not just skill output)