v1.0.0 to v1.0.0

---

name: query-enhancer

display-name: Query Enhancer

description: RAG query optimization - rewrite, expand, decompose, and analyze search queries for better retrieval results

version: 1.0.0

category: knowledge

user-invocable: true

tags: [query, search, rag, rewrite, optimize, retrieve, semantic]

capabilities:

[

query-rewriting,

multi-query-expansion,

hyde-generation,

query-decomposition,

synonym-expansion,

intent-analysis,

]

tools:

- query_rewriter

- query_expander

- query_analyzer

os: [win32, darwin, linux]

handler: ./handler.js

instructions: |

Use this skill when the user wants to optimize search queries for RAG retrieval.

For --rewrite mode, generate multiple query variants using synonyms, rephrasing, and

contextual expansion. For --multi mode, generate 3-5 diverse search queries from a

single question. For --hyde mode, generate a hypothetical answer passage that would

match the ideal document (Hypothetical Document Embedding). For --decompose mode,

break a complex question into atomic sub-queries. For --expand mode, add related

terms, synonyms, and context keywords. For --analyze mode, classify query intent,

extract entities, and determine expected answer type. Always return structured output

ready for downstream retrieval pipelines.

examples:

- input: "/query-enhancer --rewrite How does the permission system handle delegation?"

output: "Variants: 1) 'permission delegation mechanism implementation', 2) 'RBAC delegate temporary access grant', 3) 'DelegationManager permission transfer workflow', 4) 'how are permissions delegated between users in the access control system'"

- input: "/query-enhancer --multi What causes memory leaks in Electron apps?"

output: "Queries: 1) 'Electron memory leak causes', 2) 'BrowserWindow garbage collection issues', 3) 'IPC handler memory retention patterns', 4) 'Node.js event listener leak detection Electron', 5) 'Chrome DevTools heap snapshot Electron debugging'"

- input: "/query-enhancer --hyde How does the hybrid search combine vector and BM25 results?"

output: "Hypothetical passage: 'The hybrid search engine combines vector similarity search (60% weight) with BM25 keyword matching (40% weight) using Reciprocal Rank Fusion (RRF). Vector search finds semantically similar documents via embeddings, while BM25 handles exact keyword matches. Results are merged by computing reciprocal ranks...'"

- input: "/query-enhancer --decompose How do I set up SSO with SAML and link it to my DID identity?"

output: "Sub-queries: 1) 'How to configure SAML 2.0 SSO provider', 2) 'SAML assertion parsing and signature verification', 3) 'DID identity linking with SSO accounts', 4) 'Identity bridge bidirectional lookup DID SSO'"

- input: "/query-enhancer --analyze What is the best way to optimize LLM token usage?"

output: "Intent: how-to/optimization. Entities: ['LLM', 'token usage']. Expected answer: procedural/tutorial. Query type: open-ended. Suggested expansions: ['context engineering', 'KV-cache', 'token compression', 'prompt optimization']."

input-schema:

type: object

properties:

action:

type: string

enum: [rewrite, multi, hyde, decompose, expand, analyze]

description: Query enhancement action

query:

type: string

description: The search query to enhance

output-schema:

type: object

properties:

original: { type: string }

action: { type: string }

variants: { type: array }

entities: { type: array }

intent: { type: string }

queryType: { type: string }

model-hints:

preferred: [claude-opus-4-6, gpt-4o]

cost: free

author: ChainlessChain

license: MIT

homepage: https://github.com/nicekid1/ChainlessChain

repository: https://github.com/nicekid1/ChainlessChain

---

# Query Enhancer Skill

Optimize search queries for better RAG retrieval results using NLP heuristics.

## Usage

```

/query-enhancer [action] "<query>"

```

## Actions

| Action | Flag | Description |

| ----------- | ------------- | ---------------------------------------------------------- |

| Rewrite | `--rewrite` | Generate multiple query variants with synonyms and context |

| Multi-Query | `--multi` | Expand one question into 3-5 diverse search queries |

| HyDE | `--hyde` | Generate a hypothetical answer passage for embedding |

| Decompose | `--decompose` | Break complex question into atomic sub-queries |

| Expand | `--expand` | Add related terms, synonyms, and context keywords |

| Analyze | `--analyze` | Classify intent, extract entities, determine answer type |

## How It Works

1. **Rewrite**: Applies synonym substitution, rephrasing, and contextual expansion to produce 3-4 variant queries that cover different retrieval angles

2. **Multi-Query**: Generates 3-5 semantically diverse queries targeting different facets of the original question

3. **HyDE**: Creates a hypothetical document passage that would answer the query, useful for embedding-based retrieval where the answer embedding is closer to relevant documents

4. **Decompose**: Splits compound questions into independent sub-queries that can be searched separately and results merged

5. **Expand**: Enriches the query with related terms from a built-in synonym map and detected domain context

6. **Analyze**: Detects query type (who/what/when/where/why/how), extracts named entities and quoted terms, and classifies expected answer format