# Enterprise Search

> A Claude skill from frank-luongt/faos-skills-marketplace.

- **Type:** Skill
- **Install:** `agentstack add skill-frank-luongt-faos-skills-marketplace-enterprise-search`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [frank-luongt](https://agentstack.voostack.com/s/frank-luongt)
- **Installs:** 0
- **Category:** [Developer Tools](https://agentstack.voostack.com/c/developer-tools)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [frank-luongt](https://github.com/frank-luongt)
- **Source:** https://github.com/frank-luongt/faos-skills-marketplace/tree/main/plugins/faos-architect/skills/enterprise-search
- **Website:** https://faosx.ai/skills

## Install

```sh
agentstack add skill-frank-luongt-faos-skills-marketplace-enterprise-search
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

---
name: enterprise-search
description: Design cross-tool knowledge retrieval strategies, architect enterprise search systems, and tune relevance models. Use when building internal search experiences, consolidating knowledge across tools, or improving search result quality.
tags: [search, knowledge-management, information-retrieval]
---

# Enterprise Search

Business-oriented framework for designing cross-tool knowledge retrieval, architecting enterprise search systems, and tuning relevance models. Focused on strategy and requirements — for technical implementation, see `hybrid-search-implementation` and `similarity-search-patterns` in the ai-ml domain.

## Use this skill when

- Designing an enterprise search strategy across multiple internal tools (Confluence, Slack, Drive, SharePoint, GitHub)
- Choosing between federated, centralized, or hybrid search architectures
- Defining relevance tuning requirements and quality metrics
- Building a knowledge taxonomy or metadata schema for searchable content
- Creating search UX requirements for internal portals
- Evaluating search quality and measuring improvement

## Do not use this skill when

- Implementing vector search or embeddings at code level (use `hybrid-search-implementation`)
- Building similarity search with specific vector databases (use `similarity-search-patterns`)
- Optimizing web SEO for external search engines (use `seo-audit`)
- Building RAG pipelines for LLM applications (use RAG skills in ai-ml domain)

## Instructions

1. **Audit current state** — inventory all content sources, volumes, and access patterns.
2. **Choose architecture** — federated, centralized, or hybrid based on your constraints.
3. **Design taxonomy** — define metadata schema, facets, and tagging standards.
4. **Define relevance model** — scoring factors, boosting rules, and personalization signals.
5. **Set quality metrics** — establish baselines and targets for search quality.
6. **Design search UX** — autocomplete, facets, snippets, and result presentation.

---

## Search Architecture Patterns

### Architecture Comparison

| Pattern | How It Works | Pros | Cons | Best For |
|---------|-------------|------|------|----------|
| **Federated** | Query multiple sources in real-time, merge results | No data duplication, real-time freshness | Slower, limited cross-source ranking | Small orgs (6 months | Flag for review |
| Meeting notes | Permanent | N/A | Reduce ranking weight over time |
| Code / PRs | Always current (live sync) | N/A | N/A |
| Tickets / Issues | Live sync | N/A | Archive closed items after 12 months |
| Policies / Runbooks | Updated semi-annually | >12 months | Alert content owner |

---

## Relevance Tuning Framework

### Scoring Factors

| Factor | Weight | Description |
|--------|--------|-------------|
| **Text relevance (BM25)** | 40% | Keyword match quality — title, body, tags |
| **Freshness** | 20% | More recent content ranked higher (decay function) |
| **Popularity** | 15% | View count, link count, citation count |
| **Personalization** | 15% | User's team, recent searches, frequently accessed sources |
| **Source authority** | 10% | Official docs > Slack messages > personal notes |

### Field Boosting

| Field | Boost Factor | Rationale |
|-------|-------------|-----------|
| Title | 3.0x | Titles are the strongest relevance signal |
| Tags | 2.0x | Curated metadata is high-signal |
| Headings (H1-H3) | 1.5x | Section headers indicate topic boundaries |
| Body text | 1.0x | Baseline — full content match |
| Comments | 0.5x | Noisy, often tangential |

### Query Understanding

| Technique | Purpose | Example |
|-----------|---------|---------|
| **Synonym expansion** | Match equivalent terms | "deploy" → "deploy, release, ship" |
| **Spell correction** | Handle typos | "kuberntes" → "kubernetes" |
| **Intent classification** | Route to specialized search | "how do I deploy" → tutorial filter |
| **Entity recognition** | Boost specific entities | "John's PR for auth" → person + code filter |

---

## Search Quality Metrics

### Core Metrics

| Metric | Formula | Target | How to Measure |
|--------|---------|--------|---------------|
| **MRR (Mean Reciprocal Rank)** | Average of 1/rank of first relevant result | >0.6 | Relevance judgments on sample queries |
| **NDCG@10** | Normalized discounted cumulative gain at position 10 | >0.7 | Graded relevance judgments |
| **Precision@5** | % of top 5 results that are relevant | >60% | Binary relevance judgments |
| **Zero-Result Rate** | % of queries returning no results | 40% | Click tracking |
| **Query Reformulation Rate** | % of searches followed by a refined query | 80% |

---

## Output Template: Enterprise Search Requirements Document

```markdown
# Enterprise Search Requirements — [Project Name]

## Current State
- **Content sources:** [list with estimated volumes]
- **Current search tools:** [what people use today]
- **Top pain points:** [from user interviews]

## Architecture Decision
- **Pattern:** [Federated / Centralized / Hybrid]
- **Rationale:** [why this pattern]
- **Search platform:** [Elasticsearch, Typesense, Algolia, Vespa, etc.]

## Scope (Phase 1)
- **Sources to index:** [list with priority]
- **Content types:** [documents, conversations, code, tickets]
- **Users:** [target audience and access model]

## Relevance Model
- **Scoring factors:** [weights per factor]
- **Field boosting:** [title, tags, headings, body]
- **Freshness decay:** [function and parameters]

## Quality Targets
| Metric | Baseline | Target |
|--------|----------|--------|
| MRR | [current] | [goal] |
| Zero-result rate | [current] | <5% |
| p95 latency | [current] | <1s |

## Roadmap
- Phase 1: [Core sources, basic search] — [timeline]
- Phase 2: [Additional sources, relevance tuning] — [timeline]
- Phase 3: [Personalization, AI-powered features] — [timeline]
```

---

## Common Mistakes

- **Indexing everything without curation** — more content does not mean better search; noisy sources dilute quality
- **Ignoring access control** — leaking confidential documents through search is a security incident
- **No freshness weighting** — returning 3-year-old docs before this week's update frustrates users
- **Not measuring search quality** — if you don't measure MRR/NDCG, you can't improve
- **Building search without user research** — understand what people actually search for before designing the system
- **Treating search as a one-time project** — relevance tuning is ongoing; plan for continuous improvement

---

## Additional Resources

- Related skills: `hybrid-search-implementation` (ai-ml — technical implementation), `similarity-search-patterns` (ai-ml — vector search)
- Elasticsearch / OpenSearch — open-source search engines
- Algolia — managed search platform
- Vespa — open-source search and recommendation engine

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [frank-luongt](https://github.com/frank-luongt)
- **Source:** [frank-luongt/faos-skills-marketplace](https://github.com/frank-luongt/faos-skills-marketplace)
- **License:** Apache-2.0
- **Homepage:** https://faosx.ai/skills

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-frank-luongt-faos-skills-marketplace-enterprise-search
- Seller: https://agentstack.voostack.com/s/frank-luongt
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.