Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add frank-luongt/faos-skills-marketplace --skill enterprise-searchgit clone --depth 1 https://github.com/frank-luongt/faos-skills-marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/enterprise-search)<a href="https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/enterprise-search"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/enterprise-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/enterprise-search"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/enterprise-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02700 |
| Opus 5.5 | $0.00000 | $0.01080 |
| Sonnet 5.5 | $0.00000 | $0.00540 |
| Haiku 4.5 | $0.00000 | $0.00270 |
Grade A, and why
enterprise-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Audit current state — inventory all content sources, volumes, and access patterns.
- Choose architecture — federated, centralized, or hybrid based on your constraints.
- Design taxonomy — define metadata schema, facets, and tagging standards.
- Define relevance model — scoring factors, boosting rules, and personalization signals.
- Set quality metrics — establish baselines and targets for search quality.
- Design search UX — autocomplete, facets, snippets, and result presentation.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 249 lines · 0 tokens per session scan A ba7cdea2958b
enterprise-search is a skill published in the GitHub repository frank-luongt/faos-skills-marketplace (37 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,700 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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