search

A search command that looks across all connected company sources in one query and combines the relevant results. These sources can include chat, email, cloud storage, project trackers, customer records, and knowledge bases.

In plain words
What is it for?
Use it to find decisions, documents, people, project updates, customer information, or conversations across connected tools.
Why use it?
It removes the need to repeat the same search separately in every connected system and helps bring related information together.

Command

Install

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.

agentmods
npx agentmods add commands/fergupa/claude_plugins/search
Clone the repo
git clone --depth 1 https://github.com/fergupa/claude_plugins
Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,418 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00008 $0.01418
Opus 5 $0.00004 $0.00709
Sonnet 5 $0.00002 $0.00284
Haiku 4.5 $0.00001 $0.00142

Measured yesterday against content hash d6f518da7d0c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 yesterday.

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.

Origin

This is a copy

100% identical to search — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

enterprise-search/commands/search.md · 178 lines

How it starts

The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Search Command

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Search across all connected MCP sources in a single query. Decompose the user's question, run parallel searches, and synthesize results.

Instructions

1. Check Available Sources

Before searching, determine which MCP sources are available. Attempt to identify connected tools from the available tool list. Common sources:

  • ~~chat — chat platform tools
  • ~~email — email tools
  • ~~cloud storage — cloud storage tools
  • ~~project tracker — project tracking tools
  • ~~CRM — CRM tools
  • ~~knowledge base — knowledge base tools

If no MCP sources are connected:

To search across your tools, you'll need to connect at least one source.
Check your MCP settings to add ~~chat, ~~email, ~~cloud storage, or other tools.

Supported sources: ~~chat, ~~email, ~~cloud storage, ~~project tracker, ~~CRM, ~~knowledge base,
and any other MCP-connected service.

2. Parse the User's Query

Analyze the search query to understand:

  • Intent: What is the user looking for? (a decision, a document, a person, a status update, a conversation)
  • Entities: People, projects, teams, tools mentioned
  • Time constraints: Recency signals ("this week", "last month", specific dates)
  • Source hints: References to specific tools ("in ~~chat", "that email", "the doc")
  • Filters: Extract explicit filters from the query:
    • from: — Filter by sender/author
    • in: — Filter by channel, folder, or location
    • after: — Only results after this date
    • before: — Only results before this date
    • type: — Filter by content type (message, email, doc, thread, file)

3. Decompose into Sub-Queries

For each available source, create a targeted sub-query using that source's native search syntax:

~~chat:

  • Use available search and read tools for your chat platform
  • Translate filters: from: maps to sender, in: maps to channel/room, dates map to time range filters
  • Use natural language queries for semantic search when appropriate
  • Use keyword queries for exact matches

Read the full file on GitHub · 178 lines

Changes

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.

  1. yesterday First seen · 178 lines · 8 tokens per session scan A d6f518da7d0c

Subscribe to this mod's changes

search is a command published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 8 tokens to every session and 1,418 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to search, differing in 0 lines, and is treated as a copy.