Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sun2443/designer-skillsnpx agentmods add skills/sun2443/designer-skills/searchWrote 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/sun2443/designer-skills/search)<a href="https://agentmods.dev/skills/sun2443/designer-skills/search"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/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/sun2443/designer-skills/search"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/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 | $0.00060 | $0.01471 |
| Opus 5.5 | $0.00024 | $0.00588 |
| Sonnet 5.5 | $0.00012 | $0.00294 |
| Haiku 4.5 | $0.00006 | $0.00147 |
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 13d 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.
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.
How it starts
The opening of the file, as written. The whole thing — 179 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/authorin:— Filter by channel, folder, or locationafter:— Only results after this datebefore:— Only results before this datetype:— 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
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.
- 13d ago First seen · 179 lines · 60 tokens per session scan A 5330512ac1f7
search is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 1,471 once invoked, about $0.0002 per session on Opus 5.5. 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.
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