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/search-strategyWrote 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-strategy)<a href="https://agentmods.dev/skills/sun2443/designer-skills/search-strategy"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/search-strategy/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-strategy"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/search-strategy.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 | $0.00043 | $0.01736 |
| Opus 5.5 | $0.00017 | $0.00694 |
| Sonnet 5.5 | $0.00009 | $0.00347 |
| Haiku 4.5 | $0.00004 | $0.00174 |
Grade A, and why
search-strategy 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-strategy — 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Strategy
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
The core intelligence behind enterprise search. Transforms a single natural language question into parallel, source-specific searches and produces ranked, deduplicated results.
The Goal
Turn this:
"What did we decide about the API migration timeline?"
Into targeted searches across every connected source:
~~chat: "API migration timeline decision" (semantic) + "API migration" in:#engineering after:2025-01-01
~~knowledge base: semantic search "API migration timeline decision"
~~project tracker: text search "API migration" in relevant workspace
Then synthesize the results into a single coherent answer.
Query Decomposition
Step 1: Identify Query Type
Classify the user's question to determine search strategy:
| Query Type | Example | Strategy |
|---|---|---|
| Decision | "What did we decide about X?" | Prioritize conversations (~~chat, email), look for conclusion signals |
| Status | "What's the status of Project Y?" | Prioritize recent activity, task trackers, status updates |
| Document | "Where's the spec for Z?" | Prioritize Drive, wiki, shared docs |
| Person | "Who's working on X?" | Search task assignments, message authors, doc collaborators |
| Factual | "What's our policy on X?" | Prioritize wiki, official docs, then confirmatory conversations |
| Temporal | "When did X happen?" | Search with broad date range, look for timestamps |
| Exploratory | "What do we know about X?" | Broad search across all sources, synthesize |
Step 2: Extract Search Components
From the query, extract:
- Keywords: Core terms that must appear in results
- Entities: People, projects, teams, tools (use memory system if available)
- Intent signals: Decision words, status words, temporal markers
- Constraints: Time ranges, source hints, author filters
- Negations: Things to exclude
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 · 223 lines · 43 tokens per session scan A 71d5ba2d219a
search-strategy is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,736 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-strategy, differing in 0 lines, and is treated as a copy.
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