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 nota-america/forgecat-agent-profiles --skill search-strategygit clone --depth 1 https://github.com/nota-america/forgecat-agent-profilesWrote 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/nota-america/forgecat-agent-profiles/search-strategy)<a href="https://agentmods.dev/skills/nota-america/forgecat-agent-profiles/search-strategy"><img src="https://agentmods.dev/badge/skills/nota-america/forgecat-agent-profiles/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/nota-america/forgecat-agent-profiles/search-strategy"><img src="https://agentmods.dev/badge/skills/nota-america/forgecat-agent-profiles/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 | $0.01762 |
| Opus 5 | $0.00022 | $0.00881 |
| Sonnet 5 | $0.00009 | $0.00352 |
| Haiku 4.5 | $0.00004 | $0.00176 |
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 8d 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
98% identical to search-strategy — 7 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 — 226 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 (
.forgecat/profiles/@forgecat/anthropics_knowledge-work-plugins_enterprise-search/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.
- 8d ago First seen · 226 lines · 43 tokens per session scan A b9b7dc391860
search-strategy is a skill published in the GitHub repository nota-america/forgecat-agent-profiles (66 stars, last pushed yesterday), licensed Apache-2.0. It adds 43 tokens to every session and 1,762 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to search-strategy, differing in 7 lines, and is treated as a copy.
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