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 ZhixiangLuo/10xProductivity --skill enterprise-searchgit clone --depth 1 https://github.com/ZhixiangLuo/10xProductivityWrote 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/zhixiangluo/10xproductivity/enterprise-search)<a href="https://agentmods.dev/skills/zhixiangluo/10xproductivity/enterprise-search"><img src="https://agentmods.dev/badge/skills/zhixiangluo/10xproductivity/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/zhixiangluo/10xproductivity/enterprise-search"><img src="https://agentmods.dev/badge/skills/zhixiangluo/10xproductivity/enterprise-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00136 | $0.00491 |
| Opus 5 | $0.00068 | $0.00246 |
| Sonnet 5 | $0.00027 | $0.00098 |
| Haiku 4.5 | $0.00014 | $0.00049 |
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 11d 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.
What it actually says
Enterprise Search
One question. Every connected tool. One synthesized answer.
Search your company's knowledge across Slack, Confluence, Jira, Linear, Notion, GitHub, and more — simultaneously, in a single query.
Before you search: check connection status
ls verified_connections.md 2>/dev/null && grep -c "^##" verified_connections.md || echo "0"
- 1+ tools connected → read
verified_connections.md(capability index), then followworkflows/enterprise-search/enterprise-search.mdfor the search. That workflow always uses Slack + Confluence when connected, may include AI-synthesized search tools listed only inverified_connections.md(per each connection file), and adds Jira/Linear/Notion/GitHub when relevant. - Not connected / empty → read
setup.md. Your agent handles the full connection flow — credentials, SSO, verification — in one session. ~5 minutes per tool.
What you can ask
- "Search for the discussion about the database migration"
- "Find any Jira tickets related to the login timeout bug"
- "What was the decision about the API versioning approach?"
- "Is there a Confluence runbook for on-call handoffs?"
- "Who worked on the payments refactor and what did they decide?"
The search workflow handles routing, parallel execution across tools, and synthesized results. You don't need to specify which tool — the agent figures that out.
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.
- 11d ago First seen · 44 lines · 136 tokens per session scan A f45633f1a2b1
enterprise-search is a skill published in the GitHub repository ZhixiangLuo/10xProductivity (474 stars, last pushed 2mo ago), licensed MIT. It adds 136 tokens to every session and 491 once invoked, about $0.0007 per session on Opus 5. 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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