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.
git clone --depth 1 https://github.com/ketankhairnar/ai-sales-team-publicWrote 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/commands/ketankhairnar/ai-sales-team-public/linkedin-intel-search)<a href="https://agentmods.dev/commands/ketankhairnar/ai-sales-team-public/linkedin-intel-search"><img src="https://agentmods.dev/badge/commands/ketankhairnar/ai-sales-team-public/linkedin-intel-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/commands/ketankhairnar/ai-sales-team-public/linkedin-intel-search"><img src="https://agentmods.dev/badge/commands/ketankhairnar/ai-sales-team-public/linkedin-intel-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 | $0.00015 | $0.00094 |
| Opus 5 | $0.00008 | $0.00047 |
| Sonnet 5 | $0.00003 | $0.00019 |
| Haiku 4.5 | $0.00002 | $0.00009 |
Grade A, and why
linkedin-intel: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 12d 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
linkedin-intel:search — Not Yet Implemented
This mode is planned but not yet built.
When implemented, it will cover:
- People search by keywords/title/company
- Content search by topic
- Search result analysis and clustering
To request implementation, describe your use case to help prioritize.
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.
- 12d ago First seen · 17 lines · 15 tokens per session scan A f1801f1ec27b
linkedin-intel:search is a command published in the GitHub repository ketankhairnar/ai-sales-team-public (2 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 94 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
bulk
Batch API processing with 50% cost savings.
help
Quick reference for all Attune commands.
spec
Spec-driven development — brainstorm, plan, review, and execute with quality gates and approval.
analyze
Analyze Claude Code transcripts to surface subagent behavior and improvement opportunities.
doc-gen
Generate documentation from source code — docstrings, README sections, API references.
fix-test
Auto-diagnose and fix failing tests — identifies root causes and applies fixes.