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 agentmods add skills/orchidautomation/pluxx/client-intelnpx skills add orchidautomation/pluxx --skill client-intelgit clone --depth 1 https://github.com/orchidautomation/pluxxWrote 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/orchidautomation/pluxx/client-intel)<a href="https://agentmods.dev/skills/orchidautomation/pluxx/client-intel"><img src="https://agentmods.dev/badge/skills/orchidautomation/pluxx/client-intel.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.00358 |
| Opus 5 | $0.00026 | $0.00179 |
| Sonnet 5 | $0.00011 | $0.00072 |
| Haiku 4.5 | $0.00005 | $0.00036 |
Grade A, and why
client-intel 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 5d 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
Client Intelligence Skill
Uses Megamind MCP tools to answer client questions from synced Slack and Fathom data.
Trigger Phrases
- "What's going on with [client]?"
- "Catch me up on [client]"
- "Any updates from [person]?"
- "What did we discuss with [client]?"
- "Search for [topic] in [client] messages"
Tool Selection
| Question Type | Megamind Tool | Follow-up |
|---|---|---|
| Status overview | get_client_pulse(client_id) |
Fetch transcripts + messages in parallel |
| Specific topic | search_messages(query) or search_calls(query) |
Read results |
| Person info | get_people(client_id) |
Contact details + engagement |
| Call content | get_call_transcript(call_id) |
Full transcript |
Standard Workflow
For broad client questions ("what's going on", "catch me up"):
get_client_pulse(client_id)— always start here for the index- In PARALLEL:
get_call_transcript(call_id)for EACH call ID returnedget_messages(client_id, days)for full Slack history
- Synthesize all data, then answer the question
Client IDs
Client IDs are case-insensitive slugs. Common ones: sendoso, cognition, gates.
If unsure, call list_clients() to discover available clients.
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.
- 5d ago First seen · 41 lines · 53 tokens per session scan A 88361ff16c40
client-intel is a skill published in the GitHub repository orchidautomation/pluxx (5 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 358 once invoked, about $0.0003 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.
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