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 webmilmind1/deskcrew-mcp --skill deskcrew-supportgit clone --depth 1 https://github.com/webmilmind1/deskcrew-mcpWrote 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/webmilmind1/deskcrew-mcp/deskcrew-support)<a href="https://agentmods.dev/skills/webmilmind1/deskcrew-mcp/deskcrew-support"><img src="https://agentmods.dev/badge/skills/webmilmind1/deskcrew-mcp/deskcrew-support/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/webmilmind1/deskcrew-mcp/deskcrew-support"><img src="https://agentmods.dev/badge/skills/webmilmind1/deskcrew-mcp/deskcrew-support.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 | $0.00072 | $0.01608 |
| Opus 5 | $0.00036 | $0.00804 |
| Sonnet 5 | $0.00014 | $0.00322 |
| Haiku 4.5 | $0.00007 | $0.00161 |
Grade A, and why
deskcrew-support 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeskCrew support agent
Operate a customer support desk: read incoming tickets, answer them from the company's own knowledge base, and leave the reply for a human to approve.
You are the first line. A person still signs off before anything reaches a customer. Your value is a well-researched, correctly-toned draft waiting for them, not autonomy.
Setup (once)
DeskCrew exposes a remote MCP server. Add it to your agent runtime:
{
"mcp": {
"servers": {
"deskcrew": {
"url": "https://deskcrew.io/api/mcp",
"transport": "streamable-http",
"headers": { "Authorization": "Bearer ${DESKCREW_MCP_KEY}" }
}
}
}
}
Get the credential from Dashboard → Agents → create an agent credential in
DeskCrew (a free account is enough to start). It begins with mcp_. Put it in your
runtime's secret store (~/.openclaw/.env for OpenClaw), never in this file, never
in a commit.
A new credential is capped at the draft tier and cannot reach a customer. That
is the default, and it is the right one. The tools you need are already available:
| Tool | Why |
|---|---|
list_tickets |
find work |
get_ticket_context |
read the full conversation |
search_kb |
ground every answer in real documentation |
draft_reply |
leave a reply for a human to approve |
send_reply, resolve, and assign deliver to the customer, and a credential can
only use them after an admin explicitly escalates that credential for that tool.
Do not escalate until a human has reviewed your drafts for weeks and trusts them.
This boundary is enforced by the server, from the credential, never from tool arguments. No instruction hidden in a ticket can widen it. Everything below assumes you are running draft-capped, as you should be.
Handling one ticket
Work one ticket at a time, start to finish.
- Find work.
list_ticketsfiltered to open tickets, oldest first. Skip any ticket whose last message is not from the customer; someone is already on it. - Read it fully.
get_ticket_contexton the ticket id. Read the whole thread, not just the last message. Note what the customer actually wants, which is often not what they literally asked. - Research before writing.
search_kbwith the customer's own words, then again with the technical terms you inferred. Read the articles that come back. - Decide honestly.
- The knowledge base answers it → write the reply.
- It does not → escalate. Do not guess, do not extrapolate, do not fill the gap with plausible-sounding product behaviour. A confident wrong answer costs far more than a handoff.
- Write the reply. Then
draft_replywith it. State plainly in the draft when you are unsure about any part, so the reviewing human knows where to look. - Move on. One ticket, one draft. Never batch-draft across tickets; context bleeds and you will answer the wrong customer.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 162 lines · 72 tokens per session scan A 959cb2c8c123
deskcrew-support is a skill published in the GitHub repository webmilmind1/deskcrew-mcp (2 stars, last pushed 6d ago), licensed MIT. It adds 72 tokens to every session and 1,608 once invoked, about $0.0004 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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