deskcrew-support

deskcrew-support is a skill for Claude Code, Codex from webmilmind1/deskcrew-mcp. It costs 72 tokens per session (1,608 once invoked), scanned A, original, MIT.

A customer-support workflow that reads incoming tickets, drafts replies from a company's knowledge base, and sends them to a person for approval. A ticket is a recorded customer question or problem.

In plain words
What is it for?
Finding new tickets, reviewing their conversations, writing knowledge-base-based replies, and filing drafts for approval.
Why use it?
It prepares researched, appropriately worded answers while keeping a human in control of what reaches the customer.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Finding new tickets, reviewing their conversations, writing knowledge-base-based replies, and filing drafts for approval.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/webmilmind1/deskcrew-mcp/deskcrew-support
Install

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.

Any agent
npx skills add webmilmind1/deskcrew-mcp --skill deskcrew-support
Clone the repo
git clone --depth 1 https://github.com/webmilmind1/deskcrew-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for deskcrew-support

README.md
[![agentmods](https://agentmods.dev/badge/skills/webmilmind1/deskcrew-mcp/deskcrew-support/github.svg)](https://agentmods.dev/skills/webmilmind1/deskcrew-mcp/deskcrew-support)
Your own site
<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.

agentmods 80×15 button for deskcrew-support

Your own site · 80×15
<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>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,608 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 959cb2c8c123, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

skills/deskcrew-support/SKILL.md · 162 lines

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.

  1. Find work. list_tickets filtered to open tickets, oldest first. Skip any ticket whose last message is not from the customer; someone is already on it.
  2. Read it fully. get_ticket_context on 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.
  3. Research before writing. search_kb with the customer's own words, then again with the technical terms you inferred. Read the articles that come back.
  4. 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.
  5. Write the reply. Then draft_reply with it. State plainly in the draft when you are unsure about any part, so the reviewing human knows where to look.
  6. Move on. One ticket, one draft. Never batch-draft across tickets; context bleeds and you will answer the wrong customer.

Read the full file on GitHub · 162 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 162 lines · 72 tokens per session scan A 959cb2c8c123

Subscribe to this mod's changes

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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