Ops Desk logo

Ops Desk

Organization
Lifecycle-Innovations-Limited
ops-desk

OPS on-demand: This skill should be used when the user asks to "desk sweep", "open decisions", or…

Overview

PublisherLifecycle-Innovations-Limited
Repositoryclaude-ops
Skill nameops-desk
Stars
543
Forks
64
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Lifecycle-Innovations-Limited on GitHub. Read the source before you install it.

Installation

Install the Ops Desk AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-desk
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Lifecycle-Innovations-Limited/claude-ops.git /tmp/claude-ops
mkdir -p .claude/skills
cp -r /tmp/claude-ops/claude-ops/skills/ops-desk .claude/skills/ops-desk
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ops Desk in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ops Desk on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ops Desk is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OPS ► DESK SWEEP

Load ops-rules before acting. Public repo (no personal data). Outbound: one draft → one approval → one send. If AskUserQuestion / Workflow are missing, follow Rule 10 in ops-rules (Hermes: numbered options / two-turn Telegram card; delegate_task).

Goal: everything sitting on the owner's desk — pending decisions, staged drafts awaiting approval, unpaid invoices to chase, documents to sign, blocked issues needing an owner action — becomes a ranked queue of ready-to-approve action packages, then gets worked down one item at a time. The success metric is the same spirit as inbox-zero: an empty desk, with every remaining item either executed, decided, or parked with an explicit reason and a reminder.

When to use ops-desk vs ops-inbox

  • /ops:ops-inbox — fresh inbound comms (who wrote me, what needs a reply).
  • /ops:ops-desk — the standing backlog (what am I blocking, what decision is waiting on me, what draft is staged, what must I sign/pay/approve). Run it after an inbox pass, or whenever the owner says "what's left / get this off my desk".

Step 1 — Build the item list (unless passed explicitly in ``)

  1. Task tracker (SSOT). Whichever the box uses, in order of preference:
    • Paperclip — paperclip issue list -C <company-id> for each company in ${CLAUDE_PLUGIN_DATA_DIR}/preferences.json → paperclip.companies[], falling back to paperclip company list. Keep status=blocked|todo|in_progress issues whose title/description implies an OWNER action — matcher: decision, owner decision, approve, sign, pay, chase, staged, awaiting owner, Rule-6.
    • Linear — mcp__linear__list_issues filtered to the owner as assignee, when Paperclip is absent.
    • GitHub — gh issue list --assignee @me as last resort.
  2. Inbox residue. Genuine NEEDS_REPLY plus todo/action-labeled emails surviving the most recent /ops:ops-inbox pass (run bin/ops-inbox-scan with OIS_NO_REFRESH=1 if one already ran this session).
  3. Dedupe against items already handled this session, and collapse duplicate issues tracking the same underlying ask (mirror boards, Linear↔Paperclip twins).
  4. Shape each item as {key, refs: [issue identifiers], hint} where hint states the concrete question to answer AND what must be verified (never trust the issue title alone).

Step 2 — Run the gather workflow (READ-ONLY, batched)

Invoke the Workflow tool with the script below and args = [items]. Hard rules the script encodes:

  • Agents are READ-ONLY. No sends, archives, payments, signatures, mark-reads, or tracker mutations from inside the workflow — they read, verify, and prepare only. All execution happens later in the main session under the outbound-approval gate.
  • Batched fan-out (3 at a time). A wide parallel burst can rate-limit (429) the model-account pool — pooled CLIProxyAPI setups especially. Batches of 3 finish nearly as fast and never starve the rest of the fleet.
  • Verification over trust. Every load-bearing claim in a package must come from an actual read (issue body, thread, sqlite row) with dates/ids cited in status_summary. An item that turns out already handled comes back as action_type=already_done with evidence.
  • Partial failure → resume. If some agents die (rate limits, transient errors), fix nothing and relaunch with resumeFromRunId — completed agents replay from cache.
js
export const meta = {
  name: 'desk-sweep',
  description: 'Fan out read-only context agents over open decision/action items and return ready-to-approve action packages',
  phases: [
    { title: 'Gather', detail: 'one read-only agent per item, batches of 3' },
    { title: 'Synthesize', detail: 'order into an approval queue: quick wins first' },
  ],
}

const ACTION_SCHEMA = {
  type: 'object', additionalProperties: false,
  required: ['key', 'title', 'status_summary', 'recommendation', 'action_type'],
  properties: {
    key: { type: 'string' },
    title: { type: 'string' },
    status_summary: { type: 'string', description: '2-4 sentences: what this is, current state, evidence (dates/ids)' },
    recommendation: { type: 'string', description: 'single concrete next action with an approvable default' },
    action_type: { type: 'string', enum: ['send_email', 'send_whatsapp', 'decision', 'sign', 'pay', 'chase', 'info_only', 'archive', 'already_done'] },
    draft: { type: ['object', 'null'], additionalProperties: false, properties: {
      channel: { type: 'string', enum: ['email', 'whatsapp'] },
      to: { type: 'string' }, cc: { type: 'string' }, subject: { type: 'string' },
      body: { type: 'string', description: "FULL final text in the owner's voice + thread language" },
      reply_to_message_id: { type: 'string' },
    }},
    decision_options: { type: 'array', items: { type: 'string' } },
    blockers: { type: 'array', items: { type: 'string' } },
    effort_minutes: { type: 'number' },
    confidence: { type: 'string', enum: ['high', 'medium', 'low'] },
  },
}

phase('Gather')
const ITEMS = (typeof args === 'string' ? JSON.parse(args) : args) || []
if (!ITEMS.length) throw new Error('desk-sweep needs args: [{key, refs, hint}]')

const CHUNK = 3
const packages = []
for (let i = 0; i < ITEMS.length; i += CHUNK) {
  const batch = ITEMS.slice(i, i + CHUNK)
  const res = (await parallel(batch.map(it => () =>
    agent(
      `You are a READ-ONLY desk-sweep context agent. You MUST NOT send, archive, pay, sign, ` +
      `mark-read, or mutate ANYTHING — no mail sends, no POSTs to send/archive endpoints, no ` +
      `tracker mutations. Read, search, and prepare only.\n\n` +
      `ITEM key="${it.key}": ${it.hint}\nReferences: ${JSON.stringify(it.refs || [])}\n\n` +
      `Gather context read-only, every shell command with an explicit timeout ` +
      `("timeout --signal=TERM --kill-after=5s 30s <cmd>"): the task tracker record(s) for each ` +
      `ref incl. comments; Gmail via gog (search then thread get -j; messages nest under ` +
      `.thread.messages); the WhatsApp bridge store read-only via sqlite3 (merge a person's @lid ` +
      `and @s.whatsapp.net chats; resolve names via contacts); local files if referenced.\n\n` +
      `RULES: verify every load-bearing claim with an actual read; if already handled, return ` +
      `action_type=already_done with evidence. Drafts must be the FULL final text in the owner's ` +
      `voice and the thread's language, no AI tone. Decisions get 2-4 concrete options, ` +
      `recommended first. Your final output MUST be the structured action package.`,
      { label: `gather:${it.key}`, phase: 'Gather', schema: ACTION_SCHEMA }
    )
  ))).filter(Boolean)
  packages.push(...res)
  log(`gathered ${packages.length}/${ITEMS.length}`)
}

phase('Synthesize')
return await agent(
  `You are READ-ONLY. Order these action packages into an approval queue: quick wins first ` +
  `(low effort, high confidence, drafts ready), then decisions, then heavy/blocked. Drop ` +
  `nothing; flag conflicts between packages.\n\n` + JSON.stringify(packages, null, 2) +
  `\n\nReturn {queue: [same packages verbatim + "rank"], conflicts: [], stats: {total, ` +
  `ready_drafts, decisions, already_done}}.`,
  { label: 'synthesize', phase: 'Synthesize',
    schema: { type: 'object', additionalProperties: true, required: ['queue'] } }
)

Step 3 — Work the approval queue (main session, outbound gate)

Per package, in rank order:

  • already_done / info_only → report in one line and close the item in the task tracker (status → done). This is tracker-only closure: no mailbox, chat or file is archived, moved or marked read. These are the free wins.
  • archive → a package from a read-only agent is a recommendation, not owner authorization. Show the item and the evidence, then archive it only after explicit owner authorization for that item (e.g. via AskUserQuestion). Without it, leave the thread where it is and report the item as still open.
  • send_email / send_whatsapp / chase → exactly as in /ops:ops-inbox and ops-rules Rule 6: one draft, one approval, one send, only through the host's approved outbound gate. The gate shows the exact recipient and full latest text; approval is the owner's native yes bound to those bytes, never a token, counter or earlier approval. Re-read the live thread before sending, verify delivery on a fresh read, then update the tracker. Archive only under explicit archive authorization. If the gate refuses or does not support the channel, keep the item owned with the exact diagnostic; never use a direct CLI, bridge or API sender.
  • decision → present the options (recommended first) via AskUserQuestion, record the outcome in the tracker, execute any follow-through it implies.
  • sign / pay → prepare only: surface the exact link/document/amount and what to check before signing/paying. NEVER sign or move money autonomously (Rule 5).

Step 4 — Wrap up

Report: sent / decided / closed / still-open-with-reason. Schedule reminders (CronCreate, non-outbound) for anything parked with a follow-up horizon. The task tracker is the SSOT — every touched item gets its status updated there before the summary prints.

Agent Teams support

If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when fanning out the gather workflow across independent item sources (Paperclip per company, Linear, GitHub, Gmail/WhatsApp residue, etc.) in parallel. This enables:

  • Gather agents share context mid-flight (one surfaces a high-priority payment → the ranker can prioritize before the desk-sweep plan is emitted)
  • You can steer scope in real time ("skip Linear, focus on Paperclip only")
  • Agents report progress as each gather round completes, so the main session can start the approval-queue step earlier on the items already returned

Team setup (only when the flag is enabled, and only for genuinely parallel sources — a single-source desk sweep stays inline):

TeamCreate("ops-desk")
Agent(team_name="ops-desk", name="paperclip-gather", prompt="Gather owner-action items across all configured Paperclip companies")
Agent(team_name="ops-desk", name="linear-gather", prompt="Gather Linear items assigned to the owner")
Agent(team_name="ops-desk", name="inbox-residue", prompt="Gmail/WhatsApp NEEDS_REPLY + todo-labeled items from the last inbox pass")
Agent(team_name="ops-desk", name="github-chases", prompt="Open PRs awaiting owner review, blocked issues")

After the team reports, the main session runs Step 3 (work the approval queue) on the merged, ranked list. Each SendMessage from a gather agent is a structured action package — never an outbound send (Rule 6: per-draft outbound approval gate stays in the main session, not in the team).

If the flag is NOT set, fall back to standard fire-and-forget Agent subagents (one per source) — same shape, slower wall-clock because no shared context.

Frequently asked questions

What does the Ops Desk AI skill do?

OPS on-demand: This skill should be used when the user asks to "desk sweep", "open decisions", or…

Why use Ops Desk on TypingMind?

Because you install it once and use it with any model. Ops Desk is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ops Desk in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-desk. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ops Desk?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ops Desk?

As many as you like. As long as a model supports skills, you can use Ops Desk with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ops Desk AI skill free?

Yes. It is published on GitHub by Lifecycle-Innovations-Limited under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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