Ops Linear logo

Ops Linear

Organization
Lifecycle-Innovations-Limited
ops-linear

OPS on-demand: This skill should be used when the user asks to "linear sprint", "create a ticket", or…

Overview

PublisherLifecycle-Innovations-Limited
Repositoryclaude-ops
Skill nameops-linear
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 Linear 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-linear
  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-linear .claude/skills/ops-linear
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ops Linear 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 Linear 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 Linear 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 ► LINEAR COMMAND CENTER

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).

Runtime Context

Before executing, load available context:

  1. Secrets: Linear API key required for MCP fallback queries.

    Secret Resolution

    • Check $LINEAR_API_KEY env var
    • Then: Doppler MCP tools (mcp__doppler__*) — if Doppler MCP server is configured
    • Then: doppler secrets get LINEAR_API_KEY --plain (if doppler CLI configured in prefs)
    • Then: use password_manager_config.query_cmd from ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json
    • If unavailable, use Linear MCP tools exclusively (no curl fallback possible)
  2. Preferences: Read ${CLAUDE_PLUGIN_DATA_DIR}/preferences.json for secrets_manager / doppler config.

CLI/API Reference

Linear GraphQL (fallback when MCP unavailable)

CommandUsageOutput
curl -X POST https://api.linear.app/graphql -H "Authorization: $LINEAR_API_KEY" -H "Content-Type: application/json" -d '{"query":"{ issues(filter: {state: {type: {in: [\"started\",\"unstarted\"]}}}) { nodes { id title state { name } priority assignee { name } } } }"}'Active issuesJSON
curl -X POST https://api.linear.app/graphql -H "Authorization: $LINEAR_API_KEY" -H "Content-Type: application/json" -d '{"query":"{ cycles(filter: {isActive: {eq: true}}) { nodes { id number startsAt endsAt } } }"}'Current cyclesJSON

Phase 1 — Load data

Run in parallel:

  1. mcp__linear__list_teams — get all team IDs
  2. mcp__linear__list_issues — get issues with cycle filter (use GraphQL fallback for cycle queries if needed)

Then fetch issues for the current cycle: mcp__linear__list_issues filtered to current cycle ID.


Route by $ARGUMENTS

ArgumentAction
(empty), sprintShow current sprint board
backlogShow unassigned/unscheduled issues
create [title]Create a new issue (prompt for details)
update [id]Update issue by ID
syncSync GSD phases to Linear issues
[issue-id]Show and edit that specific issue

Sprint board view

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 LINEAR ► SPRINT [N] — [start] → [end]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

IN PROGRESS
 [id]  [priority]  [title]  [assignee]  [estimate]

TODO
 [id]  [priority]  [title]  [assignee]  [estimate]

DONE THIS SPRINT
 [id]  [title]  [completed date]

BLOCKED / CANCELLED
 [id]  [title]  [reason]

──────────────────────────────────────────────────────
 Sprint velocity: [done points] / [total points] ([%])
──────────────────────────────────────────────────────

Use batched AskUserQuestion calls (max 4 options each):

AskUserQuestion call 1:

  [Create new issue]
  [Update issue status]
  [Move issue to/from sprint]
  [More...]

AskUserQuestion call 2 (only if "More..."):

  [View backlog]
  [Sync with GSD phases]

Create issue flow

Collect from user (or parse from $ARGUMENTS):

  • Title
  • Team (list choices if ambiguous)
  • Priority (urgent/high/medium/low)
  • Cycle (current sprint or backlog)
  • Assignee (optional)
  • Estimate (optional)

Use mcp__linear__create_issue to create. Confirm: Created [id]: [title]


GSD sync flow

Read all active GSD STATE.md files across projects. For each active phase:

  1. Check if a Linear issue exists with matching phase reference.
  2. If not, offer to create one.
  3. If status differs (GSD says done, Linear says in-progress), offer to sync.

Update Linear issues to match GSD phase completion status.

Use AskUserQuestion after displaying any view to get the next action.


Native tool usage

Tasks — sprint work tracking

When the user starts working on a Linear issue, use TaskCreate to track it locally. Update with TaskUpdate as the issue progresses. This bridges Linear state with local session state.

WebFetch — Linear API fallback

When Linear MCP tools hit quota limits or fail, fall back to WebFetch with the Linear GraphQL API:

WebFetch(url: "https://api.linear.app/graphql", method: "POST", headers: {"Authorization": "$LINEAR_API_KEY"}, body: '{"query":"{ issues(filter: {cycle: {id: {eq: \"<id>\"}}}}) { nodes { id title state { name } } } }"}')

Ledger Integration

CLAIM_KEY: linear:issue:<identifier> (e.g. linear:issue:<TEAM>-123)

Pre-flight skip-check

bash
CLAIM_KEY="linear:issue:<identifier>"
ledger query --claim-key "$CLAIM_KEY" --since=-PT24H

If in_progress or done exists, skip — another session or Perplexity already acted on this issue. Surface awaiting_user entries as "update already staged."

Claim + resolve

bash
# Claim when creating, updating, or triaging a Linear issue
ledger write \
  --claim-key "$CLAIM_KEY" \
  --kind "file" \
  --status "in_progress" \
  --title "Linear: <identifier> — <title>" \
  --ttl-sec 3600

# Resolve after Linear state is updated
ledger write \
  --claim-key "$CLAIM_KEY" \
  --kind "file" \
  --status "done" \
  --title "Linear: <identifier> — <title>" \
  --context "status→<new_state> | assignee→<assignee>"

Frequently asked questions

What does the Ops Linear AI skill do?

OPS on-demand: This skill should be used when the user asks to "linear sprint", "create a ticket", or…

Why use Ops Linear on TypingMind?

Because you install it once and use it with any model. Ops Linear 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 Linear 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-linear. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ops Linear?

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 Linear?

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

Is the Ops Linear 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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