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Ops Next

Organization
Lifecycle-Innovations-Limited
ops-next

OPS on-demand: This skill should be used when the user asks to "what should I do next", "priority…

Overview

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

Use it in TypingMind

Enable Ops Next 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 Next 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 Next 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.

Runtime Context

Before advising, load:

  1. Preferences: cat ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json — read owner, primary_project, default_channels
  2. Daemon health: cat ${CLAUDE_PLUGIN_DATA_DIR}/daemon-health.json — flag any action_needed as priority
  3. Ops memories: Check ${CLAUDE_PLUGIN_DATA_DIR}/memories/topics_active.md for ongoing work context
  4. Home automation: If home_automation is configured in $PREFS_PATH, probe Homey via /ops:ops-home status for active alarms before composing the priority stack.

OPS ► NEXT ACTION

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).

CLI/API Reference

gh CLI (GitHub)

CommandUsageOutput
gh pr list --state open --json number,title,statusCheckRollup,reviewDecisionOpen PRs with CI/review statusJSON array

Agent Teams support

If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when gathering priority data in parallel. This enables:

  • Agents share context and can coordinate mid-flight
  • You can steer priorities in real-time
  • Agents report progress as they complete

Team setup (only when flag is enabled):

TeamCreate("next-team")
Agent(team_name="next-team", name="fires-checker", prompt="Check infra health and CI for production fires")
Agent(team_name="next-team", name="comms-checker", prompt="Check unread messages across all channels")
Agent(team_name="next-team", name="prs-checker", prompt="Find PRs ready to merge — CI green, reviews approved")
Agent(team_name="next-team", name="sprint-checker", prompt="Check Linear sprint for highest-priority in-progress issues")

If the flag is NOT set, use standard fire-and-forget subagents.

Pre-gathered data

Infrastructure & fires

${CLAUDE_PLUGIN_ROOT}/bin/ops-infra 2>/dev/null || echo '{"clusters":[]}'

Git & PRs

${CLAUDE_PLUGIN_ROOT}/bin/ops-prs 2>/dev/null || echo '[]'

CI status

${CLAUDE_PLUGIN_ROOT}/bin/ops-ci 2>/dev/null || echo '[]'

Competitor alerts (last 24h)

source "${CLAUDE_PLUGIN_ROOT}/scripts/lib/competitor/context.sh" 2>/dev/null \
  && competitor_priority_items --top 3 --window-days 1 2>/dev/null \
  || true

Unread messages

${CLAUDE_PLUGIN_ROOT}/bin/ops-unread 2>/dev/null || echo '{}'

GSD active phases

PLUGIN_ROOT="${CLAUDE_PLUGIN_ROOT}" . "${CLAUDE_PLUGIN_ROOT}/lib/registry-path.sh"
for d in $(jq -r '.projects[] | select(.has_roadmap == true) | .path' "$REGISTRY" 2>/dev/null); do
  expanded="${d/#\~/$HOME}"
  if [ -f "$expanded/.planning/STATE.md" ]; then
    alias=$(basename "$expanded")
    cat "$expanded/.planning/STATE.md" 2>/dev/null | head -30
    echo "---NEXT---"
  fi
done

Your task

Apply the priority stack to all pre-gathered data:

Priority 1 — FIRES

Check infra data for: unhealthy ECS tasks, stopped services, failed deployments. Check CI for: broken main or dev branches. If any fires exist → recommend /ops-fires immediately.

Priority 1.5 — HOME ALARMS (only if home_automation is configured in $PREFS_PATH)

Probe /ops:ops-home status for active alarms.

  • Critical home alarms (smoke, water leak, security breach) → top priority — recommend /ops:ops-home alarm immediately, above all other fires.
  • Energy anomalies (current draw > 3× 7-day baseline) → near-top, surface before competitor alerts.
  • Routine home tasks (presence changes, flow failures) → low priority — fold into Priority 6 tail.

If home_automation is NOT configured, skip this priority silently.

Priority 2 — COMPETITOR ALERTS

Check the competitor alerts pre-gathered data (last 24h window). If competitor_priority_items returned lines (non-empty output):

  • Surface each as: REACT: <competitor> <source> changed — see latest-<brand>.md
  • Recommend the user open the latest report file (path from competitor_context → by_brand.<brand>.latest_report)
  • If high-severity alerts exist → recommend addressing before comms

If the output is empty or the source block returned nothing, skip this priority silently.

Priority 3 — URGENT COMMS

Check unread counts. If WhatsApp or email has unread messages from humans (not automated):

  • Estimate urgency from sender/preview if available
  • If urgent comms → recommend /ops-inbox [channel]

Priority 4 — READY-TO-MERGE PRs

Check PRs for: CI green + no unresolved review comments + not draft. If any ready → recommend reviewing that PR now. Check: gh pr list --state open --json number,title,statusCheckRollup,reviewDecision 2>/dev/null

Priority 5 — LINEAR SPRINT

Fetch current sprint issues: use mcp__linear__list_issues filtered to current cycle (use Linear GraphQL fallback for cycle queries if needed). Find highest-priority issue that is in progress or unstarted.

Priority 6 — GSD WORK

From GSD state, find the highest revenue-impact active phase across all projects. Revenue weighting: the synced registry (${OPS_DATA_DIR}/registry.json, schema name/path/remote_url/status/phase/branch) carries no revenue or priority field. Rank by status (active > paused > none), then last_commit_ts (newest first), then remaining_tasks (fewest first). Any priority/revenue values live in preferences.json under projects.<name>, if set.


Output format

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 OPS ► NEXT ACTION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

 TOP PRIORITY: [fires|comms|PR|sprint|gsd]
 ▶ [specific action in one sentence]

 WHY: [1-2 sentence rationale]

──────────────────────────────────────────────────────
 Full priority stack:
 1. [action] — [why] → [/skill or command]
 2. [action] — [why] → [/skill or command]
 3. [action] — [why] → [/skill or command]
 4. [action] — [why] → [/skill or command]
 5. [action] — [why] → [/skill or command]

──────────────────────────────────────────────────────
 a) Do #1 now
 b) Do #2 now
 c) Show me everything (/ops-go)
 d) I'll decide — just show the briefing

 → Pick or describe what you want
──────────────────────────────────────────────────────

Use AskUserQuestion. When user selects an option, invoke the corresponding skill directly — don't describe it, do it.

If $ARGUMENTS contains context (e.g., "focus on "), constrain the analysis to that context.


Native tool usage

Tasks — action tracking

After the user selects an action, use TaskCreate to track it. When routing to the corresponding skill, the task persists as a reminder of what the user chose to focus on.

WebFetch — enrichment fallback

When pre-gathered data is stale or incomplete, use WebFetch to pull fresh data from APIs (Linear GraphQL, Sentry, GitHub) directly.

Frequently asked questions

What does the Ops Next AI skill do?

OPS on-demand: This skill should be used when the user asks to "what should I do next", "priority…

Why use Ops Next on TypingMind?

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

Which AI models can use Ops Next?

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 Next?

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

Is the Ops Next 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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