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Ops Deploy

Organization
Lifecycle-Innovations-Limited
ops-deploy

OPS on-demand: This skill should be used when the user asks to "deploy status", "what is in…

Overview

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

Use it in TypingMind

Enable Ops Deploy 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 Deploy 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 Deploy 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 ► DEPLOY STATUS

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. Preferences: Read ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json

    • timezone — display all deploy timestamps in the correct timezone
  2. Daemon health: Read ${CLAUDE_PLUGIN_DATA_DIR}/daemon-health.json

    • Check infra-monitor status — if not running, note that ECS data may be stale
  3. Secrets: AWS and Vercel credentials required.

    Secret Resolution

    • AWS: check $AWS_PROFILE / $AWS_ACCESS_KEY_ID → doppler secrets get AWS_ACCESS_KEY_ID --plain → vault query cmd from prefs
    • Vercel token: check $VERCEL_TOKEN → doppler secrets get VERCEL_TOKEN --plain → vault

CLI/API Reference

aws CLI

CommandUsageOutput
aws ecs list-clusters --output jsonAll ECS clusters{clusterArns: [...]}
aws ecs list-services --cluster <name> --output jsonServices in cluster{serviceArns: [...]}
aws ecs describe-services --cluster <name> --services <arn> --output jsonService health{services: [{serviceName, status, runningCount, desiredCount, pendingCount}]}
aws logs tail /ecs/<service> --since 1h --format shortECS logsLog lines

gh CLI (GitHub)

CommandUsageOutput
gh run list --repo <owner/repo> --limit 5 --json status,conclusion,name,headBranch,createdAt,databaseIdCI runsJSON array
gh run view <id> --repo <repo> --log-failedFailed CI logsLog output
gh api repos/<repo>/actions/runs/<run-id> --jq .status,.conclusionPoll one CI runJSON fields (REST bucket)

gh run watch and gh pr checks --watch are banned — they poll every 2-5s with no cap. hooks/gh-watch-guard.sh denies them. Poll the REST endpoint above on a sleep 30 floor with a finite tick count instead.


Agent Teams support

If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when checking deploy platforms 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("deploy-team")
Agent(team_name="deploy-team", name="ecs-checker", prompt="List all ECS clusters and describe service health, running/desired counts")
Agent(team_name="deploy-team", name="vercel-checker", prompt="List Vercel projects and recent deployments with status")
Agent(team_name="deploy-team", name="ci-checker", prompt="Check GitHub Actions runs across all registered repos for failures")

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

Phase 1 — Gather deploy data in parallel

ECS services (all clusters)

bash
${CLAUDE_PLUGIN_ROOT}/bin/ops-infra 2>/dev/null || \
aws ecs list-clusters --output json 2>/dev/null

ECS service details

bash
for cluster in $(aws ecs list-clusters --output json 2>/dev/null | jq -r '.clusterArns[]'); do
  cluster_name=$(basename "$cluster")
  aws ecs list-services --cluster "$cluster_name" --output json 2>/dev/null | \
    jq -r '.serviceArns[]' | while read svc; do
    aws ecs describe-services --cluster "$cluster_name" --services "$svc" \
      --output json 2>/dev/null | jq '.services[] | {name: .serviceName, desired: .desiredCount, running: .runningCount, pending: .pendingCount, image: (.taskDefinition // "unknown"), status: .status}'
  done
done

Recent GitHub Actions runs (registry-driven)

bash
REGISTRY="${CLAUDE_PLUGIN_ROOT}/scripts/registry.json"
[ -f "$REGISTRY" ] || REGISTRY="${CLAUDE_PLUGIN_ROOT}/scripts/registry.example.json"
for repo in $(jq -r '.projects[] | select(.gsd == true) | .repos[]' "$REGISTRY" 2>/dev/null); do
  echo "=== $repo ==="
  gh run list --repo "$repo" --limit 5 --json status,conclusion,name,headBranch,createdAt,databaseId 2>/dev/null
done

Vercel deployments

Use mcp__claude_ai_Vercel__list_projects then mcp__claude_ai_Vercel__list_deployments for each project (limit 5 per project).


Phase 2 — Render dashboard

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 OPS ► DEPLOY STATUS — [timestamp]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

ECS SERVICES
 CLUSTER    SERVICE         D/R/P   STATUS   LAST DEPLOY
 ─────────────────────────────────────────────────────
 [cluster]  [service]       [x/x/x] ACTIVE   [time ago]
 ...

VERCEL DEPLOYMENTS
 PROJECT     ENV        STATUS    COMMIT    DEPLOYED
 ─────────────────────────────────────────────────────
 [project]   production  READY    [sha]     [time ago]
 ...

CI/CD PIPELINE
 REPO              BRANCH   WORKFLOW        STATUS    AGE
 ─────────────────────────────────────────────────────
 example-api       main     Deploy API      ✓ success  2h
 example-web       dev      Build            ✗ failure  1h
 ...

PENDING DEPLOYS (branch ready, not yet deployed)
 [repo] [branch] [PR#] [CI status] → needs merge to trigger

──────────────────────────────────────────────────────

After rendering, use batched AskUserQuestion calls (max 4 options each). Only show actions relevant to the current state (e.g., skip "View logs for failing service" if nothing is failing). If <=4 relevant actions, use a single call. If >4, batch:

AskUserQuestion call 1:

  [View logs for [failing service]]
  [Trigger manual deploy for [project]]
  [View build logs for [failing CI run]]
  [More actions...]

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

  [Check Vercel [project] runtime logs]
  [Open GitHub Actions for [repo]]
  [Back to dashboard]

Deep-dive by project

If $ARGUMENTS has a project alias, show only that project's deploy info + last 10 CI runs + option to view logs.

For failing deploys: offer to view logs via mcp__claude_ai_Vercel__get_deployment_build_logs or ECS CloudWatch logs.

If user selects manual deploy (option b), confirm with AskUserQuestion before triggering:

Trigger deploy for [project]:
  Environment: [production/staging]
  Branch: [branch]
  Last commit: [sha] — [message]

  [Deploy now]  [View diff since last deploy first]  [Cancel]

If user selects to view logs, show the logs and use AskUserQuestion:

  [Dispatch fix agent for this failure]  [Redeploy]  [Back to dashboard]

Native tool usage

Monitor — live deploy streaming

When watching a deploy in progress, call the approved monitor script. Do NOT hand-roll a gh api polling loop: hooks/gh-watch-guard.sh denies any for/while/until loop containing a quota-spending gh verb, so a hand-rolled loop is rejected before it ever polls. gh run watch is denied for the same reason (it polls every 2-5s with no cap).

bash
${CLAUDE_PLUGIN_ROOT}/scripts/ops-deploy-monitor.sh <owner/repo> <pr-number>

That script owns the bounded poll: a tick cap, a wall-clock deadline, a 25s sleep floor and a REST rate gate, all in one place. It is also what the bin/ops-deploy-fix-merge-trigger PostToolUse hook spawns, so the interactive path and the automatic path share one implementation and one set of limits.

Only a /rate_limit-only loop is exempt from the guard (that endpoint is quota-free) — useful for blocking until quota recovers, not for watching a run.

For ECS deploys: Monitor(command: "aws ecs wait services-stable --cluster <cluster> --services <service>")

Tasks — deploy tracking

Use TaskCreate per project being deployed. Update with TaskUpdate as deploys succeed/fail.

WebFetch — Vercel fallback

When Vercel MCP tools are unavailable, use WebFetch with the Vercel API directly:

WebFetch(url: "https://api.vercel.com/v6/deployments?projectId=<id>&limit=5", headers: {"Authorization": "Bearer $VERCEL_TOKEN"})

Ledger Integration

CLAIM_KEY: deploy:<project>:<env> (e.g. deploy:my-api:production)

Keyed on project + environment so concurrent deploys of the same target are blocked for the full claim TTL (30 minutes).

Pre-flight skip-check

bash
CLAIM_KEY="deploy:<project>:<env>"
ledger query --claim-key "$CLAIM_KEY" --since=-PT24H

If in_progress exists, a concurrent deploy is already running — abort and surface to the user. If done exists within the last hour, confirm before re-deploying.

Claim + resolve

bash
# Claim when deploy pipeline starts
ledger write \
  --claim-key "$CLAIM_KEY" \
  --kind "file" \
  --status "in_progress" \
  --title "Deploy: <project> → <env>" \
  --ttl-sec 1800

# Resolve after deploy completes or fails
ledger write \
  --claim-key "$CLAIM_KEY" \
  --kind "file" \
  --status "done" \
  --title "Deploy: <project> → <env>" \
  --context "succeeded|failed: <reason>"

Frequently asked questions

What does the Ops Deploy AI skill do?

OPS on-demand: This skill should be used when the user asks to "deploy status", "what is in…

Why use Ops Deploy on TypingMind?

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

Which AI models can use Ops Deploy?

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 Deploy?

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

Is the Ops Deploy 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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