deploy-check
[experimental] Evaluate deployment risk by analyzing code changes against incident history, active incidents, and on-call readiness. Forked-subagent flow may not have MCP access in all Claude Code contexts.
What this skill does
# Pre-Deploy Safety Check (experimental) > **Experimental**: this skill uses `context: fork` to delegate to the `deploy-guardian` agent. In some Claude Code contexts the forked subagent does not inherit the plugin's MCP tools. If the agent reports MCP tools unavailable, run `/rootly:status` to manually check active incidents before deploying. You are evaluating whether it is safe to deploy the current code changes. Follow this workflow carefully. ## Current changes !`git diff --stat HEAD` ## Current branch !`git branch --show-current` ## Recent commits !`git log --oneline -5` ## Workflow ### 1. Assess Changes Review the git diff output above. If the diff is empty (no changes), report "No changes to evaluate -- working tree is clean" and stop. Identify which files and components are affected by the changes. ### 2. Resolve Affected Services Determine which Rootly service(s) these changes map to, using this resolution chain (in priority order): 1. **Check `.claude/rootly-config.json`** in the project root -- if it exists, use the `services` field 2. **Match repo name**: Use the git repo name (from `basename $(git rev-parse --show-toplevel)`) to search for matching Rootly services via `mcp__rootly__search_incidents` 3. **Ask the user**: If neither method works, ask which service(s) this repo maps to ### 3. Search Incident History Call `mcp__rootly__search_incidents` for the identified services, looking at the last 90 days. Note any patterns in frequency, severity, or root causes. ### 4. Find Related Incidents Call `mcp__rootly__find_related_incidents` with a summary of the current changes (based on the diff). This uses TF-IDF similarity matching to find historically similar incidents. If results have confidence scores below 0.3, flag them as low confidence and note that manual review may be needed. ### 5. Check Active Incidents Call `mcp__rootly__search_incidents` filtered to active (`started`) status for the affected services. Pay special attention to P1/P2 (critical/high severity) incidents. ### 6. Check On-Call Readiness Call `mcp__rootly__get_oncall_handoff_summary` to verify: - Who is currently on-call - When the next handoff is - Whether there are any on-call gaps ### 7. Synthesize Deployment Brief Present a structured deployment brief: ``` ## Deployment Safety Brief **Risk Level**: [LOW / MEDIUM / HIGH / CRITICAL] **Branch**: [branch name] **Changed files**: [count] ### Active Incidents [List any active incidents on affected services, or "None"] ### On-Call Status - **Current**: [name] (since [time]) - **Next handoff**: [time] - **Status**: [Available / Gap detected / High fatigue] ### Similar Past Incidents [Top 3 similar incidents with what happened and how they were resolved] ### Risk Factors [Bullet list of specific risks identified] ### Recommendation **[GO / CAUTION / NO-GO]**: [1-2 sentence reasoning] ``` **Risk level criteria**: - **LOW**: No active incidents, no similar past incidents, on-call is healthy - **MEDIUM**: Minor past incidents found, or on-call handoff is imminent - **HIGH**: Active incidents on related services, or recurring pattern of similar incidents - **CRITICAL**: Active P1/P2 incident on the affected service, or significant on-call gaps
Related in Cloud & DevOps
appbuilder-action-scaffolder
IncludedCreate, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
orchestrating-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or data space and data kit management. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase sf data360 workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching phase-specific skill), the task is STDM/session tracing/parquet telemetry (use observing-agentforce), standard CRM SOQL (use querying-soql), or Apex implementation (use generating-apex).
github-project-automation
IncludedAutomate GitHub repository setup with CI/CD workflows, issue templates, Dependabot, and CodeQL security scanning. Includes 12 production-tested workflows and prevents 18 errors: YAML syntax, action pinning, and configuration. Use when: setting up GitHub Actions CI/CD, creating issue/PR templates, enabling Dependabot or CodeQL scanning, deploying to Cloudflare Workers, implementing matrix testing, or troubleshooting YAML indentation, action version pinning, secrets syntax, runner versions, or CodeQL configuration. Keywords: github actions, github workflow, ci/cd, issue templates, pull request templates, dependabot, codeql, security scanning, yaml syntax, github automation, repository setup, workflow templates, github actions matrix, secrets management, branch protection, codeowners, github projects, continuous integration, continuous deployment, workflow syntax error, action version pinning, runner version, github context, yaml indentation error
sf-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
fabric-cli
IncludedUse this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
lark
IncludedLark/Feishu CLI skills: lark-cli operations for docs, markdown, sheets, base, calendar, im, mail, task, okr, drive, wiki, slides, whiteboard, apps, approval, attendance, contact, vc, minutes, event. Use when the user needs to operate Lark/Feishu resources via lark-cli, send messages, manage documents, spreadsheets, calendars, tasks, OKRs, deploy web pages, or any Feishu/Lark workspace operations.