automation-audit-ops

--- name: automation-audit-ops description: Evidence-first automation inventory and overlap audit workflow for ECC. Use when the user wants to know which jobs, hooks, connectors, MCP servers, or wrappers are live, broken, redundant, or missing before fixing anything. metadata: origin: ECC --- # Automation Audit Ops Use this when the user asks what automations are live, which jobs are broken, where overlap exists, or what tooling and connectors are actually doing useful work right now. This is an audit-first operator skill. The job is to produce an evidence-backed inventory and a keep / merge / cut / fix-next recommendation set before rewriting anything. ## Skill Stack Pull these ECC-native skills into the workflow when relevant: - `workspace-surface-audit` for connector, MCP, hook, and app inventory - `knowledge-ops` when the audit needs to reconcile live repo truth with durable context - `github-ops` when the answer depends on CI, scheduled workflows, issues, or PR automation - `ecc-tools-cost-audit` when the real problem is webhook fanout, queued jobs, or billing burn in the sibling app repo - `research-ops` when local inventory must be compared against current platform support or public docs - `verification-loop` for proving post-fix state instead of relying on assumed recovery ## When to Use - user asks "what automations do I have", "what is live", "what is broken", or "what overlaps" - the task spans cron jobs, GitHub Actions, local hooks, MCP servers, connectors, wrappers, or app integrations - the user wants to know what was ported from another agent system and what still needs to be rebuilt inside ECC - the workspace has accumulated multiple ways to do the same thing and the user wants one canonical lane ## Guardrails - start read-only unless the user explicitly asked for fixes - separate: - configured - authenticated - recently verified - stale or broken - missing entirely - do not claim a tool is live just because a skill or config references it - do not merge or delete overlapping surfaces until the evidence table exists ## Workflow ### 1. Inventory the real surface Read the current live surface before theorizing: - repo hooks and local hook scripts - GitHub Actions and scheduled workflows - MCP configs and enabled servers - connector- or app-backed integrations - wrapper scripts and repo-specific automation entrypoints Group them by surface: - local runtime - repo CI / automation - connected external systems - messaging / notifications - billing / customer operations - research / monitoring ### 2. Classify each item by live state For every surfaced automation, mark: - configured - authenticated - recently verified - stale or broken - missing Then classify the problem type: - active breakage - auth outage - stale status - overlap or redundancy - missing capability ### 3. Trace the proof path Back every important claim with a concrete source: - file path - workflow run - hook log - config entry - recent command output - exact failure signature If the current state is ambiguous, say so directly instead of pretending the audit is complete. ### 4. End with keep / merge / cut / fix-next For each overlapping or suspect surface, return one call: - keep - merge - cut - fix next The value is in collapsing noisy automation into one canonical ECC lane, not in preserving every historical path. ## Output Format ```text CURRENT SURFACE - automation - source - live state - proof FINDINGS - active breakage - overlap - stale status - missing capability RECOMMENDATION - keep - merge - cut - fix next NEXT ECC MOVE - exact skill / hook / workflow / app lane to strengthen ``` ## Pitfalls - do not answer from memory when the live inventory can be read - do not treat "present in config" as "working" - do not fix lower-value redundancy before naming the broken high-signal path - do not widen the task into a repo rewrite if the user asked for inventory first ## Verification - important claims cite a live proof path - each surfaced automation is labeled with a clear live-state category - the final recommendation distinguishes keep / merge / cut / fix-next

[{"step":"Define the scope of your audit by specifying the environment and component types. For example: 'audit production environment for jobs, hooks, and connectors'.","tip":"Use the exact names of your environments (e.g., 'prod', 'staging', 'dev') and component types (e.g., 'scheduled_tasks', 'webhooks', 'api_connectors') to ensure accuracy."},{"step":"Gather evidence by running diagnostic commands or queries. For ECC, this might include: `kubectl get jobs -n ecc-prod`, `curl -X GET http://ecc-hooks/status`, or querying your automation platform's API for execution logs.","tip":"If you don’t have direct access to logs, ask your DevOps team to provide recent execution data or enable verbose logging for the audit period."},{"step":"Input the gathered data into the AI audit tool using the prompt template. Include placeholders like [ECC_ENVIRONMENT] and [COMPONENT_TYPES] with your specific values.","tip":"For large environments, break the audit into chunks (e.g., audit jobs first, then hooks) to avoid overwhelming the AI with too much data at once."},{"step":"Review the AI-generated report and prioritize fixes based on risk levels. Cross-reference the dependency map to identify cascading issues.","tip":"Use the 'Next Steps' section of the report to create a prioritized backlog in your project management tool (e.g., Jira, Linear)."},{"step":"Validate the AI’s findings by manually checking a sample of the flagged components (e.g., test a broken webhook or inspect a redundant job configuration).","tip":"If the AI’s recommendations don’t match your manual findings, adjust the audit scope or provide additional context to refine the output."}]