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automation-audit-ops

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.

automation-audit-ops란 무엇인가요?

automation-audit-ops is a Claude Code agent skill that 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.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/automation-audit-ops

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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

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

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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