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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.

¿Qué es automation-audit-ops?

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.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/automation-audit-ops

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Documentación

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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