Skip to content
cccskills
FreePublished on GitHub

automation-audit-ops

Audit scheduled jobs, hooks, MCP servers, and external integrations using configuration files, logs, and recent runs. Distinguish configured tools from verified working ones, identify failures or overlap, and recommend what to keep, merge, remove, or fix next.

OriginalShow the original 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.

Works withGitHub
See how to install

Good for

  • Checking which automations are actually working
  • Identifying broken jobs and authentication failures
  • Finding duplicate hooks, scripts, and integrations
  • Checking for missing automation after a system migration

Files included (1)

  • SKILL.md4.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

accessibility

FreeSummary

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC283.3KUpdated Oct 16, 2026

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC283.3KUpdated Oct 16, 2026

agent-eval

FreeSummary

Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.

  • Comparing coding agents on real development tasks
  • Evaluating a new tool or model before adoption
  • Checking performance after agent updates
affaan-m/ECC283.3KUpdated Oct 16, 2026

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC283.3KUpdated Oct 16, 2026

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

  • Diagnosing repeated tool calls with no progress
  • Investigating quality loss as context grows
  • Checking file paths and environment mismatches
affaan-m/ECC283.3KUpdated Oct 16, 2026

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC283.3KUpdated Oct 5, 2026

See all skills by affaan-m

Report a problem with this skill