inbox-load-audit - SKILL.md Agent Skill

name: inbox-load-audit description: Scans installed skills, plugins, MCP servers, hooks, and scheduled tasks to produce an inbox load score — how many independently-managed AI surfaces the user is maintaining. Outputs a score, breakdown, and top recommendations to reduce load.

Inbox Load Audit

Diagnoses tool sprawl by counting and categorizing independently-managed AI surfaces (skills, plugins, MCP servers, hooks, scheduled tasks, active agents). Produces a single numeric score, a per-category breakdown, and actionable recommendations to reduce the number of surfaces that demand attention.

The audit answers the question Nate Kadlac put plainly: "the agents are real, they work, and the product they have collectively produced is another inbox." Inbox load audit makes that load measurable.

Trigger

Use when the user says "inbox load audit", "how many inboxes do I have", "tool sprawl check", "how overloaded am I", "how many surfaces am I managing", "audit my setup", or asks why their AI tools feel like work rather than leverage.

Phase 1: Discovery

Scan for installed AI surfaces. For each category, count and list items. Work from what the user provides or what is discoverable in the session context:

Category A — Skills

All skill directories in the skills installation path
Distinguish: active (invoked in last 14 days), stale (not invoked in 14+ days), unsponsored (no agent or callsite reference)

Category B — MCP Servers

All entries in the MCP config (claude_desktop_config.json or equivalent)
Each MCP server = one independently-managed API surface

Category C — Hooks

All PreToolUse, PostToolUse, SessionStart, UserPromptSubmit hooks
Each hook = one automated behavior that can fire unexpectedly and requires maintenance

Category D — Scheduled Tasks / Crons

All recurring tasks (cron jobs, scheduled agents, automation routines)
Each scheduled task = one inbox that accumulates output needing review

Category E — Active Agents

Named agents with running processes or dispatch endpoints
Each agent = one inbox of queued tasks and output that needs monitoring

Category F — Plugins

Installed Claude Code plugins
Flag: plugins with missing install paths (stale registry entries)

If the user cannot provide paths or configs, ask for a directory listing from their Claude Code settings.

Phase 2: Score Calculation

Base score = (A_total × 1) + (B_total × 2) + (C_total × 1.5) + (D_total × 3) + (E_total × 4) + (F_total × 1)

Weight rationale:

Skills (×1): passive until invoked — low maintenance per item
MCP Servers (×2): each requires auth, version management, and error monitoring
Hooks (×1.5): can fire silently; failure mode is often invisible
Scheduled Tasks (×3): each creates output that becomes an inbox requiring attention
Active Agents (×4): each has a task queue, error stream, and output stream
Plugins (×1): low but nonzero — can break on Claude Code updates

Stale multiplier: add 50% to the base score for each stale or unsponsored item (items that add overhead without delivering value).

Inbox Load Score (ILS):

ILS = min(100, base_score + stale_penalty)

ILS Range	Interpretation
0–20	Lean — setup is under control
21–40	Manageable — some cleanup opportunity
41–60	Heavy — tool proliferation is creating real drag
61–80	Overloaded — more time managing tools than using them
81–100	Critical — the tools are the inbox

Phase 3: Output

## Inbox Load Audit

| Category         | Count | Stale/Unsponsored | Weighted Score |
|------------------|-------|-------------------|----------------|
| Skills           |   X   |        Y          |      Z         |
| MCP Servers      |   X   |        Y          |      Z         |
| Hooks            |   X   |        Y          |      Z         |
| Scheduled Tasks  |   X   |        Y          |      Z         |
| Active Agents    |   X   |        Y          |      Z         |
| Plugins          |   X   |        Y          |      Z         |
| **Total**        |       |                   |   **ILS: XX**  |

**Status: [Lean / Manageable / Heavy / Overloaded / Critical]**

### Top 3 Reduction Recommendations

[Ordered by impact-per-effort:]

1. [Category] — [specific item or group] — [consolidation or removal action]
2. [Category] — [specific item or group] — [consolidation or removal action]
3. [Category] — [specific item or group] — [consolidation or removal action]

### Stale Surface List

[Items that scored the stale multiplier — prime candidates for removal or cold-archiving:]
- [item name] ([category]) — last active: [date or "unknown"]

Phase 4: Recommendations Logic

Prioritize recommendations using this order:

Scheduled tasks with no known consumer — each fires, produces output, and has nobody reading it. Highest inbox-creation rate per item.
Stale skills with no agent sponsor and no callsite reference — dead weight that slows session startup and adds cognitive load.
Duplicate-coverage items — two skills doing the same thing (e.g., two audit skills for the same domain). Recommend consolidation.
MCP servers not referenced in any active skill or CLAUDE.md rule — load at startup, consume auth budget, never used.
Plugins with missing install paths — load at startup, fail silently, provide no value.

Verification

A good audit:

Lists every discovered item, not a sample
Does not recommend deleting anything without confirming it has no active sponsor
Stale multiplier is applied only to items with no verifiable activity, not just old items
The ILS reflects reality: a user with 3 heavy scheduled agents should score higher than one with 20 passive skills
Recommendations are specific (name the item) not categorical (don't say "reduce MCP servers" — say which one and why)

Source

Extracted from Nate Kadlac newsletter (2026-05-05) — "The Anticipation Gap: Why 4 Problems Have to Be Solved Together for Consumer AI to Work" — diagnosis that reactive agents collectively produce "another inbox." Formalized into a measurable score using the skill-audit and token-burn-auditor audit patterns already in the the skill library library.