---
title: "capacity-planner — Claude Code Plugin & Agent Skill"
description: "Use when an ops leader (Director of CX, Head of Support, VP Ops, Head of BizOps, Head of IT ops, Head of Finance ops) is sizing ops capacity. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
---

# capacity-planner

<div class="page-meta" markdown>
<span class="meta-badge">:material-cog-outline: Business Operations</span>
<span class="meta-badge">:material-identifier: `capacity-planner`</span>
<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/business-operations/skills/capacity-planner/SKILL.md">Source</a></span>
</div>

<div class="install-banner" markdown>
<span class="install-label">Install:</span> <code>claude /plugin install business-operations-skills</code>
</div>


Sizing tool for **ops teams that handle queued work** — Support, CX,
Customer Success, BizOps, IT ops, Finance ops. Built on Erlang-C
queueing theory, Little's Law, and the operational-leadership canon
(Fournier, Larson, Cleveland, Reinertsen). Deterministic, stdlib-only,
no LLM calls.

## Purpose

You are an ops leader sized 15 → 35 with no idea how the 35-person org
will actually behave at peak load. Or you are at 88% utilization and
SLA is starting to slip. Or you have a hiring budget approved and need
to sequence it across four quarters without burning out the existing
team. This skill answers those questions with arithmetic, not vibes.

It produces three artifacts:

1. **Capacity sizing** at 70/80/90% utilization against P50/P90/P99
   demand, with P(SLA breach) at each point and a SAFE/WATCH/AT_RISK/CRITICAL
   risk band.
2. **Utilization health** at the per-member traffic-light level plus a
   team verdict (HEALTHY/SQUEEZED/OVERLOADED/UNBALANCED).
3. **12-month quarterly hiring plan** accounting for ramp curves,
   attrition, QoQ demand growth, and span-of-control manager triggers.

## When to use

- **Annual ops capacity planning** (October-November for the following
  fiscal year).
- **Quarterly re-sizing** if demand changed >15% or attrition spiked.
- **Pre-budget defense** — the math that justifies the headcount ask
  to your CFO.
- **Diagnostic** when an ops team is missing SLA and you need to know
  whether it's a sizing problem, a process problem, or a bottleneck
  problem.
- **M&A / new-segment launch** modeling — sizing a new team or
  combined org.

## Workflow

1. **Intake demand**. Pull P50/P90/P99 daily ticket/case volume from
   your work system (Zendesk, Intercom, JSM, ServiceNow, Salesforce).
   If you only have averages, stop and pull the distribution. Single-
   point demand estimates are the most expensive anti-pattern in ops.
2. **Model throughput**. Run `capacity_modeler.py` with your demand,
   AHT, SLA target, current FTE, and shrinkage. Use `--profile` for
   your function (support / cx / bizops / finance-ops / it-ops). Read
   the 80%-utilization row — that's your sizing point.
3. **Flag utilization risk**. Run `utilization_analyzer.py` against
   your current team's actual utilization data. Anyone >85% sustained
   is a throughput-collapse risk per Reinertsen. Spread >30 percentage
   points across team means UNBALANCED — fix that before hiring.
4. **Sequence hiring**. Run `hiring_sequencer.py` with current FTE,
   target EOY, ramp time, attrition, and growth. It will front-load
   hires (Q1 35%, Q4 15%), apply ramp curves, and trigger a manager
   hire when span of control crosses 7 ICs/manager.
5. **Walk the Forcing-question library** (see below). One question at
   a time. Do not skip ahead. Answers must be written down before
   you commit the plan.

## Scripts

- `scripts/capacity_modeler.py` — Erlang-C sizing with shrinkage
  adjustment and P50/P90/P99 breach probabilities. `--profile`
  for industry defaults.
- `scripts/utilization_analyzer.py` — per-member traffic-light +
  team-level health verdict with variance detection.
- `scripts/hiring_sequencer.py` — 12-month quarterly plan with ramp,
  attrition, growth, max-hires-per-quarter constraint, and
  manager-trigger logic.

All three accept `--input <path>` (JSON), `--output {markdown,json}`,
`--sample` (built-in example), and `--help`. Stdlib only.

## Quick example

```bash
# Emits an Erlang-C capacity model (required headcount + P50/P90/P99 breach probabilities) for the built-in example
cd business-operations/skills/capacity-planner && python3 scripts/capacity_modeler.py --sample
```

## References

- `references/queueing_theory_canon.md` — Erlang, Little, Hopp &
  Spearman, Reinertsen, Kingman, Cleveland, ITIL, Armony et al. (8
  sources). The math.
- `references/ops_workforce_planning_canon.md` — Fournier, Larson,
  Google SRE Workbook, Frei, Lawler, Bersin, Gartner, Grove (8
  sources). The people factors.
- `references/capacity_anti_patterns.md` — 11 named anti-patterns
  with cited sources, tool guards, and the meta-discipline that
  Lencioni + Goldratt + Christensen impose. (8+ named sources.)

## Assets

- `assets/capacity_brief_template.md` — 20-minute fill-out template
  with JSON skeletons for all three tools and an output checklist.

## Assumptions

This skill assumes:

- Work is **queued** (tickets, cases, work items) — not project-style.
  If your team's work isn't queued, this is the wrong skill.
- Demand has a **stationary-enough distribution** within a quarter.
  Step-changes (new product launch, M&A, regulatory shift) require
  re-running mid-quarter.
- You have **at least 90 days of historical demand data** to compute
  P50/P90/P99. If not, generate the distribution from your sales /
  user-base forecast first.
- Service is **single-class** within a queue. If you have hard
  priority tiers (P1/P2/P3 with class-specific SLAs), model each as
  a separate queue and sum.
- **Channels are modeled coherently.** Multi-channel teams use the
  appropriate `--profile` with built-in shrinkage premium.

## Anti-patterns

See `references/capacity_anti_patterns.md` for the full taxonomy with
sources. Top eight:

1. Plan-to-100%-utilization (Reinertsen Principle 12)
2. Treat-ramp-as-instant (Larson)
3. Ignore-attrition-in-12-month-plan (Bersin)
4. Hire-ICs-forever-with-no-manager-trigger (Fournier)
5. Size-to-P50-demand-only (Cleveland)
6. No-shrinkage-adjustment (Cleveland, SRE Workbook)
7. Single-channel-model-for-multi-channel-work (Gartner, Kingman)
8. No-surge-plan-for-P99-events (Hopp & Spearman, Reinertsen)

## Distinct from

- **`c-level-advisor/vpe-advisor`** measures *engineering* throughput
  via DORA 4 metrics, story points, deployment frequency, and cycle
  time bottlenecks. It is for engineering teams shipping code. This
  skill is for ops teams handling tickets/cases. Different unit of
  work, different math (Erlang-C vs. DORA), different bottleneck
  (queueing-blind staffing vs. WIP + lead time).
- **`c-level-advisor/chro-advisor`** does *strategic* workforce
  planning (1-5 year capability portfolios, talent supply, leadership
  succession). This skill does *operational* 0-12 month capacity
  sizing against demand. Per Lawler: conflating them gets you hired
  into the wrong jobs.
- **`project-management/*`** tracks delivery throughput on projects
  (Jira velocity, sprint capacity). This skill sizes around steady-
  state queued work.
- **Sibling `process-mapper`** *finds* the bottleneck. This skill
  *sizes the team around* a known bottleneck. Order of operations:
  process-mapper first → capacity-planner second. Hiring around the
  wrong constraint wastes the hires.
- **`business-growth/cs-coverage`** (if it exists) sizes Customer
  Success coverage by ARR/CSM ratio and segment. This skill sizes by
  queued work volume (tickets, cases, escalations). For a CS team
  that handles both relationship work AND a ticket queue, run both.

## Forcing-question library (Matt Pocock grill discipline)

**Discipline**: walk these one at a time. Do not skip ahead. Answers must
be written down. If you can't answer one, that is your next investigation.

### Q1 — "What is your bottleneck, and have you confirmed it empirically?"

**Recommended answer**: a named, measured stage in the workflow with
queue-time data showing where work waits. Not a vibe. Not "escalations
take too long". An actual measured queue.

**Why it's the first question**: Goldratt (*The Goal*, 1984) — every
system has exactly one binding constraint at a time. Sizing around the
wrong constraint wastes hires entirely. If you do not know your
bottleneck, run `process-mapper` BEFORE this skill.

**Canon**: Eli Goldratt, *The Goal* (1984); Reinertsen, *Principles of
Product Development Flow* (2009).

### Q2 — "What service trade-off are you accepting?"

**Recommended answer**: a written, explicit choice — fast vs. empathetic,
broad vs. deep, low-cost vs. high-quality. Frances Frei is unambiguous:
you cannot win all four. The team that tries wins zero.

**Why it matters**: AHT, SLA, and shrinkage inputs are the operational
expression of this trade-off. If they don't agree (e.g., you set AHT for
"empathy" but SLA for "speed"), the plan is internally inconsistent.

**Canon**: Frances Frei & Anne Morriss, *Uncommon Service* (HBR Press,
2012).

### Q3 — "What's your demand P90, and what's the gap to your P99?"

**Recommended answer**: two specific numbers from the last 90 days of
data, with the calendar context of each (e.g., "P90 was 480 tickets/day
on normal Tuesdays; P99 was 720 on the day after the November release").
A team sized to P50 misses SLA half the time. A team sized to P99
overstaffs by 30-50%. P90 is the right operating sizing point per
Cleveland.

**Canon**: Brad Cleveland, *Call Center Management on Fast Forward* (4th
ed., 2019); A.K. Erlang, *The Theory of Probabilities and Telephone
Conversations* (1909).

### Q4 — "At your planned utilization, what is P(SLA breach) at P90 and at P99?"

**Recommended answer**: two probabilities, computed (not guessed) from
Erlang-C with your specific N, AHT, and SLA target. If P(breach at P90)
> 10% you are understaffed at the sizing point. If P(breach at P99) >
50% you have no surge plan and the next peak event will be visible to
the CEO.

**Canon**: Erlang (1909); Hopp & Spearman, *Factory Physics* (3rd ed.,
2008), VUT equation.

### Q5 — "Have you budgeted replacement hires for the attrition you'll see this year?"

**Recommended answer**: yes, with a specific number. At 30% annual
attrition (Bersin BPO midpoint), a 20-FTE team loses ~6 people this year.
If your "add 5 net" plan is actually a "hire 11" plan, the recruiting
volume changes drastically. Anti-pattern #3.

**Canon**: Bersin/Deloitte talent benchmarks (2015-2023); Edward Lawler,
*Strategic Workforce Planning* (USC CEO, 2008).

### Q6 — "When does span of control trigger a manager hire, and who is the candidate?"

**Recommended answer**: a specific quarter (from `hiring_sequencer.py`)
and at least one identified candidate (internal lead or external hire).
Past 7 ICs/manager, 1:1s degrade, feedback cycles slip, attrition
climbs. Past 10 you have a coverage crisis. Hire the manager BEFORE
crossing 10, not after.

**Canon**: Camille Fournier, *The Manager's Path* (O'Reilly, 2017),
ch. 5; Andy Grove, *High Output Management* (1983).

### Q7 — "What is your surge plan for the P99 day?"

**Recommended answer**: an explicit, documented plan — overflow tier,
BPO contracted capacity, on-call rotation, executive escalation tree,
OR a written degradation contract that says "on P99 days we extend SLA
to X minutes and notify customers proactively". If the answer is "we'll
figure it out", the P99 day is a fire visible to the board.

**Canon**: Hopp & Spearman, *Factory Physics* (2008); Reinertsen (2009)
on capacity-margin discipline.

---

**Walk these seven in order. One at a time. Write the answers down. The
plan you submit is only as defensible as your answers to these seven
questions.**
