capacity-planner – Other skill for AI agents

capacity-planner

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

# 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 ut

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