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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, building a headcount plan, modeling utilization risk, planning Q3 capacity or annual support capacity, or designing CS coverage — and needs Erlang-C queueing math, P90 demand sizing, shrinkage-adjusted FTE, manager-trigger thresholds, and a quarterly hiring sequence with ramp + attrition. Apply when sustained team utilization is above 80% or when the team
Passed 18 of 21 test cases with the skill, and 10 of 21 without it.
and 6 more in the full report
9 fixed10 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to quickly run a test demonstration of the Python queue modeler without providing an external JSON input file. What command-line flag invokes the built-in example dataset?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies `--sample` as the flag to execute built-in demonstration data. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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:
test cases passed, vs 10 / 21 without
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