PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsnpx agentmods add rules/mohitagw15856/pm-claude-skills/support-staffing-modelWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/support-staffing-model)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/support-staffing-model"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/support-staffing-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/support-staffing-model"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/support-staffing-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.00795 |
| Opus 5 | $0.00042 | $0.00398 |
| Sonnet 5 | $0.00017 | $0.00159 |
| Haiku 4.5 | $0.00008 | $0.00080 |
Grade A, and why
support-staffing-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Support Staffing Model
Queues are counterintuitive: at high occupancy, one extra contact per hour explodes wait times, and "tickets ÷ tickets-per-agent" staffing walks teams straight into the cliff. Erlang C is the century-old math call centers run on; this skill runs it for you, honestly labelled.
Required Inputs
- Contacts per hour (peak hour, not daily average — queues die at peaks) and average handle time in minutes.
- The SLA — "X% answered within Y seconds/minutes". If none exists, propose one before staffing to it.
- Shrinkage — the fraction of paid time agents aren't available (meetings, breaks, training). Teams that skip this understaff by 30-40%; default 0.3.
Output Format
- The staffing table — for load scenarios (0.8×, 1×, 1.25×, 1.5×): agents on-queue, rostered headcount after shrinkage, achieved service level, average speed of answer, occupancy.
- The occupancy warning — anywhere occupancy exceeds ~90%, say plainly: the SLA may hold while the team burns out; staff for the humans.
- The folklore contrast — the naive tickets-per-agent number next to the Erlang answer, so the reader sees what the old method was hiding.
- Model limits, stated — M/M/c assumes Poisson arrivals; real queues are burstier, so these are floors.
Programmatic Helper
This skill ships scripts/erlang_staffing.py — zero dependencies; run it rather than approximating:
python3 scripts/erlang_staffing.py plan staffing.xlsx --arrivals 120 --aht 6 --sla 0.8 --answer-in 60 --shrinkage 0.3
Prints the base case (base 15 on-queue / 22 rostered · SL 81% · ASA 38s · occ 80%) and writes an .xlsx with editable assumption cells and the scenario table. Requires a code-execution environment.
Quality Checks
- Numbers come from the script's Erlang C computation, quoted — never estimated in prose
- Shrinkage is applied and its value stated; a 0% shrinkage plan is flagged as fiction
- Occupancy appears next to every scenario, with the >90% burnout warning where it triggers
- Peak-hour arrivals were used, or the answer says "daily average used — peaks will breach"
- The M/M/c floor-not-ceiling caveat is present
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 49 lines · 84 tokens per session scan A 730f1724e576
support-staffing-model is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It adds 84 tokens to every session and 795 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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