claude-skills is a large collection of reusable skills, commands, plugins, and supporting resources for coding agents across engineering, business, research, compliance, and productivity work. It is used by people who want agents such as Claude Code, Codex, Gemini CLI, or Cursor to follow specialized workflows. The catalogue contains selected skills and settings from the collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add alirezarezvani/claude-skills --skill capacity-plannergit clone --depth 1 https://github.com/alirezarezvani/claude-skillsWrote 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/skills/alirezarezvani/claude-skills/capacity-planner)<a href="https://agentmods.dev/skills/alirezarezvani/claude-skills/capacity-planner"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/capacity-planner/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/skills/alirezarezvani/claude-skills/capacity-planner"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/capacity-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 1 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00168 | $0.03148 |
| Opus 5.5 | $0.00067 | $0.01259 |
| Sonnet 5.5 | $0.00034 | $0.00630 |
| Haiku 4.5 | $0.00017 | $0.00315 |
Grade A, and why
capacity-planner 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 yesterday.
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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- 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.
- Utilization health at the per-member traffic-light level plus a team verdict (HEALTHY/SQUEEZED/OVERLOADED/UNBALANCED).
- 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
- 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.
- Model throughput. Run
capacity_modeler.pywith your demand, AHT, SLA target, current FTE, and shrinkage. Use--profilefor your function (support / cx / bizops / finance-ops / it-ops). Read the 80%-utilization row — that's your sizing point. - Flag utilization risk. Run
utilization_analyzer.pyagainst 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. - Sequence hiring. Run
hiring_sequencer.pywith 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. - 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.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/capacity_brief_template.md 4.9 KB
- references/capacity_anti_patterns.md 8.8 KB
- references/ops_workforce_planning_canon.md 6.2 KB
- references/queueing_theory_canon.md 5.8 KB
- scripts/capacity_modeler.py 16 KB runs code
- scripts/hiring_sequencer.py 12 KB runs code
- scripts/utilization_analyzer.py 9.7 KB runs code
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.
- yesterday Changed · +260 lines · +168 tokens per session 13edfdca1aac
- 1mo ago First seen · 1 lines · 0 tokens per session scan A 0ebfd6237c95
capacity-planner is a skill published in the GitHub repository alirezarezvani/claude-skills (28,666 stars, last pushed 2d ago), licensed MIT. It adds 168 tokens to every session and 3,148 once invoked, about $0.0007 per session on Opus 5.5. 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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