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AD capacity-planner

Use when sizing an ops or support team, building a headcount plan or checking utilization risk. Models queues with Erlang C in capacity_modeler.py, analyzes utilization with utilization_analyzer.py and orders hires with hiring_sequencer.py.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI 8 files · 3 scripts body ≈ 2 770 tokens Open the sourcegithub.com↗ analyzed 8 d ago

Models queues with Erlang C in capacitymodeler.py, analyzes utilization with utilizationanalyzer.py and orders hires with hiringsequencer.py.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorCustomer supportPeople and hiringtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
97
Quality 40%
92
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token references/capacity_anti_patterns.md:134
      High-entropy token-like string (may be an id, hash or a credential)
      ### 8. No-S…nts
    • low Secrets in code secret-high-entropy-token SKILL.md:136
      High-entropy token-like string (may be an id, hash or a credential)
      3. Igno…lan (Bersin)
    • low Secrets in code secret-high-entropy-token SKILL.md:141
      High-entropy token-like string (may be an id, hash or a credential)
      8. No-s…nts (Hopp & Spearman, Reinertsen)

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 38 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2770 tokens
    • low 11 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 240: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.