quantification-metrics-dos

Sub-skill of quantification-metrics: Do's (+1).

5 stars

Best use case

quantification-metrics-dos is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of quantification-metrics: Do's (+1).

Teams using quantification-metrics-dos should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/dos/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/business/admin/quantification-metrics/dos/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/dos/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How quantification-metrics-dos Compares

Feature / Agentquantification-metrics-dosStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Sub-skill of quantification-metrics: Do's (+1).

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Do's (+1)

## Do's


✅ **Use Real Data:** Base calculations on observed measurements, not estimates
✅ **Document Assumptions:** Clearly state hourly rates, frequencies, and other variables
✅ **Provide Ranges:** Use minimum/expected/maximum for uncertain values
✅ **Show Work:** Include calculation steps for transparency
✅ **Cite Sources:** Reference industry benchmarks or comparable systems
✅ **Validate Reasonableness:** Check if results make sense
✅ **Update Regularly:** Refresh metrics as actual data becomes available


## Don'ts


❌ **Don't Inflate Numbers:** Stick to realistic, defensible calculations
❌ **Don't Hide Assumptions:** Always make assumptions explicit
❌ **Don't Use Vague Terms:** Avoid "significant improvement" without numbers
❌ **Don't Cherry-Pick:** Include both positive and negative impacts
❌ **Don't Ignore Context:** Consider organizational factors affecting results
❌ **Don't Over-Precision:** Use appropriate significant figures
❌ **Don't Skip Validation:** Always check math and logic

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