quantification-metrics-dos
Sub-skill of quantification-metrics: Do's (+1).
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/dos/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How quantification-metrics-dos Compares
| Feature / Agent | quantification-metrics-dos | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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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