tolerance-stackup
Skill for dimensional tolerance analysis and stack-up calculations
Profile →npx skills add a5c-ai/babysitter --skill tolerance-stackup --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Tolerance Stack-Up Analysis Skill ## Purpose The Tolerance Stack-Up Analysis skill provides capabilities for dimensional tolerance analysis and stack-up calculations, enabling verification of assembly fits and functional requirements through systematic tolerance chain analysis. ## Capabilities - Worst-case tolerance analysis - Statistical (RSS) tolerance analysis - Monte Carlo tolerance simulation - GD&T-based stack-up analysis - Assembly feasibility verification - Tolerance allocation optimization - CETOL/3DCS integration - Stack-up report generation ## Usage Guidelines ### Tolerance Analysis Methods #### Method Comparison | Method | Approach | Application | Result | |--------|----------|-------------|--------| | Worst-case | All tolerances at limit | Safety critical | Maximum variation | | RSS | Statistical combination | High volume production | Probable variation | | Monte Carlo | Random sampling | Complex assemblies | Distribution | | 6-Sigma | Process capability | Quality control | Defect rate | ### Worst-Case Analysis #### Linear Stack-Up ``` Gap = Nominal gap +/- sum of all tolerances For a simple assembly: Gap_min = Nominal - sum(all positive contributors) Gap_max = Nomin
- Purpose
- Capabilities
- Usage Guidelines
- Tolerance Analysis Methods
- Worst-Case Analysis
- Statistical Analysis
- Monte Carlo Simulation
- GD&T in Stack-Ups
- Analysis Process
- Tolerance Allocation
- Process Integration
- Input Schema
- Output Schema
- Best Practices
What does the tolerance-stackup skill do?
Skill for dimensional tolerance analysis and stack-up calculations
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill tolerance-stackup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.