用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/a5c-ai/babysitter --skill tolerance-stackup命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
正在显示 SKILL.md
基于 SOC 职业分类
| name | tolerance-stackup |
| description | Skill for dimensional tolerance analysis and stack-up calculations |
| allowed-tools | ["Read","Write","Glob","Grep","Bash"] |
| metadata | {"specialization":"mechanical-engineering","domain":"science","category":"design-development","priority":"medium","phase":8,"tools-libraries":["CETOL 6 Sigma","3DCS","VSA","Excel"]} |
| graph | {"domains":["domain:mechanical-engineering"],"skillAreas":["skill-area:physics-simulation","skill-area:mathematical-reasoning","skill-area:motion-planning"],"roles":["role:systems-integration-engineer","role:research-engineer"]} |
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.
| 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 |
Gap = Nominal gap +/- sum of all tolerances
For a simple assembly:
Gap_min = Nominal - sum(all positive contributors)
Gap_max = Nominal + sum(all negative contributors)
Or using sensitivity:
Gap = sum(ai * xi)
Tolerance = sum(|ai| * ti)
Where:
ai = sensitivity coefficient (+1 or -1)
xi = nominal dimension
ti = tolerance on dimension i
Define positive direction:
- Dimensions adding to gap: positive (+1)
- Dimensions subtracting from gap: negative (-1)
Example (shaft in hole):
Gap = Hole_dia - Shaft_dia
Hole: +1 (increases gap)
Shaft: -1 (decreases gap)
Statistical tolerance (RSS):
T_rss = sqrt(sum(ti^2))
For unequal distributions (weighted):
T_rss = sqrt(sum((ai * ti)^2))
Assumes:
- Normal distribution
- Independent variables
- Process centered at nominal
Cp = (USL - LSL) / (6 * sigma)
Cpk = min((USL - mean)/(3*sigma), (mean - LSL)/(3*sigma))
For 6-sigma quality:
Cpk >= 2.0
PPM defective < 3.4
For tolerance analysis:
sigma = T / (3 * k)
Where k depends on desired Cpk:
k = 3 for Cpk = 1.0
k = 4 for Cpk = 1.33
k = 6 for Cpk = 2.0
1. Define distribution for each dimension
- Normal: mean, sigma
- Uniform: min, max
- Skewed: appropriate parameters
2. Generate random samples (N = 10,000+)
3. Calculate assembly result for each sample
4. Analyze output distribution
5. Determine percent out-of-spec
| Scenario | Distribution | Parameters |
|---|---|---|
| Machined feature | Normal | Nominal, T/6 (Cpk=2) |
| Purchased part | Normal/Uniform | Per vendor data |
| Press fit | Truncated normal | Limits at tolerance |
| Unknown process | Uniform | Min, max |
Position tolerance contribution:
Dia_positional / 2 = linear contribution (per direction)
For MMC position:
Contribution = (Position_tol + Bonus_tol) / 2
Bonus tolerance:
Bonus = |Actual_size - MMC_size|
Stack-up must follow datum precedence:
1. Establish primary datum (constrains normal)
2. Establish secondary datum (constrains one rotation)
3. Establish tertiary datum (constrains remaining DOF)
Feature control frame specifies:
|Position|0.5 MMC|A|B|C|
Define the Problem
Create the Loop Diagram
Gather Data
Perform Calculation
Document Results
If tolerance too tight:
1. Increase gap nominal (if possible)
2. Tighten critical dimension tolerances
3. Add adjustment or shim
4. Change assembly method
5. Accept higher defect rate
If tolerance too loose:
1. Relax non-critical tolerances
2. Reduce manufacturing cost
Approximate relationship:
Cost ~ 1 / Tolerance^n
Where n ~ 1.5 to 2 for machining
Tighten tolerances on:
- Lower cost features
- Higher sensitivity contributors
{
"analysis_name": "string",
"requirement": {
"type": "gap|clearance|interference|alignment",
"nominal": "number",
"min": "number",
"max": "number"
},
"contributors": [
{
"name": "string",
"nominal": "number",
"tolerance": "number (bilateral half)",
"direction": "+1|-1",
"distribution": "normal|uniform",
"cpk": "number (if normal)"
}
],
"method":
{
"analysis_summary": {
"requirement": {
"min": "number",
"max": "number"
},
"nominal_result": "number"
},
"worst_case": {
"min_result": "number",
"max_result": "number",
"pass_fail": "pass|fail",
"margin": "number"
},
"statistical": {
"mean": "number",
"sigma": "number",
"min_3sigma": "number",
"max_3sigma":