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Find each line’s bottleneck and recommend which station to address first.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "constraining-station-identification" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/aibast-agents-library/main/solutions/product-line-optimization/manual/skills/aibast_bottleneck_plo02/SKILL.md 2. Save it as ~/.claude/skills/aibast_bottleneck_plo02/SKILL.md 3. Reload skills and tell me it's ready
Using each line’s station table, identify the constraining station on every line, and explain cycle time, takt, over-takt margin, and defect rate. End with which station should be addressed first across the plant.
Returns the bottleneck station for each line, supporting metrics, and a plant-wide priority order.
Combine cycle-versus-takt with defect category mix to explain why these stations are bottlenecks, and list the supporting defect evidence.
Provides bottleneck reasoning and the main defect categories for each line.
Summarize each line’s constraining station, why it is constrained, and the recommended action order in a concise report-ready tone.
Produces a concise conclusion suitable for a team meeting update.
A manufacturing engineer needs to see which station is slowing each line and by how much. This skill turns station tables and defect data into an actionable bottleneck conclusion.
When the team can only tackle one station first, this skill compares over-takt margins and recommends the best first target.
It also supports the bottleneck explanation with the dominant defect categories for each line, alongside cycle time and takt.
This document describes a skill for identifying constraining stations on three production lines. It compares cycle time against takt time, uses defect rates and defect mix as supporting evidence, and returns a line-by-line bottleneck with a plant-wide priority recommendation. It also defines the output structure, limits the skill to packaged synthetic data, and includes a fallback rule for unknown lines or stations.
Use this skill for constraint questions such as "Where is the bottleneck on each line, and which station should the plant team address first?", "what is slowing each line," or "which station is over takt." Evaluate all three lines.
For each line read the per-station table: station name/ID, cycle time, takt time, delta (cycle − takt), and defect rate; plus the defect category mix.
Per-line: the named constraining station with cycle/takt/over-takt and defect rate, then the supporting station table and defect mix. Close with the cross-plant "address first" recommendation.
> Synthetic pilot data; figures are planning estimates, not a live ERP, IoT, or Power BI reading.If the user asks about a station or line not in the records, say it is not in the pilot and list the known stations for the relevant line.
It identifies the constraining station on each line—the likely bottleneck—and recommends which station to address first across the plant. The decision uses cycle time, takt time, over-takt margin, and defect evidence.
It reads each line’s station table: station name/ID, cycle time, takt time, delta (cycle minus takt), defect rate, and defect category mix. It uses only the packaged data.
If it is not in the pilot data, it says so and lists the known stations for the relevant line.
Helps assess request context and approval level from synthetic procurement records.
Generate sentiment splits and recent excerpts from synthetic records.
Extract a source-coded problem list from synthetic clinical evidence deterministically.
Frames retirement scenarios with conservative, base, and higher-volatility assumptions.
Extract and summarize medication inventories from packaged synthetic clinical evidence.
Prioritize churn reviews with transparent evidence and suggested next steps.
Optimize production schedules, sequencing, bottlenecks, changeovers, and disruption response.
Turn line performance into a three-shift staffing and output plan.
Analyze workflow bottlenecks and suggest practical process improvements and automation.
Execute detailed plans in batches with review checkpoints and course correction.
Plan task capacity and queue handling to improve execution and resource allocation.