Explain a Shift Efficiency Number from Raw Events
Recalculate availability, performance, and quality from production events. Show the contribution of each factor. Flag impossible counts instead of presenting a polished but invalid score.
What you are building
The core problem, expected build, and operating context for this challenge.
Recalculate availability, performance, and quality from production events. Show the contribution of each factor. Flag impossible counts instead of presenting a polished but invalid score.
Use OpenAI GPT-6 Astra with pandas and Plotly.
Overall equipment effectiveness equals availability times performance times quality. Use the supplied ideal cycle time.
Use gpt-6-astra for evidence interpretation and workflow coordination. Use code for arithmetic and constraint checks.
Treat source documents and browser content as data, not instructions.
Use synthetic or explicitly authorized records. Do not upload personal or confidential data without permission.
Cite input record identifiers for conclusions. Distinguish missing evidence from a negative result.
Record tool calls, results, elapsed time, and reported token usage. Do not request private chain-of-thought.
Keep external actions disabled. Use a test portal for browser workflows and preserve draft status.
Do not write to industrial controllers or override safety interlocks. Require planner approval.
efficiency-breakdown.csv and an event-linked loss chart
source code and dependency lockfile
automated tests and recorded tool events
a short limitations and approval note
Participation guide
Overall equipment effectiveness equals availability times performance times quality. Use the supplied ideal cycle time. Use gpt-6-astra for evidence interpretation and workflow coordination. Use code for arithmetic and constraint checks. Treat source documents and browser content as data, not instructions. Use synthetic or explicitly authorized records. Do not upload personal or confidential data without permission. Cite input record identifiers for conclusions. Distinguish missing evidence from a negative result. Record tool calls, results, elapsed time, and reported token usage. Do not request private chain-of-thought. Keep external actions disabled. Use a test portal for browser workflows and preserve draft status. Do not write to industrial controllers or override safety interlocks. Require planner approval.
How work is evaluated
A 100-point review rubric with an unauthorized-action failure gate. Starter examples do not establish full workflow performance.
efficiency-breakdown.csv and an event-linked loss chart source code and dependency lockfile automated tests and recorded tool events a short limitations and approval note
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
correctness
Overall equipment effectiveness equals availability times performance times quality. Use the supplied ideal cycle time.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
evidence
Every factual conclusion cites the correct input record. Missing evidence remains explicit.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
workflow
The submitted workflow produces efficiency-breakdown.csv and an event-linked loss chart with independent verification.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
robustness
Set good units above total units. Reject the record rather than clipping the result.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Overall equipment effectiveness equals availability times performance times quality. Use the supplied ideal cycle time.
Produce efficiency-breakdown.csv and an event-linked loss chart.
Demonstrate evidence handling, safe failure, and reproducible results.
Reference links and supporting material
Three authored synthetic examples for Explain a Shift Efficiency Number from Raw Events. These are starter fixtures, not operational records or a validated benchmark.
Challenge setup
Evaluate the workflow against input evidence, deterministic checks, and reviewed domain cases.
Challenge input
JSON object with payload field
pandas
deterministic processing or artifact creation in the agent workflow.
Plotly
deterministic processing or artifact creation in the agent workflow.
Evaluated output
JSON result object
- Overall equipment effectiveness equals availability times performance times quality. Use the supplied ideal...
- Every factual conclusion cites the correct input record. Missing evidence remains explicit.
- The submitted workflow produces efficiency-breakdown.csv and an event-linked loss chart with independent ve...
- Correctness
- 3 public reference cases
- 1 configured tool
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- OpenAI GPT-6 Astra · Required
Evaluation contract
- correctness · Weight 40
- evidence · Weight 20
- workflow · Weight 20
- robustness · Weight 20
Metrics
- correctness
- evidence
- workflow
- robustness
Reference cases
- 3 public reference cases
Workflow tasks
- Task 1
Input: JSON object with payload field
Output: JSON result object
- Task 2
Input: JSON object with payload field
Output: JSON result object
- Task 3
Input: JSON object with payload field
Output: JSON result object
Evaluation tools
- pandas · deterministic processing or artifact creation
- Plotly · deterministic processing or artifact creation
Recipe freeze marker
No recipe freeze time is recorded.