Bottleneck diagnosis
Find where cases actually wait using the log rather than the process diagram.
— Operations & Process
Process mining from event logs
Computes activity volumes and profiles from an event log, then diagnoses bottlenecks, rework loops and variant paths with root-cause hypotheses and capacity gains.
- Operations & Process
- Human review built in
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~15 minutes
For a full population, not a sample
No coding required
- Input
- An event log with case id, activity, timestamp and resource
- Output
- Computed activity volumes and log profile
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Process mining from event logs for
Rework and variant analysis
Quantify rework loops and off-path variants that never appear in the documented process.
Capacity cases
Attach a capacity gain to each bottleneck so remediation can be prioritised.
What it works from
- An event log with case id, activity, timestamp and resource
- Confirmation of what a case represents
- Any SLA or target cycle times
What you get back
- Computed activity volumes and log profile
- Bottlenecks, rework loops and variant paths with root-cause hypotheses
- Charts and a capacity-gain estimate per issue
— HOW IT BEHAVES
How Process mining from event logs produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Every row of the end-to-end process
Each row of your export is processed on the same basis, so no process step is skipped however long the table is.
Scope settled before it runs
Ambiguities about the end-to-end process are resolved with you up front, so the run does not quietly assume the wrong scope.
The pattern charted
Movement across the end-to-end process is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the end-to-end process are written up as a document that reads like professional output, with each claim tied back to a process step.
Roll-up alongside process step-level detail
Detail rows are summarised into the grouped view of the end-to-end process without losing the underlying process steps.
Why this is expensive by hand
Event logs hold the truth about how a process really runs, and almost nobody has time to mine them. In practice it shows up as bottleneck diagnosis: find where cases actually wait using the log rather than the process diagram. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 1–2 weeks of capacity for it.
How this Skill produces it
Here the same job runs as a Skill. Your material goes in; computed activity volumes and log profile comes out, alongside bottlenecks, rework loops and variant paths with root-cause hypotheses. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. Net effect: 1–2 weeks down to ~15 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Who it's for
- Operations and continuous improvement leads
- Transformation and automation teams
- Service delivery and shared services managers
- Consultants running process diagnostics
Run it in Skillsize — or export it anywhere
Process mining from event logs exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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