process-mining-assistant

Wattysaid's avatarfrom Wattysaid

Perform an end-to-end process mining analysis via a command-line workflow that progressively ingests, profiles, cleans, mines and reports on event logs using PM4Py. The workflow generates stage-based artefacts (including versioned notebooks) and pauses at decision checkpoints so the user can validate findings and choose how to proceed.

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[Tool Routing and Orchestration]

When & Why to Use This Skill

This Claude skill acts as a comprehensive orchestrator for end-to-end process mining workflows. It leverages PM4Py to automate the progression through data ingestion, profiling, cleaning, and reporting. By enforcing decision checkpoints and strict reproducibility safeguards, it ensures that complex analytical tasks are executed with high auditability and version control, making it an essential tool for data-driven process optimization.

Use Cases

  • End-to-End Process Discovery: Automatically transform raw event logs into actionable process models and comprehensive reports through a structured, multi-stage pipeline.
  • Guided Analysis Resumption: Efficiently resume interrupted workflows from specific stages, such as performance analysis or log cleaning, without losing progress or data integrity.
  • Audit-Ready Data Science: Maintain strict reproducibility in business process analysis by using hash enforcement and versioned notebooks to track every decision and data transformation.
  • Standardized Workflow Management: Orchestrate multiple specialized process mining sub-skills (pm-01 to pm-10) to ensure consistent application of organizational standards and best practices.
namepm-00-orchestrator
descriptionOrchestrate an end-to-end process mining engagement by chaining phase skills with decision checkpoints and reproducibility safeguards.
short-descriptionEnd-to-end orchestrator for the process mining skill suite.

Overview

This skill coordinates the full process mining workflow from environment validation to reporting. It enforces decision checkpoints, stage gating, and reproducibility as defined in pm-99-utils-and-standards. Use it when the user wants an end-to-end run or a guided resume from a specific stage.

When to use this skill

Use when the user requests an end-to-end process mining run or wants help sequencing phases. Examples:

  • “Run the full pipeline for this log.”
  • “Resume from performance analysis.”

Inputs required

  • Input log file path and format
  • Optional config file (YAML or JSON)
  • Output directory
  • Stage resume point if resuming

Outputs produced

  • Stage artefacts in output/stage_*/
  • Versioned notebooks in output/notebooks/Rx.xx/
  • Updated output/manifest.json
  • Final report artefacts in output/stage_09_report/

Workflow

  1. Confirm objective, scope, and success criteria.
  2. Run environment detection and validation via pm-01-env.
  3. Run phases sequentially by delegating to pm-02 through pm-10.
  4. Enforce decision checkpoints and apply phase gating.
  5. On each transition, check notebook and artefact hashes and update the manifest.
  6. If strict reproducibility is enabled, stop on any mismatch and request action.

Decision checkpoints

Ask: Choose the run mode for this engagement.

Complication: The run mode affects reproducibility and how we handle changes between stages.

Options:

  1. Strict reproducibility with hash enforcement [preferred]
  2. Standard reproducibility with warnings
  3. Ad-hoc exploratory run with no hash enforcement

Impact:

  • Option 1: stop on any mismatch, enforce revision bumps and re-runs.
  • Option 2: allow continuation with logged warnings and revision bumps.
  • Option 3: fastest iteration but lowest auditability.

Ask: Choose the execution approach for the pipeline.

Complication: Using stage-by-stage execution gives tighter control, while full pipeline runs are faster but less flexible.

Options:

  1. Stage-by-stage with checkpoints [preferred]
  2. Full pipeline run via run_pipeline.py

Impact:

  • Option 1: explicit decisions between stages, higher governance.
  • Option 2: faster execution, fewer intervention points.

Commands

  • End-to-end pipeline:
    • python .codex/skills/pm-00-orchestrator/scripts/run_pipeline.py --file <path> --format <csv|xes> --output <dir>
  • Resume from stage (manual sequence):
    • python .codex/skills/pm-01-env/scripts/00_detect_env.py --output <dir>
    • python .codex/skills/pm-01-env/scripts/00_validate_env.py --output <dir> --setup-venv --venv-dir .venv --requirements .codex/skills/pm-99-utils-and-standards/requirements.txt
    • Follow phase skills in order with their command patterns.

Validations

  • Confirm manifest.json exists or will be created in the output root.
  • Confirm stage outputs match required artefact folders.
  • Exit criteria: all requested phases complete with artefacts and an updated manifest.

Issue handling

  • On errors, check references/index.json for matching error signatures and follow the referenced runbook.
  • If references/index.json or references/README.md is missing, create them using the pm-01-env issue pack format.
  • When you resolve a new issue, add a runbook under references/issue-fixes/ and register it in references/index.json.

Failure modes and remediation

  • Missing dependencies: stop and route to pm-01-env remediation.
  • Hash mismatch in strict mode: stop, bump revision, re-run impacted stages.
  • Missing stage artefacts: re-run the missing stage via its skill.

Compatibility notes

  • Delegates to pm-01 through pm-10 and follows pm-99 standards.
  • Compatible with process_mining_cli.py wrapper if present.

Version history

  • R1.00 Initial orchestrator split from the monolithic process-mining-assistant.