Agent skill · operations · alirezarezvani
process-mapper
Use when documenting an end-to-end business process (procurement, onboarding, handoffs) and finding where work waits. Maps it with process_documenter.py, measures stages with cycle_time_analyzer.py and finds bottlenecks with bottleneck_detector.py.
Why this skill is useful
Adds 3 executable Python scripts for process mapping, bottleneck detection, and cycle-time analysis that aren't in public docs.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
process-mapper BPMN-style business process documentation, bottleneck detection, and cycle-time analysis for internal-operations leaders. Purpose Internal-operations work suffers from three recurring failure modes: 1. Implicit process — the steps exist only in tribal knowledge, so handoffs drop and onboarding takes weeks. 2. Invisible waiting — most of the elapsed time on any business process is queue / wait / approval time, not actual work; teams optimize the wrong stage. 3. Local optimization — Goldratt's Theory of Constraints is ignored; resources are added to non-constraint stages, gaining nothing. This skill produces a documented process map, identifies where work waits, and points the constraint out by name with deterministic logic — not LLM intuition. When to use Documenting a new business process (procurement intake, vendor onboarding, employee onboarding, incident handoff, expense reimbursement, customer onboarding, claims adjudication). An existing process is "too slow" but nobody can name the bottleneck. Cycle time is being measured but value-add ratio is not — so the team can't tell whether the process is healthy or waste-heavy. Cross-functional handoffs are dropping work and root cause is unclear. Workflow Five-step deterministic flow: 1. Intake. Capture the process as a JSON file with one entry per stage: name, owner, type (value-add wait rework), durationminutesp50, durationminutesp90. Use assets/processtemplate.md and its JSON skeleton. 2. Map stages. Run processdocumenter.py to produce an ASCII swim-lane diagram + a normalized JSON artifact. The swim-lane separates lanes by owner so cross-functional handoffs become visible. 3. Measure cycle time. Run cycletimeanalyzer.py to compute total P50, total P90, value-add ratio (VA%), and a Little's-Law throughput estimate. Verdict: VA% > 25% = HEALTHY, 10–25% = TYPICAL, < 10% = WASTE-HEAVY. 4. Detect bottlenecks. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/process-mapper--c96f45