process-mapper

$npx mdskill add alirezarezvani/claude-skills/process-mapper

BPMN-style business process documentation, bottleneck detection, and cycle-time analysis for internal-operations leaders.

SKILL.md

.github/skills/process-mapperView on GitHub ↗
---
name: process-mapper
description: "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."
license: MIT
metadata:
  version: 2.8.0
  author: claude-code-skills
  tags: [bizops, process, bpmn, bottleneck, cycle-time, lean, six-sigma, value-stream]
  compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
---

# 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`), `duration_minutes_p50`, `duration_minutes_p90`. Use `assets/process_template.md` and its JSON skeleton.
2. **Map stages.** Run `process_documenter.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 `cycle_time_analyzer.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.** Run `bottleneck_detector.py` with the appropriate `--profile` (saas / services / manufacturing / healthcare). Output is a ranked list with severity (CRITICAL / HIGH / MEDIUM), root-cause hypothesis, and one recommended action per finding.
5. **Recommend.** Pair the bottleneck list with the cycle-time verdict; recommend a single constraint-focused intervention per Goldratt's "subordinate everything to the constraint" rule. Don't recommend optimization of a non-constraint stage.

## Scripts

**`scripts/process_documenter.py`** — Reads a process JSON, validates it, and emits a text-based BPMN-style swim-lane diagram in Markdown (lanes by owner, stages annotated with type + duration). Also outputs a normalized JSON artifact for downstream tools. Stdlib only. `--sample` prints a 6-stage procurement-intake example.

**`scripts/bottleneck_detector.py`** — Applies three deterministic detection rules: (a) stage P50 > 2× mean of value-add stages, (b) wait-state % > 40% of total cycle, (c) rework % > 15%. Thresholds adjust by `--profile` because SaaS, services, manufacturing, and healthcare have different "normal" wait ratios. Output is a ranked list with severity, hypothesis, action.

**`scripts/cycle_time_analyzer.py`** — Computes total P50 and P90 cycle time, value-add ratio (VA%), wait %, rework %, and a Little's-Law throughput estimate (WIP / cycle time). Per Lean canon: VA% > 25% = HEALTHY, 10–25% = TYPICAL (most non-manufacturing processes land here), < 10% = WASTE-HEAVY.

## Quick example

```bash
# Renders a BPMN-style swim-lane diagram + normalized JSON for the built-in 6-stage procurement-intake example
cd business-operations/skills/process-mapper && python3 scripts/process_documenter.py --sample
```

## References

- `references/lean_six_sigma_canon.md` — TIMWOOD wastes, value-stream mapping, Theory of Constraints, Kanban WIP, Little's Law. Cites Womack & Jones, Rother & Shook, Goldratt, Ohno, Liker, Pyzdek, Anderson.
- `references/bpmn_essentials.md` — Pools, lanes, gateways, events, message flows, common notation mistakes. Cites the OMG BPMN 2.0 spec, Silver, Allweyer, Freund/Rücker, OASIS, ISO/IEC 19510:2013.
- `references/bottleneck_anti_patterns.md` — Seven specific anti-patterns drawn from Goldratt, Kim et al., Spear, DORA, Deming, and process-mining research.

## Assumptions

1. The user can provide stage-level cycle-time data (even rough P50 / P90 estimates). If they cannot, the first step is to instrument the process — not to map it.
2. "Process" here means a repeatable business workflow with discrete stages, not a one-off project.
3. The user has authority to act on bottlenecks (or can route findings to someone who does). Without that, the output is academic.
4. Stage `type` is honest: a "value-add" stage labeled as such by the user really does change the work product from the customer's perspective. Mis-labelling waiting as value-add is the most common data-quality failure.

## Anti-patterns

- **Mapping every process at once.** Pick one. Goldratt: the constraint is a single point.
- **Optimizing the non-constraint.** If stage 4 is the bottleneck, speeding up stage 2 just builds inventory in front of stage 4. Subordinate everything to the constraint.
- **Mistaking total cycle time for processing time.** They are almost never the same; VA% reveals the gap.
- **Adding people to a wait-bound process.** Wait time is not solved by more headcount; it's solved by removing the handoff or batch.
- **Treating rework as a separate problem.** Rework loops belong in the process map. Hiding them understates true cycle time.

## Distinct from

- **business-growth skills** — external sales motion, lead-funnel conversion, customer-success retention. Process-mapper is *internal* operations.
- **engineering/slo-architect** — system-reliability SLOs / error budgets / burn-rate alerts. Process-mapper is *business-process* cycle time, not system uptime.
- **c-level-advisor (COO / CEO)** — strategic prioritization of which processes to fix. Process-mapper is the tactical instrument used after that prioritization decision.
- **project-management skills** — Jira / Confluence ticket workflow tooling. Process-mapper is process *design*, not ticket *tracking*.

## Forcing-question library (Matt Pocock grill discipline)

Before invoking the tools, the orchestrator (or `/cs-grill-bizops`) walks the user through these questions **one at a time, with a recommended answer + canon citation**. Never bundled.

1. **"Do you have measured cycle times for the top-3 longest stages, or only estimates?"**
   Recommended: insist on measured data.
   Canon: Goldratt 1984 (*The Goal*) — optimizing estimated bottlenecks reliably attacks the wrong constraint.

2. **"Are you mapping the *current* process (as-is) or the *intended* process (to-be)?"**
   Recommended: map as-is first. To-be after bottleneck is identified.
   Canon: Rother & Shook 1999 (*Learning to See*) — value-stream mapping starts with the current state, always.

3. **"Where do handoffs occur between teams, and how long does each handoff wait?"**
   Recommended: log every handoff with median wait time.
   Canon: Reinertsen 2009 (*Principles of Product Development Flow*) — wait time at handoffs is the largest invisible cost.

4. **"What's your batch size at each stage?"**
   Recommended: drive batch size toward 1 wherever possible.
   Canon: Anderson 2010 (*Kanban*) — batch size correlates 1:1 with cycle time variance.

5. **"What's the rework rate per stage?"**
   Recommended: surface it explicitly; rework loops belong in the map.
   Canon: Pyzdek (*Six Sigma Handbook*) — hidden rework drives 30-50% of total cycle time in service processes.

Walk depth-first. Don't open question 4 before 1-3 are answered. After all 5 are locked, invoke `process_documenter.py` → `bottleneck_detector.py` → `cycle_time_analyzer.py` in sequence.

More from alirezarezvani/claude-skills

SkillDescription
a11y-auditUse for any accessibility audit or a11y fix (WCAG 2.2 A/AA, color contrast, compliance report, CI check) in React, Next.js, Vue, Angular, Svelte or HTML. Scans with a11y_scanner.py and checks contrast with contrast_checker.py, then fixes and re-verifies.
ab-test-setupUse before planning or launching any A/B, split or multivariate test, or when asked if a result is significant. Sizes the test with sample_size_calculator.py and writes the hypothesis, metric and stop rule first, so it is not called early on noise.
access-lifecycleUse when preparing or running a user access review, checking that leavers lost their access, finding orphaned or ownerless service accounts, answering an auditor's access-control request for SOC 2, ISO 27001, PCI DSS, HIPAA or NIST 800-53, or checking whether a finished review campaign will hold up. Diffs the HR roster against account exports, tests the campaign for coverage, self-review, unremoved revokes and rubber-stamping, and maps every exception to the controls it breaks.
ad-creativeUse for any ad copy request (RSA headlines, Meta, LinkedIn or TikTok ads, bulk variations), before writing. Checks every line against platform character limits and policy with ad_copy_validator.py. Campaign strategy and budgets: paid-ads.
adversarial-reviewerTough, critical review of a diff or PR before merge, through hostile reviewer personas that catch shared blind spots. Use whenever the user wants a change torn apart, a harsh second opinion, or a self-review felt too agreeable.
aeoUse when content should be cited by ChatGPT, Perplexity, Claude or Gemini (answer engine optimization), before rewriting it. Scores E-E-A-T and structure with aeo_audit.py, rewrites with aeo_optimizer.py and logs citations with citation_tracker.py.
agent-decision-receiptsUse when an autonomous agent takes a side-effecting action (deploy, delete, pay, grant access) that may need proving later, or for EU AI Act Art. 12 record-keeping. Builds a tamper-evident signed receipt with build_action_manifest.py and verifies it.
agent-designerDesign multi-agent systems (supervisor, swarm, pipeline) and tool schemas; find bottlenecks in run logs. Use whenever agents split work or a run is slow or costly. Not for Claude Code workflow files (workflow-builder).
agent-harnessUse when an agent should drive a goal in one of this repo's domains to a verified close, before starting the work. Compiles the goal into a task plan with goal_compiler.py and runs it through loop_controller.py, which verifies each task, caps retries and escalates.
agent-launcher-orchestratorUse when someone wants to build, launch, grade or schedule a Claude Managed Agent in their own Anthropic account ('build me an agent', 'run this nightly'). Reads the session goal, routes with goal_router.py and compiles the loop with loop_compiler.py.