Dmaic process improvement
Skill walterkken/popular-process-skills/dmaic-process-improvement
Run a DMAIC process improvement project: define, measure, analyze, improve, and control. Use when Codex is asked to fix an existing process with measurable defects, delays, variation, waste, quality problems, or customer-impacting failures.From its SKILL.md
npx -y skills add walterkken/popular-process-skills --skill dmaic-process-improvementAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.0 KB, 164 tokens by cl100k_base, as published. Nobody here has run it
DMAIC Process Improvement
Workflow
- Define: customer, problem, scope, defect, goal, and process boundary.
- Measure: current performance, baseline, data plan, and measurement reliability.
- Analyze: root causes, variation, bottlenecks, and evidence.
- Improve: countermeasures, pilots, risks, and implementation plan.
- Control: control plan, owner, metric, response trigger, and handoff.
马斯克 Supervision
Delete nonessential steps before improving them. Do not build a control plan for a process that should be removed.
Output
- project charter
- baseline metrics
- root-cause analysis
- improvement plan
- pilot plan
- control plan
See references/source-notes.md.
What ships with it: 2 files
548 B alongside SKILL.md
agents/
- openai.yaml256 B
references/
- source-notes.md292 B
Gives 0 of the 12 instructions most operations skills give in 164 tokens
Counted across 483 of the 484 authors here whose files we hold, read 2026-08-07
- Collect monitoring data throughout the simulationin 14 of 483, across 6 files
- Set the random seed for reproducibilityin 14 of 483, across 6 files
- Validate simulations against analytical solutionsin 12 of 483, across 4 files
- Clarify goals, constraints, and inputsin 11 of 483, across 2 files
- Implement contract tests for integration pointsin 11 of 483, across 2 files
- Implement strangler fig infrastructure with API gatewayin 11 of 483, across 2 files
- Audit modernized components for security vulnerabilitiesin 11 of 483, across 2 files
- Avoid Python blocking calls in processesin 10 of 483, across 3 files
- Use resource context managers for automatic cleanupin 9 of 483, across 2 files
- Maintain consistent time unitsin 9 of 483, across 2 files
- Validate outcomes against success criteriain 8 of 483, across 1 file
- Analyze the legacy codebase for technical debtin 8 of 483, across 1 file
Said here and by no other author read
- define the customer, problem, scope, defect, and goal
- measure current performance and baseline data
- analyze root causes, variation, bottlenecks, and evidence
- improve with countermeasures, pilots, and an implementation plan
- control with a plan, owner, metric, and response trigger
- delete nonessential steps before improving them
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.