Plan Do Check Act
CheckedIteratively refines actions through planning, execution, verification, and adjustment cycles for improved task completion.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add glennguilloux/context-engineering-kit --skill plan-do-check-act -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
glennguilloux
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned glennguilloux/context-engineering-kit at 245278227a.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 245278227a
- Scanned
- 2 files, 501 lines
- Copies in repo
- 2 — all of them scanned
About this skill
What it does
This skill implements the Plan-Do-Check-Act (PDCA) cycle, a structured problem-solving methodology. It guides an AI agent through iterative improvement loops by first planning an action, then executing it, observing the results, and finally adjusting the plan based on those observations. This allows for continuous refinement of processes or strategies.
When to use it
- Process Optimization: To systematically improve a workflow, like customer onboarding or content creation.
- Experimentation & A/B Testing: To structure experiments by planning a test, executing it, analyzing the results, and refining the approach.
- Problem Solving: For tackling complex issues where a single solution isn't immediately apparent and iterative refinement is needed.
- Strategic Planning: To develop and adjust strategies over time based on real-world feedback and performance data.
Key capabilities
- Iterative planning, execution, observation, and adjustment cycles.
- Structured problem solving framework.
- Continuous improvement methodology.
Example prompts
- "Let's use the PDCA cycle to improve our email marketing open rates."
- "Apply the Plan-Do-Check-Act method to optimize this sales script."
- "Guide me through a PDCA loop for debugging this code snippet."
Tips & gotchas
The skill requires clear goals and measurable metrics to effectively evaluate each "check" phase. Without defined success criteria, it's difficult to determine if adjustments are needed or whether the plan is working as intended.