kaizen-loop
Run a continuous product improvement loop: evaluate a product or codebase, present prioritized recommendations for approval, then implement only approved…
Skill by s-hiraoku
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/s-hiraoku-kaizen-loop-74c259
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/kaizen-loop
What's inside
- Skill name
kaizen-loop- Description
- Run a continuous product improvement loop: evaluate a product or codebase, present prioritized recommendations for approval, then implement only approved improvements through verification and PR follow-up.
- License
- MIT
- Instructions
- 70 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Kaizen Loop Use this workflow when the task starts with product evaluation rather than a known implementation request. First evaluate the product as a user, engineer, and maintainer; then present concrete improvement candidates; after the user approves items, execute them with `implement-to-merge-ready` and repeat the evaluation loop. ## Workflow 1. Establish evaluation scope. - Identify the product, target user, primary workflows, success criteria, repository, branch, and deployment or local run path. - Read local instructions such as `AGENTS.md` and `CLAUDE.md`. - Inspect `git status` and preserve unrelated user changes. - Ask only when the evaluation target or approval boundary is unclear. 2. Set an evaluation goal. - Use goal tracking for product-wide evaluation or work expected to continue into implementation and PR follow-up. - Include evaluation, recommendation, approval gate, approved implementation, verification, PR creation, and merge-readiness follow-up. 3. Build product understanding. - Read product docs, routes, screens, tests, analytics assumptions, configuration, and recent changes. - Run the app locally when practical. - For frontend products, inspect key screens in a real browser across representative desktop and mobile viewports. - For APIs or libraries, inspect public interfaces, examples, error behavior, docs, and test coverage. 4. Evaluate relevant dimensions. - User value, UX/UI, correctness, performance, reliability, engineering quality, security, privacy, docs, and release risk. - Label inferences clearly when evidence is indirect. 5. Produce an evaluation report before editing. - Lead with highest-impact findings. - For each candidate include problem, impact, evidence, likely fix direction, estimated scope, risk, and suggested verification. - Separate must-fix defects from product improvements and speculative ideas. 6. Ask for approval to proceed. - Present a short prioritized list of recommended improvements. - Treat silence or ambiguity as no approval for implementation. - If multiple items are approved, group them into the smallest coherent PRs. 7. Implement approved improvements only. - Use `implement-to-merge-ready` for each approved change or coherent group. - Use `ui-imagegen-director` before coding when UI visual direction matters. - Do not opportunistically fix unapproved items. 8. Review, open PRs, and bring them toward merge-ready. - Self-review against the approved scope and evaluation evidence. - Open regular ready-for-review PRs by default. - Monitor CI and review feedback when practical. 9. Re-evaluate after approved changes. - Confirm the approved problems were resolved. - Identify newly visible opportunities and wait for approval before repeating. ## Recommendation Format ```text Evaluation summary: - <one short product-level assessment> Recommended improvements: 1. <title> [impact: high|medium|low, scope: small|medium|large] Problem: <what is wrong> Evidence: <what was observed> Fix direction: <what should change> Verification: <how to know it worked> Please tell me which item numbers to implement. ``` ## Guardrails - Evaluation may inspect broadly, but implementation must stay within approved scope. - Do not fabricate user research, metrics, analytics, security findings, or benchmark results. - Prefer concrete, verifiable improvements over broad redesign advice. - Do not merge PRs unless the user explicitly asks and repository policy permits it. ## Final Report Include evaluation scope and evidence, approved improvements implemented, PR URLs/branches/status, verification before and after implementation, resolved issues, remaining recommendations, and goal status when used.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 1
- Last commit (this folder)
- Jun 13, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/s-hiraoku/claude-harnesses
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
kaizen-loop skill. Agent Mart. https://agentmart.dev/skills/s-hiraoku-kaizen-loop-74c259 (last updated 2026-10-24)