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Build a self-correcting coding workflow that improves across repeated sessions.
The material indicates a prompt-only skill that is open-source with meaningful community adoption, and it does not declare required secrets, remote endpoints, or local execution capabilities, so overall risk appears low. However, the missing README details, undeclared license, and unknown maintenance status warrant some supply-chain caution.
The material explicitly states that no keys or environment variables are required, and there is no factual indication that it requests API tokens, account credentials, or other sensitive authentication data, so credential exposure risk is low.
No remote endpoints or external service connections are declared, and the system marks it as prompt-only; based on the available material, there is no factual sign of user data being sent to third parties.
As a prompt-only skill, the material does not show that it starts local processes, executes scripts, or requests additional system privileges; the mentioned workflow/agent capabilities do not by themselves prove actual code execution.
The material does not state that it reads or writes local files, databases, system resources, or external account data; although it mentions 'memory' and '50+ sessions,' it provides no implementation details, so there is insufficient evidence to conclude actual data access or overbroad authorization.
The source is an open-source GitHub repository with about 2.3k stars, which is a clear risk-reducing factor; however, the missing README, undeclared license, and unknown maintenance status leave auditability details and maintenance posture insufficient, so the repository contents and commit activity should be verified before use.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "pro-workflow" yet — see the docs or source repo.
Act as my coding workflow assistant and learn from my corrections on code style, folder structure, naming, and commit habits. In this session, implement a user login module, then summarize my feedback and produce a reusable preference list for future sessions.
Delivers the login module code, a summary of corrections, and a reusable personal development preference list.
Split this request into three parallel tracks: one branch for API refactoring, one for tests, and one for documentation. Define each worktree's goal, dependencies, deliverables, and how to merge and validate everything at the end.
Provides a clear parallel workflow plan with task allocation, branch strategy, merge steps, and acceptance criteria.
Organize an agent team for a mid-sized feature: an architecture agent for solution design, a coding agent for implementation, and a testing agent for test cases and regression checks. Output the collaboration flow, handoff format, risks, and completion criteria for each role.
Generates a multi-agent collaboration plan with role responsibilities, handoff rules, risk controls, and final delivery requirements.
Production-ready Claude Code workflows powered by specialized AI agents.
Enhance coding workflows with testing, reviews, automation, and multi-LLM delegation.
Use Claude skills to automate Git, testing, and code review workflows.
Adds reusable Claude Code workflows with agents, skills, hooks, and commands.
Reusable AI coding workflows and prompts for faster, more consistent development.
Build a two-tier memory system so AI understands team context and shorthand.