Generate skill and preference candidates from project changes and evidence.
npx skills add https://github.com/Tx1207/hello-scholar --skill evolve-tx1207
Please help me install this Agent Skill. Skill: ~evolve Source: https://github.com/Tx1207/hello-scholar/tree/main/skills/commands/evolve Command: npx skills add https://github.com/Tx1207/hello-scholar --skill evolve-tx1207
This Skill helps teams convert scattered project signals—such as changes, experiments, evidence, and closeout notes—into reusable candidates for skills and user preferences, enabling consistent, evolving workflows without automatic changes.
Invoke the evolve skill to generate candidate Skill and Preference proposals from recent changes, experiments, evidence, and closeout summaries.
High-intent search queries and answers about installing and using this skill.
The skill evolution process summarizes changes, experiments, and evidence into reusable skill and preference candidates. It applies across project lifecycles, supporting both project-level and global scopes while maintaining reference-traceability to original records.
You generate skill and preference proposals by summarizing closeout notes and project signals through a candidate-first process. This creates proposals with a preview/approve/apply lifecycle, ensuring no automatic changes occur to live configurations without explicit consent.
Generated skill candidates are project-level by default, but you can request an optional global scope to apply proposals across multiple project lifecycles. This flexibility supports both isolated and broad workflow evolution from ideation to closeout.
No, the candidate generation process does not modify live configurations automatically. It enforces a safety-first preview, approve, and apply lifecycle, ensuring changes are only applied when explicitly approved by the user.
The best way to ensure traceability when proposing workflow evolution candidates is to use a process that enforces reference-traceability to source records. This creates an auditable evolution path from project signals to skill and preference candidates.
You should avoid an automated skill evolution process if you require immediate modifications to live configurations, as this enforces a candidate-first preview and approval lifecycle. It is designed for safety-first, auditable evolution rather than instant application.
Tx1207Verify change and experiment states against contract, diff, and evidence records.
Tx1207Review, preview, approve, and apply skill or preference candidates with audit logs.
doanchienthangdevAutomate machine learning experiment design, baseline establishment, and tracking with MLflow.
koasis89Create and manage notes using OMX persistent memory features.
