AI & LLM Engineering · By LearnPrompt

Consolidate recent logs, sessions, and existing memory files into durable topic memories, normalize dates, prune stale entries, and keep MEMORY.md short enough for prompt use.

LearnPrompt/cc-harness-skills

Build a lightweight proactive mode with scheduled checks, sleep intervals, concise user briefs, and expiry safeguards so an agent can work in the background without becoming an uncontrolled daemon.

LearnPrompt/cc-harness-skills

Extract durable memories from recent conversation turns into user, feedback, project, and reference categories while avoiding stale code-state facts.

LearnPrompt/cc-harness-skills

Compress a long agent conversation into a nine-part continuation summary that preserves request, files, errors, user messages, current work, and the next aligned step.

LearnPrompt/cc-harness-skills

Coordinate multiple agents by splitting work into research, synthesis, implementation, and verification, assigning ownership, and keeping the coordinator focused on integration rather than raw…

LearnPrompt/cc-harness-skills

Run a read-only verification pass after implementation to check whether completion claims are real, validation actually ran, and obvious edge cases or regressions were missed.

LearnPrompt/cc-harness-skills

Apply Karpathy-style minimalism and anti-dependency principles to code and system design.

LearnPrompt/andrej-karpathy-skills

Build practice environments and evaluation gyms where agents can try, fail, and learn from feedback.

LearnPrompt/andrej-karpathy-skills

Write explicit strategy into system prompts as a learning mechanism — making LLM behavior more predictable and teachable through prompt-as-textbook approach.

LearnPrompt/andrej-karpathy-skills