Log newly discovered learnings to .claude/learning.yaml with timestamps.
npx skills add https://github.com/PackmindHub/packmind --skill learning-loop
Please help me install this Agent Skill. Skill: learning-loop Source: https://github.com/PackmindHub/packmind/tree/main/.claude/skills/learning-loop Command: npx skills add https://github.com/PackmindHub/packmind --skill learning-loop
Capture newly discovered learnings during code exploration, user interaction, or tool discovery and log them for future Claude sessions.
Use the learning-loop skill's script to log a new learning: .claude/skills/learning-loop/scripts/log-learning.sh "Observed undocumented behavior: API returns 201 on create"
High-intent search queries and answers about installing and using this skill.
Logging learnings preserves undocumented behaviors, hidden configurations, and domain knowledge discovered during code exploration. Use the learning-loop Skill to append entries to .claude/learning.yaml with timestamps, creating a persistent knowledge base that informs future Claude sessions and prevents repeating discovery work.
Write concise summaries of undocumented behavior to .claude/learning.yaml using the log-learning.sh script. Each entry captures the discovery context and timestamp, making it retrievable and actionable in subsequent sessions without manual re-exploration.
Yes. Learning-loop logs new discoveries about tools, configurations, and domain-specific knowledge directly to a YAML file. This creates a cumulative knowledge base that Claude can reference in future sessions, eliminating duplicate discovery and building institutional memory.
By writing timestamped entries to .claude/learning.yaml, learning-loop ensures insights discovered in one session remain accessible in the next. This avoids re-exploration, accelerates problem-solving, and maintains continuity of domain knowledge across your Claude interactions.
Learning-loop stores all entries in YAML format within .claude/learning.yaml. This human-readable, structured format preserves timestamps and summaries, making learnings easy to review, query, and reference across sessions without parsing overhead.
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