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/save-learning

Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference,

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ai-setup
1.3k8 skills
Install
$ npx -y skills add caliber-ai-org/ai-setup --skill save-learning --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/save-learning

Context preview

The summary Claude sees to decide when to auto-load this skill.

Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference,

SKILL.md

save-learning.SKILL.md
name: save-learning
description: Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference, convention, or rule they clearly want followed in the future.

Save Learning

Save a user's instruction or preference as a persistent learning that will be applied in all future sessions on this project.

Instructions

1. Detect when the user gives an instruction to remember, such as:

  • "remember this", "save this", "always do X", "never do Y"
  • "from now on", "going forward", "in this project we..."
  • Any stated convention, preference, or rule

2. Refine the instruction into a clean, actionable learning bullet with an appropriate type prefix:

  • `**[convention]**` — coding style, workflow, git conventions
  • `**[pattern]**` — reusable code patterns
  • `**[anti-pattern]**` — things to avoid
  • `**[preference]**` — personal/team preferences
  • `**[context]**` — project-specific context

3. Show the refined learning to the user and ask for confirmation 4. If confirmed, run:

   caliber learn add "<refined learning>"

For personal preferences (not project-level), add `--personal`:

   caliber learn add --personal "<refined learning>"

5. Stage the learnings file for the next commit:

   git add CALIBER_LEARNINGS.md

Examples

User: "when developing features, push to next branch not master, remember it" -> Refine: `**[convention]** Push feature commits to the \`next\` branch, not \`master\`` -> "I'll save this as a project learning: **[convention]** Push feature commits to the \`next\` branch, not \`master\` Save for future sessions?" -> If yes: run `caliber learn add "**[convention]** Push feature commits to the next branch, not master"` -> Run `git add CALIBER_LEARNINGS.md`

User: "always use bun instead of npm" -> Refine: `**[preference]** Use \`bun\` instead of \`npm\` for package management` -> Confirm and save

User: "never use any in TypeScript, use unknown instead" -> Refine: `**[convention]** Use \`unknown\` instead of \`any\` in TypeScript` -> Confirm and save

When NOT to trigger

  • The user is giving a one-time instruction for the current task only
  • The instruction is too vague to be actionable
  • The user explicitly says "just for now" or "only this time"
Read more
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Continuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for Claude Code, Cursor, and Codex.

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Repo: caliber-ai-org/ai-setup

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