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, convention, or rule they clearly want followed in the future.
Install
$ agentstack add skill-caliber-ai-org-ai-setup-save-learning ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-caliber-ai-org-ai-setup-save-learning) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
- 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
- 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
- Show the refined learning to the user and ask for confirmation
- If confirmed, run:
``bash caliber learn add "" ` For personal preferences (not project-level), add --personal: `bash caliber learn add --personal "" ``
- Stage the learnings file for the next commit:
``bash 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"
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: caliber-ai-org
- Source: caliber-ai-org/ai-setup
- License: MIT
- Homepage: https://caliber-ai.dev
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.