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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, convention, or rule they clearly want followed in the future.

70

Quality

88%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, actionable instruction skill with concrete commands, clear sequencing, and good sectioning. The only real slack is mild redundancy across examples and unstated edge-case handling in the workflow.

Suggestions

Collapse the three Examples into one fully-worked example plus a short list of alternate triggers; the refine-confirm-save flow is already shown once.

Add an explicit 'If the user declines, do not save anything' branch to step 3/4 so the decline path is unambiguous.

Note whether to verify the learning was added (e.g., re-running caliber learn list) so the workflow has a lightweight verification step.

DimensionReasoningScore

Conciseness

The body is lean with no concept-padding Claude already knows, but the three Examples repeat the same refine-confirm-save pattern; trimming to one detailed example would tighten it further.

4 / 5

Actionability

Provides copy-paste-ready commands (caliber learn add, --personal, git add CALIBER_LEARNINGS.md) plus concrete type-prefix tags and worked examples covering the common cases.

5 / 5

Workflow Clarity

A clear 5-step sequence with an explicit confirmation checkpoint is present, but the decline path and any post-add verification are not spelled out, leaving a minor validation gap.

4 / 5

Progressive Disclosure

With no bundle files and roughly 50 lines, the skill is well organized into Instructions, Examples, and 'When NOT to trigger' sections, satisfying the simple-skill exception for one-level structure.

5 / 5

Total

18

/

20

Passed

Description

86%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A clear, well-structured description that explicitly pairs a concrete 'what' with natural-language 'when' triggers. It is highly actionable and mostly distinct, with only minor action-breadth and overlap caveats.

DimensionReasoningScore

Specificity

Names the domain ('Saves user instructions as persistent learnings for future sessions') and two concrete actions (save, proactively suggest), which matches the 1-2-actions anchor rather than the several-actions anchor.

3 / 5

Completeness

Explicitly answers both what ('Saves user instructions as persistent learnings for future sessions') and when ('Use when the user says...') with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Lists exact natural phrases users say ('remember this', 'always do X', 'from now on', 'never do Y') plus 'preference, convention, or rule', giving comprehensive coverage of natural trigger terms.

5 / 5

Distinctiveness Conflict Risk

The persistent-learning niche is mostly distinct with specific triggers, but phrases like 'remember this' carry minor overlap risk with a generic memory or note-taking skill.

4 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
caliber-ai-org/ai-setup
Reviewed

Table of Contents

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