Self-Improving Skills: How SkillSafe Skills Get Better With Use

Editor’s note (July 2026): The CLI workflow shown in the original version of this post referred to a retired SkillSafe client. The flow below is described with current tooling — the AI SkillSafe desktop app and npx skills add — but the observe-improve-save loop itself is unchanged.

The Problem With Static Skills

Most AI coding skills are write-once artifacts. An author publishes a skill, users install it, and that’s where the story ends. If a command fails on macOS because the skill was written on Linux, the user has to debug it themselves. If the output format isn’t quite right, they work around it. The skill never learns.

SkillSafe changes this. Skills installed from the registry can improve themselves based on how they’re actually used — fixing broken commands, adding examples from successful runs, and clarifying instructions when users get confused.

How It Works

The self-improvement loop has five steps:

1. Execute in a Forked Context

When an improvable skill runs, it executes in a sub-agent — a separate context from the main agent. This is the key design decision. The main agent acts as an observer, watching the skill’s execution and the user’s reaction from outside the skill’s context.

name: my-skill
context: fork
improvable: true
registry: "@myname/my-skill"

The context: fork field tells the host tool (Claude Code, Cursor, etc.) to spawn a sub-agent. Without it, the main agent would be inside the skill’s execution and couldn’t observe the result objectively.

2. Detect Feedback

After the skill completes, the main agent watches the user’s next few messages for signals:

• Positive — the user says “thanks” or proceeds without corrections. The skill worked as intended.
• Negative — the user says “wrong” or manually corrects the output. Something needs fixing.
• Error recovery — the sub-agent hit a tool error (like jq: command not found) and used a workaround. The skill should learn the workaround.

3. Edit the Skill

When feedback warrants a change, the main agent edits the skill’s files directly. Three types of edits:

• Add examples — append a successful input/output pair to a ## Examples section so the skill handles similar requests better next time
• Patch scripts — replace a command that failed with one that works (e.g., swap jq for python3 -c when jq isn’t installed)
• Fix instructions — rewrite a confusing section of the SKILL.md based on what the user actually meant

4. Save a New Version

The main agent saves the improved skill back to the registry — through the AI SkillSafe desktop app or the SkillSafe API — with a short changelog entry such as [patch] replaced jq with python3 fallback.

No version number needed — the patch version is auto-incremented (e.g., 1.0.2 to 1.0.3). If the content hasn’t actually changed, the save is skipped.

5. Confirm

The main agent tells the user what was improved and the new version number. The improved skill is ready for the next invocation.

Opting In to Self-Improvement

Self-improvement is disabled by default. First install the skill:

npx skills add https://api.skillsafe.ai/publisher/some-skill

Then opt in by adding improvable: true and registry: "@publisher/some-skill" to the installed skill’s SKILL.md frontmatter. With those fields present, the main agent observes how the skill performs in real use, proposes edits when feedback warrants them, and saves the result back as a new version. Without them, the skill behaves like any other static install — nothing is observed and nothing is changed.

Guiding the Improvement

Skill authors can include optional sections to steer how improvements are made.

Feedback Signals

Define what positive and negative feedback looks like for your specific skill:

## Feedback Signals

### Positive
- User accepts the generated output without edits
- Tests pass after the skill's changes

### Negative
- User reverts the skill's changes
- Tests fail after the skill's changes

Improvement Guide

Tell the main agent what kinds of edits to make in different failure modes:

## Improvement Guide

### When a command fails
Add platform detection and fallback commands.

### When output format is wrong
Add a concrete example showing the correct format.

### When instructions are misunderstood
Add DO and DO NOT lists to clarify edge cases.

Without these sections, the main agent uses its own judgment. With them, improvements are more targeted.

Rate Limiting

Self-improvement is conservative by design:

• Improvements only happen after explicit user feedback, not on every error
• Maximum one improvement save per skill per conversation — no rapid-fire version bumps
• If a skill fails again after an improvement, the agent asks the user before making another edit

Changelog Convention

Each improvement uses a bracketed prefix so the version history is easy to scan:

• [example] — added a concrete example of correct behavior
• [patch] — fixed a script or command
• [instruction] — clarified or corrected instructions
• [bugfix] — fixed a bug in the skill’s logic

The skill’s registry page (and the AI SkillSafe desktop app) shows the full version history with changelogs.

Versioning Keeps You Safe

Every improvement creates a new immutable version with a SHA-256 tree hash. Nothing is overwritten. If an improvement makes things worse, you can always roll back — open the skill in the AI SkillSafe desktop app and install any earlier version (say, 1.0.2) from its version history.

Combined with dual-side verification, this means self-improvement doesn’t compromise security. Each new version goes through the same scan-and-verify pipeline as any other published skill.

Get Started

Install any skill from the registry and start using it. If something doesn’t work right, just say so — the skill will learn.

Grab a skill with the SkillSafe desktop app, or add it from the command line:

npx skills add https://api.skillsafe.ai/owner/skill-name

Browse available skills on the Skills page, or read the documentation for the full API reference.