Skills are specialized instruction sets that GSD loads when the task matches. They provide domain-specific guidance for the LLM — coding patterns, framework idioms, testing strategies, and tool usage.
## Bundled Skills
GSD ships with these skills, installed to `~/.gsd/agent/skills/`:
| Skill | Trigger | Description |
|-------|---------|-------------|
| `frontend-design` | Web UI work — components, pages, dashboards, styling | Production-grade frontend with high design quality |
| `swiftui` | macOS/iOS apps — SwiftUI, Xcode, App Store | Full lifecycle from creation to shipping |
| `debug-like-expert` | Complex debugging — after standard approaches fail | Methodical investigation with evidence gathering |
GSD tracks skill performance across auto-mode sessions and surfaces health data to help you maintain skill quality.
### Skill Telemetry
Every auto-mode unit records which skills were available and actively loaded. This data is stored in `metrics.json` alongside existing token and cost tracking.
### Skill Health Dashboard
View skill performance with `/gsd skill-health`:
```
/gsd skill-health # overview table: name, uses, success%, tokens, trend, last used
/gsd skill-health rust-core # detailed view for one skill
/gsd skill-health --stale 30 # skills unused for 30+ days
/gsd skill-health --declining # skills with falling success rates
```
The dashboard flags skills that may need attention:
- **Success rate below 70%** over the last 10 uses
- **Token usage rising 20%+** compared to the previous window
- **Stale skills** unused beyond the configured threshold
### Staleness Detection
Skills unused for a configurable number of days are flagged as stale and can be automatically deprioritized:
```yaml
---
skill_staleness_days: 60 # default: 60, set to 0 to disable
---
```
Stale skills are excluded from automatic matching but remain invokable explicitly via `read`.
### Heal-Skill (Post-Unit Analysis)
When configured as a post-unit hook, GSD can analyze whether the agent deviated from a skill's instructions during execution. If significant drift is detected (outdated API patterns, incorrect guidance), it writes proposed fixes to `.gsd/skill-review-queue.md` for human review.
Key design principle: skills are **never auto-modified**. Research shows curated skills outperform auto-generated ones significantly, so the human review step is critical.