* docs: update README for v2.37 — changelog, extensions, stale refs - Update "What's New" section from v2.33 to v2.37 - Update extensions table: add Async Jobs and GitHub, remove LSP (Pi SDK core) - Fix extension count in architecture section (12 → 18) - Remove stale v2.17 version tags from Token Optimization section * docs: fix stale references across documentation - commands.md: update version example from v2.28 to v2.37 - troubleshooting.md: fix Node.js requirement from ≥20.6.0 to ≥22.0.0 - skills.md: fix project-local skills path from .pi/ to .gsd/ - CONTRIBUTING.md: fix scope area paths to include packages/ prefix, remove incorrect PR #1232 supply chain attack reference - vscode-extension: fix Node.js requirement, remove hardcoded RPC command count (changes over time) * docs: add troubleshooting for command not found after install Addresses #1542 — npm global bin directory not in PATH is a common issue on macOS, especially with Homebrew Node, version managers, or oh-my-zsh git aliases. - Add "command not found: gsd" section to troubleshooting.md - Add callout to getting-started.md install section
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Skills
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 |
rust-core |
Rust code — ownership, lifetimes, traits, async | Idiomatic, safe, performant Rust patterns |
axum-web-framework |
Axum web apps — routing, middleware, extractors | Complete Axum development guide |
axum-tests |
Testing Axum apps — integration tests, mock state | Test patterns for Axum applications |
tauri |
Tauri v2 desktop apps — setup, plugins, bundling | Cross-platform desktop app development |
tauri-ipc-developer |
Tauri IPC — React-Rust type-safe communication | Command scaffolding and serialization |
tauri-devtools |
Tauri debugging — CrabNebula DevTools integration | Profiling and monitoring |
github-workflows |
GitHub Actions — CI/CD, workflow debugging | Live syntax, run monitoring, failure diagnosis |
security-audit |
Security auditing — dependency scanning, OWASP | Comprehensive security assessment |
security-review |
Code security review — injection, XSS, auth flaws | Vulnerability-focused code review |
security-docker |
Docker security — Dockerfile, runtime hardening | Container security best practices |
review |
Code review — staged changes, PRs, security, performance | Diff-aware code review with quality analysis |
test |
Test generation and execution — auto-detects frameworks | Generate tests or run existing suites with failure analysis |
lint |
Linting and formatting — ESLint, Biome, Prettier | Auto-detect linter, fix issues, report remaining problems |
Skill Discovery
The skill_discovery preference controls how GSD finds skills during auto mode:
| Mode | Behavior |
|---|---|
auto |
Skills are found and applied automatically |
suggest |
Skills are identified but require confirmation (default) |
off |
No skill discovery |
Skill Preferences
Control which skills are used via preferences:
---
version: 1
always_use_skills:
- debug-like-expert
prefer_skills:
- frontend-design
avoid_skills:
- security-docker
skill_rules:
- when: task involves Clerk authentication
use: [clerk]
- when: frontend styling work
prefer: [frontend-design]
---
Resolution Order
Skills can be referenced by:
- Bare name — e.g.,
frontend-design→ scans~/.gsd/agent/skills/and project skills - Absolute path — e.g.,
/Users/you/.gsd/agent/skills/my-skill/SKILL.md - Directory path — e.g.,
~/custom-skills/my-skill→ looks forSKILL.mdinside
User skills (~/.gsd/agent/skills/) take precedence over project skills.
Custom Skills
Create your own skills by adding a directory with a SKILL.md file:
~/.gsd/agent/skills/my-skill/
SKILL.md — instructions for the LLM
references/ — optional reference files
The SKILL.md file contains instructions the LLM follows when the skill is active. Reference files can be loaded by the skill instructions as needed.
Project-Local Skills
Place skills in your project for project-specific guidance:
.gsd/agent/skills/my-project-skill/
SKILL.md
Skill Lifecycle Management
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:
---
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.