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Diagnose planning directory health and optionally repair issues
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Basierend auf der SOC-Berufsklassifikation
| name | gsd-health |
| description | Diagnose planning directory health and optionally repair issues |
--context runs an orthogonal check: the running session's context utilization. The workflow asks for the model's tokensUsed + contextWindow, calls gsd-tools query validate.context, and renders one of three states:
| Utilization | State | Action |
|---|---|---|
| < 60% | healthy | no action — context is comfortable |
| 60% – 70% | warning | recommend /gsd-thread to start fresh |
| ≥ 70% | critical | reasoning quality may degrade past the fracture point |
<execution_context> @/home/aming/.config/opencode/gsd-core/workflows/health.md </execution_context>
Execute end-to-end. Parse `--repair` and `--context` flags from arguments and pass to workflow.Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Use this skill when the user wants direct operations on an Obsidian vault: read or edit notes and daily notes, search vault contents, count or list notes, manage tasks, move or rename files, find orphaned notes or broken links, inspect frontmatter/properties, manage sync or file history, query Bases, manage bookmarks, script the CLI, or run JavaScript against the Obsidian API. Trigger when the request implies "go into my vault and do X"; the user wants action on vault data, not an explanation. Skip pure conceptual or GUI-help questions such as settings navigation, theme/plugin installation, sync troubleshooting, Dataview syntax, keyboard shortcuts, or parsing vault files with unrelated external scripts.
Captures corrections, failures, missing capabilities, and reusable discoveries in agent so future work improves. Use this skill whenever a command, tool, or API fails unexpectedly; the user corrects the agent; the agent realizes its knowledge is outdated or wrong; a missing capability is exposed; or a better recurring approach is discovered. Record the result under .agents/learnings/, review related entries before major tasks, and promote durable workflow rules into AGENTS.md when they should change future behavior.
Generate tests for a completed phase based on UAT criteria and implementation
Generate an AI-SPEC.md design contract for phases that involve building AI systems.
Autonomous audit-to-fix pipeline — find issues, classify, fix, test, commit
