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Found 7,082 Skills
Launch the interactive web dashboard to visualize a codebase's knowledge graph
Use when you need to ask questions about a codebase or understand code using a knowledge graph
Break down a one-sentence idea into a task plan that an AI agent can execute independently. Use this when the user says: "Help me write a goal for the agent", "Help me break down this goal in detail", "Write a task brief for the agent", "Write a goal prompt", "Let the agent run this project on its own", "Split the work among multiple agents for parallel execution". First conduct actual tests in the codebase, conduct online research if necessary, then ask a maximum of 5 questions in one go, and produce a task plan of ≤4000 characters that can be directly pasted into /goal to run, including actual test data, whitelist boundaries, anti-cheating acceptance criteria, and resumable progress. Automatically distinguish between execution-type and exploration-type (research/selection/solution-finding) tasks.
Assists in provisioning instances and databases, designing performant schemas, and querying data in Spanner. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
Master skill that runs the full PlanetScale safe best-practices assessment — inventory, engine review, Insights, Traffic Control, webhooks, schema recommendations, codebase instrumentation, and agent operating model — then produces a unified recommendations report. Never applies changes without explicit approval. Use when the user asks to run the full assessment, all skills, or PlanetScale best-practices review.
Check whether your rules file (CLAUDE.md or AGENTS.md) still matches the codebase after recent changes — run before a merge, or fold into your code-review pass. Reports stale/now-false rules, drifted architecture-map entries, and any new invariant worth adding, each with the minimal edit. Advisory and anti-bloat: it keeps the rules file true, never longer than it needs to be.
Use when parallel agents share a codebase: adding new behavior, editing or resolving conflicts in shared files (dispatchers, registries, lockfiles), finding and splitting churn hotspots, or writing AGENTS.md, CLAUDE.md, or README.
Plan zero-downtime schema changes across code, data backfills, and cutovers. Use for expand-contract database changes. NOT for fresh schema design or DBA ops.
Adversarial senior-engineer review for agent-generated plans, designs, and architectures. Treats the current output as junior work, constructs a senior reviewer whose domain expertise comes from live codebase research plus web research of current best practices, diagnoses altitude failures (too vague or too granular), then rewrites the plan into a scoped, state-of-the-art version. Use when the user says "junior to senior", "senior review", "review this like a staff engineer", when a plan feels hand-wavy or lost in details, or before committing to any agent-written plan.
Build a roguelike: turn-based grid movement, procedural dungeons, permadeath, field-of-view, and loot tables. Use for a roguelike/roguelite or turn-based grid dungeon crawler with procedural levels.
Run a health check on existing memory (the wiki substrate) and goal artifacts. Use this when the user wants to lint the wiki, health-check the knowledge base, find orphan pages, spot broken or missing cross-links, clean up stale claims and unresolved wikilinks with safe local fixes, consolidate a legacy root `overview.md` into `index.md`, or health-check goal artifacts. Not for adding new material; use /loam::adding-to-memory or /loam::learning-from-session for that.