Loading...
Loading...
Found 271 Skills
Run system health checks including CPU, memory, disk, and process status. Use when: user asks about system performance, needs diagnostics, or wants to check resource usage. No API key needed.
Donella Meadows's System Leverage Points applied to any complex system — company, market, policy, or organization. Spawns a team of specialist agents — System Cartographer, Leverage Diagnostician, Counterintuitive Analyst, Paradigm Archaeologist, Dancing Advisor — who each apply a distinct lens from Meadows's framework to identify where you're wasting effort on low-leverage interventions. The lead synthesizes into a leverage audit: which level you're pushing at, which level you should be pushing at, and whether you're pushing in the right direction. Use when the user says "meadows this", "where's the leverage", "systems analysis", "why isn't this working", or describes a complex system that seems stuck despite effort. Works as a standalone analysis or paired with /munger.
Stage 3 of Clinical ASR Flywheel. Score a NeMo manifest, produce the five-section KER leaderboard (by-ipa_source diagnostic). Not for ASR auth (/riva-asr).
Use when debugging schema migration crashes, concurrency thread-confinement errors, N+1 query performance, SwiftData to Core Data bridging, or testing migrations without data loss - systematic Core Data diagnostics with safety-first migration patterns
Use when debugging connection timeouts, TLS handshake failures, data not arriving, connection drops, performance issues, or proxy/VPN interference - systematic Network.framework diagnostics with production crisis defense
Use when debugging memory leaks from blocks, blocks assigned to self or properties, network callbacks, or crashes from deallocated objects - systematic weak-strong pattern diagnosis with mandatory diagnostic rules
Active diagnostic tool for analyzing skill prompts to identify token waste, anti-patterns, trigger issues, and optimization opportunities. Use when reviewing skill prompts, debugging why skills aren't triggering, optimizing token usage, or preparing skills for publication. Provides specific, actionable suggestions with examples.
Planner-pass coverage + redundancy report for an outline+mapping, producing `outline/coverage_report.md` and `outline/outline_state.jsonl`. **Trigger**: planner, dynamic outline, outline refinement, coverage report, 大纲迭代, 覆盖率报告. **Use when**: you have `outline/outline.yml` + `outline/mapping.tsv` and want a verifiable, NO-PROSE planner pass before writing. **Skip if**: you don't want any outline/mapping diagnostics (or you have a frozen/approved structure and will not change it). **Network**: none. **Guardrail**: NO PROSE; do not invent papers; only report coverage/reuse and propose structural actions as bullets.
Validate Godot GDScript files using gdlint, gdformat, gdradon, and LSP diagnostics. Use when users want to: (1) Check code quality after making changes, (2) Validate before committing, (3) Run code metrics analysis, (4) Run export validation, (5) Get real-time LSP diagnostics. Uses command-line tools directly and MCP tools for LSP integration.
Check production health: Sentry errors, Vercel logs, health endpoints, GitHub CI/CD. Outputs structured findings. Use log-production-issues to create issues. Invoke for: production diagnostics, error audit, health status, CI failures.
This skill should be used when users encounter cspell unknown word warnings, spelling errors from cspell diagnostics, or CI/linting failures on unrecognized words. Also applies when users ask to add words to the cspell dictionary, suppress or ignore cspell warnings, choose between cspell:words and cspell:ignore directives, or bootstrap cspell config in a new project
Comprehensive prompt and context engineering for any AI system. Four modes: (1) Craft new prompts from scratch, (2) Analyze existing prompts with diagnostic scoring and optional improvement, (3) Convert prompts between model families (Claude/GPT/Gemini/Llama), (4) Evaluate prompts with test suites and rubrics. Adapts all recommendations to model class (instruction-following vs reasoning). Validates findings against current documentation. Use for system prompts, agent prompts, RAG pipelines, tool definitions, or any LLM context design. NOT for running prompts, generating content, or building agents.