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Write Prisma Next queries for Postgres, SQLite, or Mongo — pick a lane (Postgres/SQLite `db.orm.<Model>` + `db.sql.<table>`; Mongo `db.orm.<root>` + `db.query.from(...)` pipeline builder), filter / project / sort / paginate, eager-load with `.include(...)`, Postgres/SQLite `db.transaction(...)`, Postgres/SQLite ORM `.aggregate(...)`, Mongo aggregations via query builder, namespace-aware accessors (`db.orm.<ns>.<Model>`, `db.sql.<ns>.<table>`). Triggers: query, where, match, select, project, orderBy, take, skip, include, lookup, first, all, count, aggregate, group, create, update, delete, upsert, returning, transaction, db.close, script teardown, variant, polymorphism, drizzle-style, kysely-style. Notes: `.all()` is a Thenable (just `await` it), iterators are single-use (`RUNTIME.ITERATOR_CONSUMED`), Postgres `count` is `number` while sum/avg/min/max are `number | null`, ranges use chained `.where()` or `and(...)` (no `.between(...)`).

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Divergent ideation across model families — the same brainstorm, but each cognitive frame is handed to a different model (Claude sub-agent, OpenAI Codex, a cheap third via OpenCode) so the ideas diverge by frame and by model priors at once. Use for open design questions, architecture and API shape, naming, schema choices, fuzzy bugs with no known root cause, or on "multi adhd", "brainstorm with everyone", "wide ideas from all models". Skip for lookups, syntax, and anything phrased as "quick", "standard" or "canonical".

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This skill provides expert-level guidance for debugging and fixing bugs in VS Code extensions. Use when investigating runtime errors, fixing memory leaks, resolving WebView issues, debugging activation problems, fixing TypeScript type errors, or troubleshooting extension communication failures. Covers systematic debugging workflows, common bug patterns, root cause analysis, and prevention strategies.

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Cuts the next release of the unified Prisma CLI: bumps the root package.json version (on the Prisma 8 RC line: 8.0.0-rc.N → rc.N+1), propagates it to every lockstep workspace package, and opens a PR titled "chore(release): bump to <next-version>". When the maintainer merges the PR, the `Publish to npm` workflow ships @prisma/cli-engine and @prisma/cli under dist-tag `latest` (RC versions get a pre-release GitHub Release). Use when a maintainer asks to "cut the next RC", "cut the next release", "bump to the next version", "open a release PR", or "prepare a publish PR".

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The investigation harness for "why is this slow?" over a Pulp Perfetto trace (.pftrace). Runs the hypothesis→query→drill-down chain-of-evidence loop autonomously and returns a plain-English root cause + evidence + a concrete fix. TRIGGER on "why is my plugin slow to open", "find the slowest frames", "why is the UI stuttering", "why is my plugin using so much CPU", "which DSP node is expensive", "the load meter looks calm but CPU is pinned", or a named `pulp trace gpu-startup|gpu-health|gpu-probe` analysis. Ships Pulp-specific hints for dsp, frame, js, gpu, and cross-platform symptoms.

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Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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Unity 6 animation system guide. Use when working with Animator Controllers, animation state machines, blend trees, animation clips, Avatar system, humanoid rigs, root motion, animation events, Timeline, or Cinemachine. Based on Unity 6.3 LTS documentation.

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Use when a task needs the judgment of an Electromechanical Equipment Assembler — verifying shaft/coupling alignment directly rather than trusting an initial functional test that only confirms the device runs, applying fastener torque to spec with a calibrated tool rather than by feel, routing wire to account for the equipment's full range of motion rather than its static assembled position, or distinguishing electrical from mechanical root cause when a device malfunctions.

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Use when a task needs the judgment of a dental hygienist — full-mouth periodontal charting and staging, sequencing scaling and root planing across quadrants, deciding whether a finding needs escalation back to the dentist, or building a home-care compliance plan for a patient with active periodontal disease.

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Use when a task needs the judgment of a Compliance Officer — deciding whether a suspected violation triggers a mandatory regulatory filing or disclosure, designing or risk-rating a compliance monitoring program, running a violation investigation to root cause, briefing a board/audit committee on compliance findings, or responding to a regulatory exam finding.

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Use when a task needs the judgment of a Bookkeeping, Accounting, and Auditing Clerk — reconciling a bank or credit card account, resolving an AP three-way-match discrepancy before payment, coding an expense to the correct account and department, tracing a general ledger discrepancy to root cause, or running a month-end close checklist.

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Create high-quality, secure, and performance-optimized Containerfiles (Dockerfiles) following best practices for multi-architecture builds, OpenShift/Kubernetes compatibility, and BuildKit cache optimization. Use when the user wants to: (1) create a new Containerfile or Dockerfile for any project (Python, Rust, Go, Node.js, .NET, or any language), (2) containerize an application with multi-stage builds, (3) optimize an existing Containerfile for security, performance, or image size, (4) review or improve container image build practices, (5) set up BuildKit cache mounts for package managers, (6) create OpenShift-compatible container images with non-root users and arbitrary UID support, (7) write a .dockerignore file, or (8) apply OCI LABEL standards.

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Design and verify professional multi-sheet KiCad hierarchies generated from Circuit Weaver functional sections. Use when adding, splitting, renaming, or reviewing schematic sheets, cross-sheet interfaces, root symbols, sheet ownership, or final hierarchy ERC.

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Detect project stack and generate production-ready multi-stage Dockerfiles with compose services, .dockerignore, non-root users, health checks, layer caching, and optional dev hot-reload profiles

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Audit financial reconciliation workflows -- evaluate automated transaction matching engines, intercompany balance reconciliation, suspense and clearing account health, variance root-cause investigation, and close calendar integration.

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Analyze a workplace safety incident tracking system for incident classification accuracy, root cause analysis depth, OSHA 300 log recordkeeping compliance, trend analysis capabilities, and leading indicator identification..

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Analyze an incident response program for playbook coverage, MTTR optimization opportunities, evidence collection readiness, root cause analysis quality, and post-incident review effectiveness. Triggers: building a SOC, assessing IR maturity, optimizing detection-to-recovery timelines.

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Analyze mining safety management systems including incident investigation quality (ICAM, TapRooT, BowTie analysis), hazard identification and risk register completeness, critical control verification per ICMM framework, occupational health exposure monitoring (respirable dust, silica.

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Analyze manufacturing defect detection and quality control systems — computer vision inspection pipelines, SPC control charts, Six Sigma process capability (Cp/Cpk), defect classification taxonomies, root cause analysis tooling, and measurement system analysis.

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Analyze franchise inventory management for par level optimization, waste tracking and root cause analysis, and theoretical vs. actual usage variance.

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Capture learnings from a campaign, launch, sprint, or initiative using Five Whys (Taiichi Ohno, Toyota Production System) for root cause analysis and Start-Stop-Continue for action planning. Append reusable insights to knowledge/learnings.md so future runs of the OS get smarter. Use when the user asks for a retro, retrospective, post-mortem, "what did we learn", "campaign wrap-up", "lessons learned", or after a campaign or launch ends. Critical feedback loop that makes the OS compound over time. For the numbers review itself, see kpi-review. For planning the next campaign, see campaign-brief.

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Analyze budget vs. actual variances, identify root causes, and recommend corrective actions. Use when an accountant or finance professional says "explain this variance", "budget vs. actual analysis", "why did we miss budget", "favorable / unfavorable variance", "price volume variance", "management reporting commentary", "explain the P&L variance to the CFO", "what drove the cost overrun", "revenue shortfall analysis", or needs to write the narrative behind a financial result. Also trigger when someone shares a table of actuals vs. budget and needs to explain the gaps - even if they don't use the word "variance".

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**WORKFLOW SKILL** — Register and generalize agent errors into reusable lessons. USE FOR: when the user corrects the agent, capturing what went wrong, analyzing root cause, generalizing the error into a lesson that prevents recurrence. Use to register errors, analyze mistakes, create lessons, generalize corrections. DO NOT USE FOR: general debugging, writing tests, code review.

日本語の概要は準備中です。原文の説明を表示しています。