「errors」の検索結果

Diagnoses and resolves Google Cloud Filestore client mount failures, permission errors (EACCES), and network timeouts (ETIMEDOUT). Use when an NFS mount hangs or fails from a Compute Engine VM, GKE pod, Cloud Run service, or Vertex AI workload, when `mount.nfs` reports "Connection timed out" or "access denied by server", when checking whether VPC ingress firewall rules or `nfsExportOptions` allow a client IP, or when a previously working Filestore share suddenly stops mounting after an administrative change. Don't use for Cloud Storage (GCS) buckets, Persistent Disk, or Cloud NetApp Volumes, and don't use for Filestore capacity scaling or backup and export-policy auditing.

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Manages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use the gke-storage-troubleshooting skill instead. Don't use for database administration or replication strategies outside volume provisioning context.

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Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.

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Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.

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Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

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Review Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.

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Troubleshoots Google Cloud Gemini Enterprise Agent Platform issues (Agent Gateway, Registry, Identity, Policies, Model Armor, Identity-Aware Proxy (IAP)). Use when agent requests fail with 403 (especially unauthorized egress), Agent Runtime queries return 500, or gateway/IAP logs show permission errors. Don't use for general Google Cloud Identity and Access Management (IAM) debugging or networking issues unrelated to the Agent Platform stack.

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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

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Fix a bug using Scout → Diagnose → Fix → Review → Finalize. Use when the user pastes an error message, stack trace, or test failure, or says "fix this bug", "something's broken", "tests are failing", "I'm getting an error". Also accepts `--from-quality <report>` or `--from-test <report>` to fix exactly the findings/failures a report lists. Modes (pick one): --fast (trivial errors — lint, type, build — skip scout and review), --hard (mandatory review, no auto-approve).

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Fix a bug using Scout → Diagnose → Fix → Review → Finalize. Use when the user pastes an error message, stack trace, or test failure, or says "fix this bug", "something's broken", "tests are failing", "I'm getting an error". Also accepts `--from-quality <report>` or `--from-test <report>` to fix exactly the findings/failures a report lists. Modes (pick one): --fast (trivial errors — lint, type, build — skip scout and review), --hard (mandatory review, no auto-approve).

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Design Invocable Apex actions that return deterministic, agent-friendly errors instead of surfacing raw exceptions to the LLM. NOT for authoring the action itself — @InvocableMethod schema, labels, security context — use agentforce/custom-agent-actions-apex. NOT for the Apex tests that force each error branch — use agentforce/agent-action-unit-tests.

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Distinguish the six real reasons a Salesforce query can 'fail', and the protocol for diagnosing before declaring: object doesn't exist, not queryable in edition, permission-denied, field-level errors, namespace prefix missing, API version mismatch. NOT for one object's own query restrictions, required filters or row caps - use apex/soql-object-limits-and-restrictions. NOT for a query that works but is slow - use data/soql-query-optimization.

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Diagnose and mitigate Salesforce data skew — ownership skew (single user owns >10,000 records) and parent-child skew (>10,000 children under one parent) — that cause sharing recalculation slowness, group membership lock errors, and record-level locking failures. Covers lookup skew and UNABLE_TO_LOCK_ROW, Defer Sharing Calculation (deferGroupMembership / deferSharingRules in Sharing.settings), ownership bucket groups, Controlled by Parent OWD, Bulk API serial vs parallel concurrency for skewed loads, and the documented load ordering for roles, groups and sharing rules. NOT for planning and batching a recalculation job — use data/sharing-recalculation-performance. NOT for designing the sharing model itself — use admin/sharing-and-visibility.

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Diagnoses and fixes CI failures and re-runs GitHub Actions jobs. Use when CI is failing, tests break in CI, lint errors block merging, a build fails, or jobs need re-running.

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Firmware development for the LGT8F328P-LQFP32 MiniEVB — the Nano-style 30-pin "purple nano" board whose silkscreen reads LGTBF32BP (also sold as BTE21-15A, WAVGAT, LGT8F328P Nano V3.0), built on the Logic Green / Prodesign LGT8F328P, an AVR-instruction-compatible LGT8XM part with a 32 MHz internal RC and no crystal, a 12-bit ADC, an 8-bit DAC on D4, four timers, 80 mA drive pins, SWD instead of ICSP, and EEPROM emulated in program flash. Use when working on this board, any LGT8F328P/LGT8F328D/LGT8FX8P board, or a sketch being ported from an Arduino Nano or ATmega328P: PlatformIO setup with the lgt8fx core, platformio.ini, f_cpu/f_osc/clock_source and why timings come out 2x wrong, pin mapping and the LQFP32 bonded pads, analogRead resolution and reference errors, analogWrite hitting the DAC, EEPROM vanishing after upload, 80 mA outputs, upload failures over CH340/CH9340/HT42B534, SWD recovery, or debugging why something on the board does not work.

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Firmware development for the Guition JC4880P443C_I_W (JC4880P443, JC4880P443C, often mistyped JC4880P433) — a 4.3" 480x800 IPS touch display board on the JC-ESP32P4-M3 module (ESP32-P4NRW32, 32 MB PSRAM, 16 MB flash, ESP32-C6 Wi-Fi co-processor on SDIO) — its ST7701S MIPI-DSI panel, GT911 touch, backlight, ES8311 codec with speaker amp and mic, microSD, RS485, battery charger, 2x13 header, and the PlatformIO + ESP-IDF (pioarduino) setup around them. Use when working on this board: project setup, platformio.ini and sdkconfig.defaults, the P4 silicon revision and an "Illegal instruction" boot, a black DSI screen, ST7701 init sequence and timings, GT911 init errors, backlight PWM, choosing a free GPIO, Wi-Fi through esp_hosted and esp_wifi_remote, SD card, audio, flashing over USB-C, or debugging why something on the board does not work.

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。

Master debugger for Python code with expertise in common errors, performance issues, and debugging tools

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Use the Skmtc CLI to scaffold projects, install or clone generators from JSR, configure schema sources and enrichments, and produce code artifacts from an OpenAPI v3 or GraphQL SDL schema. Teaches the workspace mental model (`<root>/.skmtc/<project>/`, client.json, bundle, manifest) and the agent contract (strict text / strict JSON modes, exit codes, recipe errors, `agent-context` + `doctor`); the command surface itself is discovered from the binary — `skmtc --help`, `skmtc <cmd> -h` — rather than carried in this skill. Use this skill when the user asks to "run skmtc", "generate code from an OpenAPI schema", "install a skmtc generator", "scaffold a skmtc project", "watch a skmtc project", "configure enrichments", "publish a stack", "deploy to skmtc-hub" (the command is `publish`; there is no `deploy`), "skmtc in CI", or invokes any CLI subcommand. For *authoring* a generator package (Projections, Snippets, transform functions), defer to `skmtc-generator`. When something is broken (no output, wrong output, error messages, a failed bundle build), verify before proposing a fix: read the manifest and the parse issues, and reproduce the failure first.

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Look up Microsoft API references, find working code samples, and verify SDK code is correct. Use when working with Azure SDKs, .NET libraries, or Microsoft APIs—to find the right method, check parameters, get working examples, or troubleshoot errors. Catches hallucinated methods, wrong signatures, and deprecated patterns by querying official docs.

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Prevents overengineering when designing, implementing, refactoring, or reviewing software. Use when the user asks to implement a feature, add an abstraction, refactor code, review a design, simplify existing code, or make an architectural decision. Also triggers when the user asks if code is too complex, whether to abstract something, or how to structure something. Do not use for deployment, infrastructure configuration, or debugging runtime errors unrelated to code structure.

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MUST USE for ANY work on .py .pyi .rs .ts .tsx .mts .cts .go files. One philosophy: strict types, modern stacks (Pydantic v2 / serde+thiserror / Zod / gin+sqlc+pgx+slog), modern toolchains (uv+basedpyright+ruff / cargo+clippy+miri / Bun+Biome+tsc / gofumpt+golangci-lint v2+nilaway+go-race), parse-don't-validate, exhaustive match, typed errors, no any/unwrap/panic, 250 LOC ceiling, TDD. Routes to references/{python,rust,typescript,rust-ub,go}/. Triggers: write/edit Python/Rust/TypeScript/Go code, new project, gin server, bubbletea TUI, CJK IME, connect-go RPC, sqlc pgx, branded ids, exhaustive match, unsafe Rust, miri, oversized file, refactor, TDD, e2e test, arena, allocator, bumpalo, const fn, const generics, comptime, zero-alloc, bitfield, repr, scopeguard, errdefer, Zig-like, zerocopy, packed struct.

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When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup. For AI search optimization, see ai-seo.

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shadcn-vue for Vue/Nuxt with Reka UI components and Tailwind. Use for accessible UI, Auto Form, data tables, charts, dark mode, or encountering component imports, Reka UI errors.

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Patterns for robust error handling across TypeScript, Python, and Go. Covers typed errors, error boundaries, retries, circuit breakers, and user-facing error messages.

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。