「log」の検索結果

Transform any research material into SEO+GEO-optimized blog posts for Knox Hub. Takes any input (URL, article text, notes, video transcript, competitor post, topic idea) and rewrites it as natural human-sounding content, then fills in all fields for the Knox Hub post editor: title, excerpt, SEO settings, FAQ schema, table of contents, sticky sidebar, and full article content in Markdown. Output is ready to copy-paste into your blog/CMS post editor. Use when asked to "write a post", "rewrite this", "сделай пост", "напиши статью", "перепиши это для блога", "create blog post", or any content creation task.

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。

Search Google Cloud (GKE) server logs of the PROD environment. Use when troubleshooting a production incident that needs the server side of the story — stream stalls, RPC peer changes / reconnects, watchdog closures, mesh/node churn, pod restarts — or when the user says "check PROD logs", "check production server logs", or gives a production timeline from a client console log.

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Search Google Cloud (GKE) server logs of the DEV environment (dev.voxt.ai). Use when troubleshooting anything that needs the server side of the story — stream stalls, RPC peer changes / reconnects, watchdog closures, mesh/node churn, pod restarts — or when the user says "check GCP logs", "check server logs on dev", or gives a dev.voxt.ai timeline from a client console log.

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Scans AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection API. Identifies unusual error patterns, latency spikes, and log volume anomalies across log groups.

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。

Analyzes AWS CloudWatch Logs using the CloudWatch Logs API and Logs Insights query syntax. Identifies error patterns, calculates error rates, and generates metric filters from log data.

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Instruments Anthropic API calls to log token usage, latency, and cost per request using the Anthropic TypeScript SDK. Wraps the anthropic.messages.create method to capture usage.input_tokens, usage.output_tokens, and timing from the API response. Writes structured logs to CloudWatch Logs via the AWS SDK v3 CloudWatchLogsClient.

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Keep a Changelog format guidelines and entry writing best practices. Use when writing changelog entries, updating CHANGELOG.md, or following Keep a Changelog specification.

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Applies best practices for logging (zerolog/zap), project structure (cmd/internal/pkg), middleware registration order, and environment configuration in Go Fiber v2/v3 applications.

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Structured debug log analysis for Claude Code sessions — auto-discovers most recent log, runs reducer, extracts error patterns, correlates with full log, produces observability report. Fills 5 identified gaps: hook error body capture, agent identity, file path tracking, stall correlation, success visibility.

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Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

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Use whenever the user mentions Betaflight, FPV drone firmware, flight-controller config, PID/filter/rates tuning, or blackbox log analysis — or shares a Betaflight artifact: a CLI diff/dump (lines like 'diff all', 'set ', 'feature ', 'resource ') or a .bbl/.bfl blackbox log, even when not named as such — or runs a chirp tuning session via Sweep Studio / FPVLogForge (a chirp log ID to analyse, 'analyse log', 'résultat chirp', iterative Bode/step/Ms tuning). Covers flight symptoms (oscillations, wobble, propwash, jello, hot motors, drift), motor/ESC and RX/ELRS/CRSF setup, builds from 3" to 10" (cinewhoop, tinywhoop, longrange, X-class), version migrations (4.4 → 4.5 → 4.6), and configuring a new drone from scratch (setup wizard). Trigger even when phrased casually ("mon drone wobble", "PID help", "config FC") — under-triggering is worse than over-triggering.

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"Apply the Technology Acceptance Model (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) to predict technology adoption. Use this skill when the user needs to evaluate user acceptance of a new system, diagnose adoption barriers, design interventions to improve technology uptake, or when they ask 'why aren't users adopting this', 'what drives technology acceptance', or 'how do we increase adoption rates'.".

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Use when a task needs the judgment of an Anthropologist/Archeologist working in cultural resource management (CRM) — scoping a Section 106 Phase I identification survey, designing a shovel-test-pit sampling strategy, evaluating National Register of Historic Places eligibility under Criteria A-D, interpreting excavation stratigraphy via a Harris matrix and radiocarbon results, or running the ethnographic field-methods branch (participant observation, informant interviewing) for a cultural-anthropology component of a project.

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Use when designing, validating, deploying, changing, or troubleshooting Volcengine TLS LogCollector collection on Linux hosts or Kubernetes, including Project, Topic, host group, collector rule, index, binding, heartbeat, parsing, and end-to-end log ingestion checks through volclog. 当用户需要设计、部署、 变更或排查火山引擎日志服务 LogCollector 采集（主机、Kubernetes DaemonSet/Controller、机器组、采集配置、 心跳、解析、日志未入库）时调用。

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。

Write human-sounding, SEO-optimized blog posts for skills-hub.ai. Use when: 'write a blog post', 'draft a post', 'update blog', 'blog about', 'write about'. Supports styles: tutorial, deep_dive, opinion, listicle, case_study.

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Create a bounded, extractive, structurally idempotent rollup of recent Obsidian wiki log entries, with dry-run preview by default and one optional transaction apply. Use for manual log compression without modifying child pages. Triggers: fold the log, run a fold, run wiki-fold, log rollup, roll up log entries, commit the fold.

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Learn author writing style from 5 to 10 existing blog posts and generate a voice profile for /blog style learn, VOICE.md, blog-persona, and blog-write when users ask to infer tone, analyze author voice, learn style, or build a writing baseline.

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。

Deep cultural adaptation of translated blog posts. Run after blog-translate completes. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "lokalisieren", "localiser", "adaptar".

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Establish durable brand and voice context for cross-skill consumption. Generates BRAND.md (audience, positioning, do/don't editorial rules, taboo phrases, competitor differentiation) and VOICE.md (existing persona JSON re-expressed as readable prose), both written to the project root. When present, the blog orchestrator auto-loads these files for write, rewrite, brief, outline, calendar, and strategy workflows. Pairs with blog-persona, which manages the structured persona JSON. Use when user says "blog brand", "create brand context", "brand voice doc", "BRAND.md", "VOICE.md", "establish editorial brand", "brand guidelines for blog".

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SERP-informed outline generation with H2/H3 heading hierarchy, competitive content gap analysis, section-by-section word count targets, chart and image placement markers, optional FAQ question planning, and internal linking zones. Skeleton only: structure, H2/H3 hierarchy, word counts, optional FAQ slots. Use blog-brief instead if you need full competitive analysis, statistics research, and image suggestions. Lighter than a full content brief, generates article skeleton and structure only, ready for /blog write to consume. Use when user says "outline", "blog outline", "content outline", "structure blog", "plan sections", "article skeleton", "heading structure", "SERP analysis", "competitive outline", "plan article".

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。

Generates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

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Catalog-then-fix slop removal on an existing prose draft. Parallel catalog subagents inventory four slop categories (theatrics, overwrought register, corrective antithesis, dramatic short sentences), fix subagents repair only what a catalog names, mechanical lint + frequency analysis bracket the run, and a regression gate (lint delta, word count, cold meaning check) decides whether the result ships. Use when the user says "deslop this draft", "catalog deslop", "strip the AI patterns from this file", or as the final stage of the article-factory and mosaic-writing skills. For writing or redrafting prose from scratch, use the prose skill instead - this skill only repairs an existing draft in place.

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。

Configures structured logging (Serilog/.NET, structlog/Python)

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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.

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。