Results for “slo”

Structured logging extensions for Golang using samber/slog-**** packages — multi-handler pipelines (slog-multi), log sampling (slog-sampling), attribute formatting (slog-formatter), HTTP middleware (slog-fiber, slog-gin, slog-chi, slog-echo), and backend routing (slog-datadog, slog-sentry, slog-loki, slog-syslog, slog-logstash, slog-graylog...). Apply when using or adopting slog, or when the codebase already imports any github.com/samber/slog-* package.

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Get guidance on adapting LLMs with Unsloth, including implementation, API usage, and debugging. Covers LoRA and QLoRA, methods for adjusting models with fewer trainable parameters, with a focus on training speed and memory use.

• Learning how to fine-tune models with Unsloth
• Exploring LoRA and QLoRA for efficient training
• Implementing features using Unsloth APIs

Define service reliability targets, choose measurements that reflect user experience, and calculate error budgets—the failures a target allows. Generate alert thresholds and review existing definitions for unrealistic targets, unclear metrics, and missing response policies.

• Defining reliability targets for a new service
• Calculating allowed downtime and alert thresholds
• Reviewing existing reliability definitions

Get guidance on fine-tuning language models with Unsloth, including LoRA and QLoRA methods for training with fewer resources. The skill points to documentation for understanding APIs, implementing training code, debugging problems, and exploring speed and memory optimizations.

• Exploring faster training with lower memory use
• Learning about LoRA and QLoRA fine-tuning
• Checking Unsloth features and APIs

Define reliability goals for Google Cloud services and generate Terraform files for Cloud Monitoring SLO alerts. A guided conversation gathers service scope, measurement metrics, targets, and alert conditions, then explains the PromQL queries used to evaluate them.

• Create availability SLO alerts for a service
• Define latency targets and measurement windows
• Configure SLO alerts for App Hub resources

Inspect NSLogger logs from iOS and macOS apps, filter messages by severity, tag, text, or thread, summarize counts, export text, JSON, or CSV, and receive live logs for debugging.

• Review errors in a captured app session
• Find network failures by tag and message text
• Export logs as JSON or CSV for analysis

Helps you use Unsloth for language model fine-tuning with LoRA and QLoRA, methods that adapt models with fewer trainable parameters. Supports API questions, implementation, and debugging using reference material drawn from official documentation.

• Learning how to fine-tune models with LoRA or QLoRA
• Looking up Unsloth features and APIs
• Investigating errors in training code

Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".

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Objectively evaluate a UI/web design against the pols.dev anti-slop design law: detect catalogued slop tells with cited evidence, score 8 weighted axes (color, type, components, layout, motion, execution, signature, cohesion), and emit a Slop Report with a 0–100 Slop Index and grade. Use when the user asks to "evaluate design slop", "slop report", "is this design AI slop", "audit this landing page design", "de-slop review", or wants an objective score of how generic/machine-made a design looks. To fix text (not design), use human-ai or humanizar skills instead.

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日本語の文章 (Markdown・テキスト) から「AI 臭さ」(LLM の文章に特有の定型句・単調な文のリズム・ 見出しと箇条書きだけの体裁) を検出する CLI。日本語の文章を書いた・直した後の見直し、 AI が書いたような文を自然にしたいとき、推敲・校正・AI っぽさのチェック、改稿の前後の比較 (数字や固有名詞が消えていないか・同じ直しを一律に当てていないか) の場面で発動。コードに書いた 日本語のコメント・静的な文字列・JSX/HTML の表示文言 (コードのファイルも直接検査できる) や、gws で Google ドキュメント・スプレッドシートに書き込む文章の見直し、noslop のフックが指摘を返した・ gws の書き込みを止めたときの対応にも使う。 noslop check --report brief --format toon で直す箇所を少ないトークンで受け取り、 改稿のルールに従って効果の大きい箇所だけを直す。直した後は noslop diff で 1 回だけ確かめる。

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Inspect Mac performance to find processes using excessive CPU, GPU, memory, or energy. Combine measurements with explanations of known system processes to diagnose slowness, heat, and startup load, then recommend actions without changing system settings.

• Find the cause of a slow Mac
• Investigate loud fans and overheating
• Check memory pressure and swap activity

dontbesilent 慢就是快。帮创业者找到看起来更慢但长期更快的方法，用摩擦建造资产。 触发方式：/dbs-slowisfast、/慢就是快、「有没有更慢的方法」「我是不是太快了」 Slow-is-fast diagnosis. Help entrepreneurs find seemingly slower methods that build assets through friction. Trigger: /dbs-slowisfast, "is there a slower way", "am I going too fast"

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De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is clean and sits in the right register for its genre: academic article, tweet, reddit post, email, blog, docs, marketing. Use when the user says "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize this", "make this not sound like AI", or invokes /deslopify. Also use before publishing any agent-drafted prose.

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日本語文章のAI臭（slop）を検出し、書き手のいる文章に直す。読みにくさ（一文の長さ、語順、読点、漢字連続、翻訳調）の推敲も扱う。日本語で記事・note・ブログ・報告書・メール・議事録・スライド原稿・論文・返答を「書く」「直す」「校正する」「レビューする」「人間っぽくする」「AI臭を消す」「humanize」「de-slop」ときは、明示的に頼まれなくても必ずこのスキルを使う。読者に「AIっぽい」と言われた文章の修正、公開前チェック、英語・日本語の臨床研究論文の執筆と推敲にも使う。語彙リスト（「効く」「強い」「重い」「浮き彫り」「これにより」、全角ダッシュなど約300項目）と、文長・段落構造・「〜ではなく」比率を機械的に測る scripts/check.py を含む。

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Locate CCSPlayer_WeaponServices::GetSlot in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSPlayer_WeaponServices::GetSlot" (symbol CCSPlayer_WeaponServices_Weapon_GetSlot). This is a non-virtual function - emit a byte signature (func_sig), not an offset. Locate it via cross-references, distinctive constants/strings in its body, or callers of related symbols; verify by decompilation before committing to a candidate. Trigger: CCSPlayer_WeaponServices_Weapon_GetSlot, CCSPlayer_WeaponServices::GetSlot

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Investigate slow PostgreSQL queries in Paperclip, reproduce their cost locally, and implement measured fixes. Compare performance and returned results, add regression coverage, and carry pull requests through review and automated checks.

• Investigate slow requests with a suspected database cause
• Reduce the cost of frequently repeated queries
• Compare query speed and results before and after a fix

Define service reliability measurements and targets, then turn them into Prometheus monitoring rules and alerts. Calculate error budgets, the amount of failure allowed, and outline Grafana dashboards for tracking reliability and deciding when to prioritize fixes.

• Setting availability and response time targets
• Calculating and tracking error budgets
• Creating alerts for rapid error budget consumption

Set up machine learning training and inference on NVIDIA DGX Spark. Get guidance on choosing containers and packages, resolving CUDA compatibility errors, and checking that PyTorch can access the GPU before starting a workload.

• Setting up a new DGX Spark for training or inference
• Diagnosing CUDA compatibility and import errors
• Choosing between containers and direct pip installation

Define reliability targets for production services and calculate error budgets, the allowed amount of failure. Create monitoring rules, incident response runbooks, capacity plans, and automation scripts, with failure experiments to check recovery behavior.

• Define service reliability targets and error budget policies
• Design monitoring dashboards and actionable alerts
• Prepare incident response runbooks and postmortems

Build and refine layouts that rearrange across phone, tablet, and desktop widths. The skill addresses grids, text sizes, spacing, overflow, touch controls, and navigation so content remains readable and controls remain usable as the screen changes.

• Fixing layouts that break on narrow screens
• Adjusting grids for tablet and small laptop widths
• Improving touch targets and tap interactions

Remove AI writing patterns from prose. Use this skill when writing, drafting, editing, reviewing, or revising any text to eliminate predictable AI tells, slop, and formulaic patterns. Trigger this skill whenever the user asks to "deslop", "de-AI", "make it sound human," "remove AI patterns," "remove AI tropes," "clean up AI writing," fix "slop," "deslop" text, or review prose for authenticity. Also use when the user asks you to write or draft anything and wants it to sound natural rather than AI-generated. Common use cases include scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses), blog posts, newsletters, memos, reports, and any other substantial prose.

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Find and remove AI slop — the generic, machine-default visual and copy tics of vibe-coded products — from a web project. Use when the user asks to "kill AI slop", "de-slop", "remove the AI look", "make this not look AI-generated", or clean up a landing page / UI / docs that feels templated. Detects and fixes the catalogue of tells: indigo→violet gradients, gradient-clip headlines, the default semantic palette, one-hue status boxes, atmospheric gradients, serif-italic emphasis, highlighted keywords, AI copywriting voice ("not just X — it's Y"), emoji everywhere, glowing status dots, wobbling spinners, colored-left-border callouts, pastel icon tiles, glassmorphism, over-rounding, oversized shadows, borders that die at corners, badge & pill spam, AI-drawn SVG icons, kickers over every heading, flat type hierarchies, invented stat rows, 01/02/03 section markers, cards nested in cards, the default Inter/Space Grotesk look, and more. Works on HTML/CSS, React/Vue/Svelte/Astro, Tailwind, PHP, and Markdown copy.

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Detect and fix AI-generated writing patterns (slop). Comprehensive detection with 50+ patterns across 3 severity tiers, structure weighted above lexical tells, scoring system, and editor mode that directly fixes problems. Use when scanning any content for AI tells, auditing drafts before publishing, checking if writing "sounds like AI", humanizing AI-generated text, or verifying content authenticity. Trigger on "check for slop", "does this sound like AI", "humanize this", "AI audit", "slop check", "clean up AI writing", or any request to detect/remove artificial-sounding patterns. Also use proactively before publishing any AI-assisted content.

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Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).

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