303건 · 관련도순
Estimate how Google and Meta budget changes could affect acquisition costs, revenue efficiency, and conversion volume. Use historical account performance to compare spending levels, identify diminishing returns, and choose budgets with a clear rationale.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Audit Reddit Ads data to assess community targeting, creative fit, and bidding efficiency. Get prioritized fixes, suggested communities to add or exclude, and feedback on whether ad copy suits the communities it reaches.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Audit the developer experience of a product, SDK, docs site, or SKILL.md by dropping multiple Claude subagents at it with only a tiny task prompt and real tools (WebFetch, Bash, Write). Agents must discover the docs themselves, install deps, ask for credentials if needed, and attempt real execution. The skill captures each agent's trace — tool calls, retries, wall time, errors — and scores on Setup Friction, Speed, Efficiency, Error Recovery, and Doc Quality, then emits an HTML report with an A–F grade and concrete fixes. Use when the user asks to audit agent experience, test a skill, audit docs for agents, check if a SDK is agent-friendly, validate a SKILL.md, measure agent DX, or benchmark how painful onboarding is for an AI agent. Triggers: 'audit agent experience', 'test this skill', 'audit docs for agents', 'is my SDK agent-friendly', 'run a DX audit', 'agent experience test', 'test my docs', 'how do agents do with my product'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Structured, enterprise-focused design system emphasizing clarity, consistency, and efficiency for data-dense web applications.
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JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient.
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Analyze and reduce avoidable Attio integration cost and load by measuring request volume, query complexity, synchronization churn, and retained data before changing architecture. Use when an Attio workload is wasteful or operationally expensive. Trigger with "Attio cost", "reduce Attio API usage", or "Attio sync efficiency".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review a skillshare change before it becomes a pull request, or review an open pull request. Use this skill whenever the user asks to: review a diff, branch, commit range or PR, check a change before opening a PR, run a pre-Codex review, or find what a change missed. It checks correctness, the repository's documented rules, the originating issue or spec, every consumer of a changed concept, and cleanup (reuse, simplification, efficiency, depth of fix), then verifies each finding against the code before reporting it. Read-only unless the user asks for fixes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reads a set of financial statements and establishes what changed and why — fluctuation analysis against prior period and against budget, profitability, liquidity, solvency and efficiency ratios, benchmarking, and the non-GAAP measures presented alongside them. Use this to interpret results, review a counterparty's or target's financials, diagnose a deteriorating trend, explain a variance to someone who will act on it, or test whether an adjusted number is honest.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data structures for memory, handle large datasets efficiently, or diagnose memory issues. Covers object sizing, generator patterns, efficient data structures, and memory profiling strategies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Optimize workload distribution across workers, processes, or nodes for efficient parallel execution. Use when asked to balance work distribution, improve parallel efficiency, reduce stragglers, implement load balancing, or optimize task scheduling. Covers static/dynamic partitioning, work stealing, and adaptive load balancing strategies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Help users identify their most high-value customers by defining a hyper-specific ICP that maximizes product-market fit and acquisition efficiency.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Gates whether a new test should exist and forces it to be efficient, protecting CI from low-value test bloat. Use before any change to what a pytest, Jest, or Playwright test asserts or sets up, down to one fixture or one assertion added to an existing block. Front-loads the value bar (every test must catch a realistic regression no existing test already catches; extend the nearest existing test before writing a new standalone one; test behavior through the public interface, not implementation details; collapse near-duplicates into parameterized cases) and the efficiency bar (deterministic, isolated, fast; pick the cheapest test level; Django TestCase over TransactionTestCase; no sleeps, no real network; no time bombs from absolute dates left to age against the real clock; no database a test never uses). Includes a "don't write it" decision tree. For fixing an existing flaky test use `/fixing-flaky-tests`; after this gate says a Playwright test is warranted, use `/playwright-test` for mechanics.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use before expensive VidSeeds MCP workflows - seeds, daily MCP call quotas, async jobId polling, when to call vidseeds_get_seed_balance, and avoiding retry loops on MCP_QUOTA_EXCEEDED or insufficient seeds.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when connecting to VidSeeds.ai, getting AUTH_REQUIRED or SUBSCRIPTION_REQUIRED from the vidseeds MCP server, or setting up VIDSEEDS_PAT / OAuth for the connector. Not for workflow recipes - use vidseeds-efficiency and domain skills after connect.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Registry fallback entry point for the VidSeeds.ai MCP connector. Use the bundled domain skills for setup, efficiency, thumbnails, projects, analytics, local video, and publishing workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when targeting Resources, Conservation & Recycling or deciding whether a resource-efficiency/circular-economy manuscript fits this venue. Encodes the journal's fit, the materials-and-waste circularity contribution bar, material-flow-analysis expectations, house style, official-submission re-check, and desk-reject heuristics.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Minimalist UX/Interaction Audit Expert that deconstructs complex interactions through cognitive load and operational efficiency lenses. Use this skill when you need to perform a UX walkthrough audit on a Figma prototype or web interface, evaluating usability based on principles like fewer clicks, less UI elements, no hidden logic, and self-explanatory design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and ranked HDR enhancers. Use when designing a donor for a point mutation, epitope/fluorophore tag, allele replacement, or knock-in, or when HDR efficiency is low. Guide design and base/prime editing are separate skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.