1,159 件 ・ 関連度順
概要と使いどころ
Act on a Caveman learn report - review the ranked token sinks, apply cost-lowering fixes with per-edit consent, and report what those fixes returned. Use when asked to lower an agent's token cost, what caveman has saved, to trim a heavy CLAUDE.md, or to offload re-pasted context into cavemem.
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。
Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.
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。
Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs integration.
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。
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
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。
The core OpenAI-compatible inference endpoints: chat completions, embeddings, images, audio (TTS/STT), moderations, rerank, and the Responses API. The primary integration surface for AI agents.
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。
SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
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。
Use when auditing locally-focused or regionally-targeted pages for geo targeting signals, particularly on sites targeting audiences through Bing or regional search engines. Note that these meta tags have no documented effect on Google's ranking.
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。
Read and manage the user's Threads account: profile, posts, feed, saved posts, activity, insights, social graph, search, trends, and a specific post by URL or ID. Can tune feed ranking and publish posts on request.
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。
Application security testing (AppSec) across a whole product with Strix — decide which asset needs which test (source code, running web app, API, CI pipeline), run it, and turn the results into a ranked remediation plan. Autonomous agents exploit and prove each issue instead of emitting static-analysis alerts, so the plan is ordered by what is actually reachable. Use when the user asks for an application security review or audit, an appsec assessment, vulnerability scanning across their stack, a security review before a launch or a customer security questionnaire, or does not yet know which kind of security test they need.
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, install, and wire registry blocks and components into HyperFrames compositions. Use BEFORE hand-building any named visual — whenever a brief, a user, or a storyboard names a look, effect, treatment, or transition such as CRT scanlines, glitch, chromatic aberration, film grain, a shimmer sweep, a chart, a code or terminal window, a map, or a confetti burst — because roughly 400 hosted items already cover many of them and the search ranks all of them with nothing installed, no project, and no account. Also use when running hyperframes add or hyperframes catalog, installing one item or every block matching a tag, wiring an installed item into index.html, or working with hyperframes.json. Covers discovery, install locations, block sub-composition wiring, component snippet merging, and authoring a new block or component to contribute upstream (idea → scaffold → validate → PR).
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。
When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'run my SEO,' 'SEO operator,' 'daily SEO agent,' 'run my outbound,' 'AI SDR,' 'reply triage,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.
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。
When the user wants to audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve it.
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。
Performs RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.
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。
Performs Scanpy single-cell RNA-seq QC, normalization, HVG selection, PCA/UMAP/t-SNE, clustering, exploratory marker ranking, pseudobulk preparation, visualization, and Seurat or SingleCellExperiment RDS conversion to h5ad. Applies to established exploratory scRNA-seq workflows with explicit count and expression provenance; complementary skills cover scvi-tools models and AnnData format details.
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。
Provides qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.
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 pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect sizes and FDR. Use for new knockout, CRISPRi, or CRISPRa screen analysis, enrichment or depletion contrasts, and MAGeCK count/test workflows; existing public dependency-score lookup belongs to DepMap.
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。
Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.
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。
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
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。
First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.
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。
Create "[Competitor] alternative" and comparison pages for developer tools. Build honest, high-converting comparison content that ranks for competitive search terms.
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。
Live launchpad feed + AI hot topics for meme tokens. (1) meme-rush: real-time lifecycle feed on launchpads (Pump.fun, Four.meme) — brand-new launches, currently-finalizing / bonding-curve tokens, and just-migrated-to-DEX tokens; filter by dev behavior, age, market cap. (2) topic-rush: AI-detected hot market narratives with the associated tokens ranked by inflow. Use for: "new pump.fun launches", "what just migrated", "currently bonding", "hot narratives", "what topic is pumping right now", "live launchpad feed", "AI hot topics".
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。
Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.
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。
Periodic architecture survey — walks the module graph and reports ranked deepening candidates (shallow modules, hypothetical seams, logic behind the wrong seam). Survey, not rescue: it finds candidates and hands them to the captain; it never refactors on its own.
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。
Debug hanging issues in SGLang distributed inference (TP/PP/DP/EP). Covers identifying hang locations via py-spy/watchdog/cuda coredump, per-rank logging to find state divergence, binary-search methodology for locating the first diverge point, and fix patterns. Use when a multi-GPU SGLang run hangs, freezes, or times out during collective operations.
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。