데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (8.9천건)
应用从 AI for Everyone 蒸馏出的「ML项目流程 — 收集→训练→部署→迭代」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to ml-workflow.
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应用从 AI for Everyone 蒸馏出的「不为数据而收购 — 数据投资的价值验证原则」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to dont-acquire-for-data.
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应用从 AI for Everyone 蒸馏出的「数据飞轮 — AI产品自我强化的增长引擎」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to data-flywheel.
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应用从 AI for Everyone 蒸馏出的「统计化验收标准 — 不追求100%准确」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to statistical-acceptance.
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Writes and reviews Swift App Intents code that exposes app actions and data to Siri, Shortcuts, Spotlight, widgets, Control Center, and Apple Intelligence. Use when adding AppIntent, AppEntity, OpenIntent, AppShortcutsProvider, EntityQuery, Focus Filters, AssistantEntity/AssistantIntent schemas, on-screen awareness, LongRunningIntent/CancellableIntent, EntityCollection, SyncableEntity, AppIntentsTesting, or when wiring SwiftData/networked data into intents.
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5つの専門エージェントがディスカッション型で競合のビジネスモデル・機能・価格・弱点を網羅的に分析し、 差別化ポイントと参入戦略を導出するスキル。 エージェント同士が対話・批判し合いながら調査し、ca-data-critic がライブモデレーターとして矛盾を検出・収束を判定する。 Phase 1 で競合プロファイル・レビュー・機能をディスカッション型で調査し、 Phase 2 でポジショニング戦略家が統合。 Gate 1（中間合意形成）→ Gate 2（最終検証 + Independent Validation + Devil's Advocate）の2段階ゲートで品質を担保。 Use when: 競合を分析したい、差別化ポイントを見つけたい、 市場のプレイヤーを調べたい、ポジショニングを考えたい。 Triggers: "競合分析", "competitive analysis", "競合調査", "差別化", "ポジショニング", "competitor", "競合", "市場プレイヤー", "ライバル調査", "competitive landscape"
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Use when the user needs to design, run, or audit product or GTM analytics — metric hierarchies and north stars, funnels, cohort retention, churn, pipeline velocity, forecasting, lead scoring, NRR, A/B tests, or analytical SQL — or uploads a dataset to analyze. Triggers on questions like "why did activation drop", "is this metric meaningful", "review my dashboard", or "what should I measure". For writing the spec that defines a feature's success metrics, use product-spec.
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Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.
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Create or improve a Senpai target repository's program.md. Use this skill whenever the user wants to point Senpai at a fresh ML or research target repository, define the research objective, primary metric, benchmark contract, allowed edit boundaries, W&B reporting contract, or prepare a repo for autonomous advisor/student experiment loops.
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Analyze datasets and extract insights. Use when user needs to understand data patterns, statistics, or trends.
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Use when profiling with Intel VTune or AMD uProf for hotspots, top-down pipeline stalls, memory-bound analysis, or roofline data. Not for raw perf stat counters: use linux-perf.
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Use when writing eBPF programs with libbpf, bpftrace, or Rust with aya-ebpf and aya-log, attaching kprobes, tracepoints, or XDP hooks, declaring maps, sharing data with a Tokio loader, triaging verifier or Aya load errors, or porting with CO-RE. Not for full kernel bypass: use dpdk.
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Use when turning perf, DTrace, pprof, or async-profiler stacks into an SVG flamegraph with the FlameGraph scripts, reading one, or diffing two profiles. Not for collecting data: use linux-perf.
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Use when writing a GNU ld script for a bare-metal target, placing code in a flash or RAM region, wiring .data and .bss startup, or fixing a region overflowed error. Not for LTO: use linkers-lto.
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Use when writing reset-to-main startup code, vector tables, VTOR, .data/.bss init, stack setup, startup.s, or crt0 for Cortex-M/RISC-V. Not for the bootloader jump: use bootloaders-embedded.
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Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading. Not for mispredict cost: use branch-prediction-and-speculation.
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Use when work reaches a session boundary or the user asks to hand off, delegate, or get a clipboard-ready prompt. Mode brief packages continuity; mode prompt builds the delegation prompt.
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Use when a human-curated dbt model index must guide BigQuery SQL for a warehouse question. Not for discovering undocumented models or executing warehouse changes.
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Use when the user asks "where to help", "contribution opportunities", or "find a good first issue". Returns data-backed first steps. Not for PR review queues: use gh-review-requests.
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Use when asked to remove old code, migrate consumers, or decide whether to maintain or sunset a system. Not for untracked data or changes without VCS rollback.
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Use when bloated code needs clean re-derivation, or the user says "this module is bloated" or "break it and rebuild". Not for untracked data or changes without VCS rollback.
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Upgrade applications from Laravel Feeds 1.x to 2.x while preserving registrations, output contracts, and rollback data. Use for runtime compatibility, slug migration, ID-based generation calls, and changed generated registration titles.
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Create and register Laravel Feeds exports with lazy Eloquent queries, explicit item fields, item date formats, and application-specific registration models. Use when adding a first feed or changing its data source and registration schema.
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Optimize a pydantic-ai agent's instructions, tool descriptions, output type, and signature inputs using the gepa CLI. Trigger when the user asks to "optimize this agent", "improve tool descriptions", iterate on prompts driven by eval failures, or otherwise improve a pydantic-ai agent against a dataset. Operates in the user's repo with full filesystem access — YOU are the reflection model; the gepa CLI handles minibatches, evaluation, and Pareto bookkeeping.
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