「framing」の検索結果

Direct cinematic shots and coverage for AI-assisted content production. Use when an agent must translate story intent into framing, shot size, lens and perspective, camera position, movement, blocking, screen direction, lighting-aware coverage, continuity, shot specifications, or diagnose why generated or filmed shots do not cut together.

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Transform vague product ideas into evidence-anchored problem statements for PRD v0.1 Spark. Triggers on starting new products/features, validating market opportunities, drafting PRD Why sections, or requests like "frame the problem", "define pain points", "write problem statement", "start v0.1", "what problem are we solving". Outputs structured problem tables with CFD evidence IDs.

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One-shot perspective reset that surfaces blind spots mid-work. Scans what the user has been doing, generates one abstract reframing question, and runs 3 quick checks (scope drift, side effects, better approach) in under 10 lines. No dialogue, no code. Trigger phrases: "/stepback", "step back", "한발 물러서", "넓은 관점", "놓치는 거 없나", "방향 맞나", "zoom out", "큰 그림", "방향이 맞는 거야", "잠깐 멈춰", "지금 뭘 하고 있는 거야", "blind spot", "재확인"

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Build or resume mathematical-modeling competition work, including CUMCM, MCM/ICM, graduate and regional contests, data challenges, and open-topic statistical modeling. Guide problem framing, modeling, computation, validation, Chinese or English LaTeX writing, and reviewed PDF/source delivery using current official requirements. Not for ordinary paper polishing.

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Turn a rough idea, a bug, or a batch of unsolved problems into well-structured GitHub issue(s) and file them with `gh`, auto-classifying type + labels from the content (recommend, then confirm) and following Rove's conventions (beginner-friendly framing, concrete file pointers, an acceptance checklist, zero AI/Anthropic attribution). Handles single, batch, and "file the unsolved problems" modes. Use when the user says "file an issue", "open a GitHub issue", "draft a good first issue", "batch these as issues", "上报 issue", "提个 issue", "报个 bug", "把没解决的发上去", or wants to hand low-priority tasks to outside contributors. Distinct from the daemon-owned internal issue store (`rove api issue-*`); this skill is for outward-facing GitHub issues.

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数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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Sentence-level and section-level craft for instructions Claude will actually follow, in any container — project instructions, a standalone prompt, or skill body text. Use when the text exists or is being drafted and the question is quality: "why is Claude ignoring this instruction", "rewrite this prompt so it works", "improve my project instructions", "make this instruction stick", "is this too prescriptive", "how much structure does Sonnet need versus Opus". Covers imperative construction, positive framing, strategic-versus-procedural calibration per model, extended thinking, complexity scaling, and a quality checklist. For deciding WHICH container the instruction belongs in, use crafting-instructions. For building, testing and packaging a whole skill directory, use creating-skill.

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Break out of a locked problem frame by picking one disciplined move — reframe, provocation (Po), random stimulus, SCAMPER, inversion, structured analogy, constraint play, or family traversal — and committing to it before evaluating. Use when stuck, when options feel narrow or obvious, when iterations produce variations of the same idea, or when the user says "widen this", "break out of", "think differently", "I'm stuck", "feels too obvious", "stress-test the framing", "what am I missing", or holds two related examples and asks what lies between or beyond them. Complements challenging (which evaluates) and convening-experts (which synthesizes viewpoints); this skill generates distance, not judgment.

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Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.

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Plan and facilitate design sprints from challenge framing through prototype testing.

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Map user Jobs-to-Be-Done with functional, emotional, and social dimensions plus outcome expectations. Use when reframing product decisions around user motivations rather than features.

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Use when the user wants to convert a video between horizontal and vertical orientations while preserving the inverted aspect ratio (16:9 ↔ 9:16, 4:3 ↔ 3:4, 21:9 ↔ 9:21). The skill crops a narrow band from the source and tracks the active speaker — the person whose mouth is moving — via MediaPipe face landmarks and mouth-aspect-ratio variance, so the talker stays in frame even when other people are visible. Triggers — "横转竖", "竖转横", "做成竖屏发抖音/视频号/小红书", "16:9 to 9:16", "make this vertical for Reels / TikTok / YouTube Shorts", "crop to portrait", "convert to landscape".

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Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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Suede-owned sales-enablement discipline. Use when creating pitch decks, one-pagers, objection guides, demo scripts, talk tracks, battle cards, proposal structures, buyer cards, or deal-level ROI framing. NOT FOR: setting price or terms (use suede-pricing), building the offer (use suede-offers), sending outreach (use suede-cold-email), or publishing unsupported proof.

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Suede-affiliated offer design for value framing, bonuses, guarantees, risk reversal, honest scarcity, naming, and payment structure. Use when the user is building or diagnosing a service, course, coaching, information-product, agency, or high-ticket B2B offer. NOT FOR: SaaS tier and value-metric architecture (use suede-pricing), sales-page copy (use suede-copy), or in-product upgrade prompts (use suede-paywalls).

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Suede-affiliated ethical application of behavioral science to marketing decisions, including framing, anchoring, social proof, loss aversion, choice architecture, and friction. Use when the user needs a named psychological model, an evidence-aware application, and a testable hypothesis. NOT FOR: clinical or mental-health advice, deceptive dark patterns, page implementation (use suede-site-alchemy), or pricing design (use suede-pricing).

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This skill should be used when the user asks to "compose a frame", "set up a grid", "plan shot composition", "place the focal point", "decide where elements enter and exit", "add a parallax/camera move", "adapt a layout for 16:9 / 9:16 / 1:1", or "keep content inside safe areas". Covers grids, safe areas, focal hierarchy, and 2D/3D camera framing.

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Use when a task asks for a model, a score, a forecast or a prediction-driven feature — pin down the decision, target, unit, prediction time, metric and baseline before writing any modelling code

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Brainstorming interactively to deepen ideas via four modes (Expand/Propose/Evaluate/Subtract). Does not write code. Not for decisions (Magi), feature specs (Spark), or one-shot reframing (Flux).

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Validate Blender animation attempts before accepting them by rendering contact sheets, checking silhouette stability, flicker, framing, subject dominance, layer separation, export compatibility, and motion-design coherence. Use after animation renders or when the user says motion/morph/outside elements look bad.

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Set up Blender cameras with cinematic intent — focal length, depth of field (f-stop / focus object), composition (rule of thirds, leading lines), animated cameras (orbit, dolly, push-in), tracking constraints. Use whenever the user asks to "set up the camera", "frame the shot", "make it look cinematic / hero / portrait / wide-angle / telephoto", "add depth of field", "orbit the camera", or any composition/framing request. Make sure to use this skill even if the user does not say "camera" — also covers "hero shot", "close-up", "from above", "shallow focus", "85mm portrait look".

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When the user wants to plan, build, or optimize SMS or MMS marketing — including welcome flows, abandoned cart texts, post-purchase, win-back, promotional sends, or transactional/auth SMS. Also use when the user mentions "SMS marketing," "text message campaigns," "SMS sequence," "SMS automation," "abandoned cart text," "post-purchase SMS," "Klaviyo SMS," "Postscript," "Attentive," "Twilio," "A2P 10DLC," "TCPA," "SMS compliance," "short code," "toll-free SMS," "MMS campaign," "should I do SMS," or "SMS vs email." For email sequences, see emails. For SMS copy framing, see copywriting. For opt-in popups that capture phone numbers, see popups.

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Explore vague or ambitious ideas into a right-sized requirements-only unified plan. Use when the user wants to brainstorm, think through scope, decide what to build, or needs collaborative product framing before planning.

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Create structured plans for multi-step work, including software and non-software tasks. Use when asked to plan, break down implementation, plan from requirements, or deepen an existing plan; prefer ce-brainstorm for exploratory framing.

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