「grain」の検索結果

Active Directory security audit using the MITRE ATT&CK framework. Full domain enumeration, trust mapping, GPO analysis, ACL abuse paths, ADCS attacks (ESC1-ESC8), delegation abuse (constrained/unconstrained/RBCD), fine-grained password policies, LAPS deployment, service account security, and Kerberos configuration. Uses enum4linux-ng, netexec, impacket, ldapsearch, certipy-ad, bloodhound-python, and rpcclient. Produces attack path diagrams, prioritized risk register, and PoCs. Chains into /gh-export for issue filing.

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。

SolidJS fine-grained reactivity — signals, effects, memos, stores, resources, control-flow components. Load when writing Solid components or reactive state.

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。

Manually trigger artifact regeneration, versioning, and chore commit. Use when you need fine-grained control over when registry artifacts sync (outside the automated sync-artifacts.yml workflow).

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 scheduled background jobs that run on cron schedules

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。

Build read models by reducing events into queryable state

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。

Define Malli schemas for commands, events, and queries using defschemas

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。

Implement event-driven side effects that react to domain events

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。

Implement query handlers that read from event-sourced read models

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 a task needs the judgment of a Data Warehousing Specialist — defining the grain of a fact table before modeling it, choosing a slowly changing dimension (SCD) type for a dimension attribute, designing an incremental ETL/ELT load strategy against a full-refresh alternative, resolving a conformed-dimension conflict across source systems, or diagnosing why a BI report's numbers don't reconcile to the source system.

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 a task needs the judgment of a licensed barber — diagnosing a fade/taper request against a client's actual grain and density, sequencing a shop's chair time and rebooking, deciding straight-razor vs. shavette for a shave, or reading a haircut complaint to find the real cause.

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 a task needs the judgment of an Agricultural Equipment Operator — calibrating planter seed population and singulation, diagnosing combine header or separator loss, sequencing planting or harvest across fields under a weather-constrained window, sizing machinery capacity in acres/hour against a season's acreage, or evaluating PTO/grain-bin entrapment safety on a job site.

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 a task needs the judgment of an agricultural engineer — sizing a center-pivot or drip irrigation system's peak capacity against crop water demand, computing lateral grain-bin wall pressure with Janssen's equation, sizing a waterway or tile-drainage system with Manning's equation and an NRCS design-storm return period, or building a manure/nutrient first-year availability mass balance against a crop's N requirement.

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。

Write the SQL or MongoDB query that answers a question and keeps answering it: the right grain, no fan out, parameterised window, checked against something known. Use when a number is needed that no report carries, when an existing query returns something that looks wrong, when a query is slow or expensive, or when somebody asks for a one off pull.

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。

Cycle de coordination ai-01 (coordinateur UNIQUEMENT — jamais sur un worker). Lit memoire + dashboards + inbox + GitHub, merge les PRs pretes, tranche les design-gates, dispatche des grains par lane, reporte. Arguments: [--dispatch] [--focus <topic>]

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。

Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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。

Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".

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。

Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.

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。

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

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。

Generate the success criteria for a task or question, then review work against them. Given a task, goal, or open-ended question, decompose it into scenarios, evaluation perspectives, and fine-grained weighted YES/NO criteria using the Recursive Expansion Tree (RET) method; if work is supplied, score it criterion-by-criterion and surface what is missing or could be better. Use when asked to self-review or check your own work, judge whether a task is done well or completely, build a definition-of-done or completeness checklist, create an evaluation rubric or grading criteria, score or grade answers to a question, set up an LLM-as-judge rubric, or when the user mentions self-review, completeness check, success criteria, evaluation criteria, scoring rubric, Qworld, or the RET algorithm.

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。

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

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。

Implement Role-Based Access Control (RBAC), permissions management, and authorization policies. Use when building secure access control systems with fine-grained permissions.

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。

1950s-1990s nostalgia with skeuomorphic touches, grainy textures, retro color palettes, and pixel-style typography.

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。

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

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。

Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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。