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概要と使いどころ
Calculate and visualize magnetic fields produced by current distributions using the Biot-Savart law, Ampere's law, and magnetic dipole approximations. Use when computing B-fields from arbitrary current geometries, exploiting symmetry with Ampere's law, analyzing superposition of multiple sources, or characterizing magnetic materials through permeability, B-H curves, and hysteresis behavior.
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Compute and analyze gematria (Hebrew numerical values) using standard, ordinal, and reduced methods. Covers word-to-number conversion, isopsephy comparisons, and interpretive frameworks. Use when computing the numerical value of a Hebrew word or phrase, comparing two words for shared gematria values, studying a biblical verse or divine name for numerical correspondences, or connecting a numerical result to its position on the Tree of Life.
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。
Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound using roofline analysis, occupancy calculations, compute/load ratio per tile, and SASS instruction inspection. Produces a decision matrix for optimization strategy selection (cp.async, warp interleaving, tiling, double-buffering, or CuAssembler hand-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。
Parallel SDLC batch runner. Analyzes a list of independent task descriptions, groups them by heuristic file-conflict prediction, dispatches one worktree-isolated background agent per task (each running the full pipeline-orchestrator: BA → Dev → QA → Sec → Docs), and aggregates results into a single batch summary. Use when: - `/sdlc:batch` invokes this skill after parsing its arguments (Step 2 of commands/batch.md) - You have 2+ independent feature/fix descriptions to run through the SDLC pipeline in parallel Do NOT use for: - A single task (use pipeline-orchestrator directly via /sdlc:start) - Tasks that must share a single working tree (this skill isolates each task in its own git worktree)
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。
Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
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 the current branch diff against dev, plan integration tests for changed frontend pages/components, and write them. TRIGGER when user asks to write frontend tests, add test coverage, or 'write tests for my changes'.
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。
Orchestrate complex development with AUTOMATIC parallel subagent execution, continuous dispatch scheduling, dependency analysis, file conflict detection, and IOSM quality gates. Analyzes task dependencies, builds critical path, launches parallel background workers with lock management, monitors progress, auto-spawns from discoveries. Use for multi-file features, parallel implementation streams, automated task decomposition, brownfield refactoring, or when user mentions "parallel agents", "orchestrate", "swarm", "continuous dispatch", "automatic scheduling", "PRD", "quality gates", "decompose work", "Mixed/brownfield".
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 addressing review feedback in analyze or reception mode, or handling GitHub PR feedback in autonomous, interactive, or summary mode for a named PR target. Classifies, applies, replies, or summarizes comments.
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 project and generate Docker configuration: Dockerfile (multi-stage dev/prod), compose.yml, compose.override.yml (dev), compose.production.yml (hardened), and .dockerignore. Includes production security audit. Use when user says "dockerize", "add docker", "docker compose", "containerize", or "setup docker".
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 project and generate or enhance build automation file (Makefile, Taskfile.yml, Justfile, Magefile.go). If a build file already exists, improves it by adding missing targets and best practices. Use when user says "generate makefile", "create taskfile", "add justfile", "setup mage", or "build automation".
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。
Self-improve AI Factory skills based on project context, accumulated patches, and codebase patterns. Analyzes what went wrong, what works, and enhances skills to prevent future issues. Use when you want to make AI smarter for your project.
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 architecture guidelines for the project. Analyzes tech stack from DESCRIPTION.md, recommends an architecture pattern, and creates .ai-factory/ARCHITECTURE.md. Use when setting up project architecture, asking "which architecture", or after $aif setup.
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。
QA workflow for testing a feature or task implementation. Analyzes changes, produces test plans, and describes concrete test scenarios. Use when user says "test this", "write test plan", "what should I test", or "QA this branch".
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。
Refine an existing implementation plan with a second iteration. Re-analyzes the codebase for gaps, missing tasks, and wrong dependencies. Use after $aif-plan or to improve an $aif-fix plan. Optional +check flag validates refinements via a fresh-context subagent.
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。
Set up agent context for a project. Analyzes tech stack, installs relevant skills from skills.sh, generates custom skills, and configures MCP servers. Use when starting new project, setting up AI context, or asking "set up project", "configure AI", "what skills do I 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。
Analyze codebases using a graph database instead of reading source files. Use when understanding code architecture, finding functions or call patterns, tracing data flow, checking dependencies, or answering "where is X used?" questions. Grafema builds a queryable code graph from static analysis — prefer querying the graph over reading files manually.
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Fix Elixir/Erlang AST processing bugs in Grafema beam-analyzer. Use when: (1) Elixir parser returns MODULE node but 0 functions/calls — body nesting issue, (2) Erlang parser crashes with "cannot convert list to string" on OTP 26+ — location format changed from integer to keyword list, (3) pipe operator |> creates spurious CALL nodes instead of desugared function calls — clause ordering bug, (4) multi-module .ex files return only the first module — missing __block__ handler, (5) installing Erlang/Elixir on macOS with outdated Xcode/Clang.
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Fix nodes silently disappearing from RFDB when an enricher or post-resolution pass writes new nodes via BatchHandle. Use when: (1) writing a TypeScript enricher that should ADD nodes/edges to existing files, (2) after calling `client.createBatch()` + `batch.addNode({file: 'X', ...})` + `batch.commit()` the original nodes in file X have vanished, (3) tests that load a graph fixture, run an enricher, then query original nodes get empty results, (4) a verification step shows expected nodes pre-enrichment but 0 post-enrichment. Root cause: `BatchHandle.commit()` invokes RFDB's `commit_batch` which AUTO-POPULATES `changed_files` from each added node's `file` field, then the server DELETES all existing nodes whose `file` matches before inserting new ones — file-level upsert semantics. This is correct for the JS/Haskell analyzers (which fully re-emit a file's contents) but catastrophic for enrichers that only ADD to existing files.
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。
How to write a Grafema batch-mode plugin that reads the graph and filesystem, then writes new edges/nodes directly to RFDB. Use when: (1) need to add project-specific edges that generic analyzers can't produce, (2) need to read config files (JSON/YAML) and trace values into code, (3) implementing framework-specific resolvers (Django settings, Express routes, pipeline configs). Covers: grafema.config.yaml plugin config, RFDB field naming (src/dst NOT source/target), addEdges/addNodes API, RFDBServerBackend connection pattern.
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。
Systematic methodology for achieving 100% backward dataflow reachability in a new language. Create gauntlet fixture, write trace, diagnose gaps, fix analyzer/algorithm, iterate to 100%. Language-agnostic process. Use when: (1) adding a new language to Grafema, (2) auditing dataflow coverage for existing language, (3) user says "/dataflow-gauntlet".
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。
Cyclical dogfooding loop: test AI-AGENT-STORIES.md against the live graph, discover new stories, analyze gaps, fix root causes, re-test, write report. Use when: (1) user says "/gap-loop", (2) periodic dogfooding session, (3) after a release or major feature to re-validate stories, (4) before sprint planning to prioritize product gaps.
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。
Fix RFDB V2 graph data silently not persisting to disk after analysis. Use when: (1) rfdb-server reports "0 nodes, 0 edges" on restart despite successful analysis, (2) segment directories exist but are empty (no .bin files), (3) manifest_index.json shows total_nodes: 0 despite analysis logging 70k+ nodes, (4) Docker builds produce empty graph databases, (5) --clear flag used before analyze command. Root cause: GraphEngineV2::clear() replaces the store with MultiShardStore::ephemeral() which has path: None, causing all flush operations to write to in-memory buffers only.
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。
This skill should be used when the user asks to "minimize the repro path", "/minimize", "shrink the crash steps", "精简复现路径", "找最小复现", "把 12 步压成 3 步", or wants to compress a recorded crash repro_path into the shortest sequence that still reproduces the same crash signature. Drives live Android replay via mobile-mcp + ui-mcp + log-mcp, validates each candidate subset with analyzer-mcp's signature, and updates the session's crash record with the verified minimal path; iOS sessions use the static fallback because ui-mcp replay is Android-only.
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 with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django/Hibernate/Rails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.
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。