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概要と使いどころ
Deep cross-domain synthesis engine for the vault. Triggers on "synthesize X", "what do I know about X", "connect ideas about X", "cross-domain analysis of X", "map everything in the vault about X". Searches comprehensively, surfaces non-obvious patterns and tensions, then challenges the user to generate original thought. The vault should surprise you — if synthesis only confirms what you already think, it failed.
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。
Framework-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.
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。
FastAPI-specific delta on top of python-foundation:sqlalchemy-patterns: async engine and AsyncSession lifecycle, get_db dependency injection, async-safe lazy loading rules, async Alembic env.py wiring. Used by fastapi-architect (model definitions) and alembic-specialist (column type finalization and migration generation). Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Manage async database sessions with AsyncSession and async_sessionmaker. - Inject sessions into routes via the get_db() dependency. - Apply async-safe lazy loading (lazy="selectin" or "raise"; never sync lazy loads). - Integrate async Alembic env.py for migration autogeneration. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - FastAPI routing and Pydantic schemas — see fastapi-plugin:fastapi-conventions. - Alembic migration execution (that's alembic-specialist's job) — this skill covers definitions.
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 the user needs to deploy, operate, secure, or scale running infrastructure — cloud architecture (multi-region, VPC, FinOps), CI/CD and deployment strategies, secrets/IAM/supply-chain security, observability and SLO alerting, SRE and incident response, Kubernetes vs ECS and autoscaling, Terraform/Pulumi/CDK, or platform engineering and DORA metrics. Also for generating runbooks, pipeline configs, Terraform modules, or Kubernetes manifests. For choosing the system's architecture or stack before it runs (monolith vs microservices, which database), use software-architect.
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 the user wants to get found in Google or AI engines (ChatGPT, Perplexity, AI Overviews) — keyword and content strategy, technical or content SEO audits, schema markup, link building, topical clustering, E-E-A-T, AI citation (AEO), or deciding whether SEO is even the right channel for their startup. Calibrated for early-stage, low-authority sites. Not for paid acquisition or channel-mix/CAC analysis (use channel-expert), or writing the page copy itself (use copywriter-skill).
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。
Choose, configure, launch, and collect bounded subagents for research and engineering decisions. Root agents and delegation-capable subagents should read this before delegating: every task requires an explicit model tier, and high-leverage work such as research ideation, round planning, plateau pivots, large research reviews, hard optimization, disputed evidence, and expensive experiment portfolios requires frontier judgment.
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。
Apply the Minimum Sufficient Work (MSW) principle through the MSW Kernel to scope, execute, verify, and stop agent work. State the requested outcome and smallest proof, admit a claim only when deleting it would leave the contract unmet or unproven, do and prove each necessary claim, and halt at the fixed point. Use for implementation, debugging, review, research, planning, documentation, or any task where scope control, proportional evidence, or avoiding overwork matters. Also use when the user says MSW, Minimum Sufficient Work, do only what is necessary, avoid over-engineering, or stop when the result is proven.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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 user, especially a new hire, asks for mentoring, guidance, or explanation of infrastructure or engineering practices. Not for code 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。
Use when a long session has accumulated stale or conflicting context or the user asks to refresh it. Not for remote, credential, publish, deploy, or irreversible 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。
Use when asked to build, run, or triage a coverage-guided C/C++ fuzz campaign on the libFuzzer or AFL++ engine. Not for harness design: use fuzz-harness-writing.
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 the user asks about recent engineering work, what the team is working on, planning or roadmap material, or an explicitly requested Slack summary. Not for remote or irreversible 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。
Use when a user needs to create or improve a deterministic, engine-agnostic fuzz harness for raw or structured target inputs. Not for coverage measurement: use fuzzing-coverage-analysis.
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 user builds or styles a web frontend or asks to make it not look AI-generated. Not for component-level UI work without the token system: use frontend-ui-engineering.
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 asked to build or modify production-quality components, layouts, state, and pages with WCAG 2.1 AA and real content. Not for token-system design: use frontend-design-deslop.
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。
Recover from stale RFDB nodes that survive `commit_batch` cleanup despite having their `file` field listed in `changed_files`. Use when: (1) re-running an enricher/orchestrator step but old data with the same file field stays in the graph, (2) `commit_batch(changed_files=[X], ...)` reports success but a query later still returns nodes with `file = X`, (3) you re-pointed structural nodes (e.g., DIRECTORY/FILE) from a synthetic file path to real file paths and the old synthetic-path nodes won't go away, (4) graph-stream / find_by_type returns a mix of "stale" and "new" nodes for the same logical entity. Root cause: `handle_commit_batch` calls `engine.find_by_attr({file: X})` to enumerate nodes-to-delete, but that query path returns only a PARTIAL set after compaction (L1 segments and the file→nodes index can desync). Workaround: enumerate stale nodes via the TS rfdb client `queryNodes({file: X})` (different code path that finds all of them) and call `deleteNode(id)` per node, then re-commit the intended state. Related: rfdb-manifest-l1-carryforward (same L1 family).
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 silent incorrect results in RFDB Datalog queries that share a variable between two edge atoms on opposite ends (e.g. self-join patterns like `edge(M, C, "T1"), edge(C, M, "T2")`). Use when: (1) a Datalog rule returns rows that look structurally wrong (e.g. a self-loop rule reports hits but pinning M to each reported id refutes the edge existence), (2) Cypher `MATCH (m)-[:R1]->(c)-[:R2]->(m)` returns the same bogus rows (engine-level bug, not Datalog-specific), (3) results change when you bind one variable explicitly vs. leave it free. Root cause: hash- join fast path in `eval_edge_hash_join` / `eval_incoming_hash_join` unconditionally rebinds the non-key Term::Var without checking whether the variable is already bound in the current row.
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。
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
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。
Delegate an approved finite batch of independent engineering or research responsibilities using the operator's existing worker channel. Use for broad repository discovery, multiple search directions, repeated checks and substantial dirty work; keep coupled single tasks with one worker and explicit solo work in the main session.
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 the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore - same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.
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。
Choose the authorized worker channel and hand over complete engineering responsibilities, including in-scope self-check, repair and delivery. Use for substantial investigation, implementation, continuation and recovery — an ordinary substantial goal is enough, no special phrasing required; use swarm for approved independent batches. Respect the operator's standing ACP/native choice, explicit solo, and tiny or nearly finished work.
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。
迷因知識輪播的獨立審核員——用「幽默感」的可二元判定要素（不協調-解消/良性冒犯/切身共鳴/誇張反差/自嘲/三的法則/秒懂）＋迷因 Hook＋版權安全，對一套迷因圖卡逐條 PASS/FAIL、指到卡給改法。預設有罪推定、禁 LGTM。是迷因輪播 loop 的「審」。觸發：這張夠不夠好笑、驗收迷因圖、meme joker、這個梗會不會冷、迷因輪播審核。
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。
知識型 IG 輪播圖卡的獨立審核員（Joker）——預設有罪推定、禁止 LGTM。對一套輪播圖卡＋文案，按「封面/結構/擴散/CTA/品牌」五類硬規則逐條 PASS/FAIL，指到卡給可照做的改法，並算社群擴散體質分。是 Social Post Producer 迴圈裡的「審」（寫審分離）。觸發詞：驗收這套輪播、這組圖卡會不會擴散、red team 這個 carousel、carousel joker、圖卡審核、這套 IG 圖文夠不夠好。立場是找碴不是稱讚。
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。