「edge」の検索結果

RFDB server becomes unresponsive (60s+ timeouts) during enricher addEdges write storms. Use when: (1) RFDB CPU spikes to 60-90% after addEdges calls; (2) subsequent reads time out at 60s; (3) server stays catatonic for 5-15 min after a write storm; (4) Tokio main thread parked (write-lock contention). Root cause: add_edges() calls maybe_auto_flush() which has a memory-pressure path that fires on NODE count — not edge count — and holds the exclusive write lock during disk I/O. Fix: separate maybe_auto_flush_edges() uses byte-limit only.

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Edge Computing Specialist IA — Expert en edge computing (Cloudflare Workers, Deno Deploy, Vercel Edge, Bun)

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Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.

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Manage the personal knowledge wiki. Use when the user shares articles, documents, or asks to organize knowledge; when a conversation produces insights worth preserving as structured knowledge; or when the user asks about the knowledge base.

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[omh] Scattered knowledge needing a wiki: wiki construction blueprints and retained knowledge capture with destination-aware external knowledge connection guidance. Use when the user says: wiki, project wiki, build a wiki, start a wiki, organize my notes, external knowledge store, knowledge base, Obsidian.

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Builds and deploys containerized workloads on Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Fargate, and ECR (Elastic Container Registry). Covers general EKS knowledge, Karpenter, AWS Load Balancer Controller and leveraging various open source Kubernetes projects with EKS. Covers general ECS knowledge, task definitions, Fargate services, ECS Exec, ECS Express Mode and ECS Managed Instances. Covers general Elastic Beanstalk knowledge, Elastic Beanstalk configuration and platforms supported by Elastic Beanstalk. Covers general ECR knowledge, ECR repository setup and lifecycle policies. Includes recommending, enabling, and reading Amazon ECS Action Logs to troubleshoot control-plane failures (deployment rollback/circuit-breaker, task placement, scaling, task replacement). Applies when deploying, debugging, or optimizing containers on AWS. Should be used instead of relying on internal knowledge for these services.

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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Develop pedagogical content knowledge for a topic by surfacing common misconceptions and effective representations. Use when preparing to teach unfamiliar content or improving teaching of hard topics.

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Facilitate a structured conversation to create a project-specific knowledge base document. Produces a knowledge-base.md that primes AI with the project's tech stack, architecture, trusted sources, and project structure. Use when the user says 'set up knowledge base', 'prime the project', 'onboard AI', 'create knowledge base', 'set up project context', or 'configure AI context'.

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Develop pedagogical content knowledge for a topic by surfacing common misconceptions and effective representations. Use when preparing to teach unfamiliar content or improving teaching of hard topics.

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Design an edge routing and geo-distribution strategy — latency routing, failover, and edge logic. Use when asked to "geo-route our traffic", "reduce global latency", or "design edge failover".

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Use at the start of any session on a project with a build graph (`.hedgehog/hedgehog.db`), and again at every claim/dispatch/verify cycle within it. Owns consuming the build graph — claiming task packets, delegating each to the layer's agent, running `hedgehog verify` to gate the commit, recording debt and decisions, the intent check at an intent's last layer, and when to clear context. Not for a build agent handed an already-claimed packet — that agent never claims, dispatches, or verifies; this skill is the orchestrating session's own.

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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.

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Pattern for adding derived edge types in the Grafema Rust orchestrator (main.rs). Use when: (1) need to create new graph edges derived from existing edges (e.g., MODULE-level DEPENDS_ON from IMPORTS_FROM), (2) Datalog joins on large edge sets time out, (3) adding enrichment steps that combine data from multiple resolver outputs, (4) need to understand how resolver outputs flow through the orchestrator pipeline.

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Curate the project's knowledge base and memory in .unikit/memory/ — the indexed reference library of coding standards and framework usage patterns. ALWAYS use this skill whenever the user adds knowledge from a source: a URL, a file, a folder path, a PDF, an article, or a book — any path/link/document is a strong signal for the knowledge base, not a quick rule. Also use it to research or document how the project uses a framework, or to add a researched core/stack knowledge entry. Sub-commands by phrasing: "migrate the rules" or "promote rules" runs migrate-rules; "optimize the knowledge base" or "optimize the rules" runs optimise (extract large sections into references); "validate" re-syncs RULES_INDEX.md; "--module <id>" targets a module. For a quick one-line convention or correction typed as a prompt ("always do X", "never use Y", "remember this") with no source, use /unikit-rules; for architecture decisions use ARCHITECTURE.md.

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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Durable categorized record-keeping for any predicate-enabled walk. Trigger when: - Classifying a step's output for recording (decision, finding, debt, feedback). - Promoting a scratch artifact to the ledger, to repo/docs, or to a category home (.ledger/tech-debt/, .ledger/process-feedback/). - Auditing whether a draft is structurally ready for its destination. - Running a zero-context sensibility read before committing to promotion. - Recording a known limitation as a tech-debt entry (not prose in the log). - Recording a friction, miss, improvement, or process amendment. - Prompt contains: /record, record-keeping, promote, ledger note, zettelkasten, scratch-to-ledger, zero-context read, over-strip, classification trigger, tech-debt, process-feedback, amendment.

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Reviews a set of knowledge files or a knowledge base listing and returns a draft hygiene table, one row per file: duplication signals with the matching file, staleness signals, conflicts between files on the same topic with both passages quoted, missing or unverified owners, and one proposed action per file (Keep, Update, Merge into, Retire, Assign owner, Confirm with owner) for the owner to decide. Never edits, merges, moves or deletes a file. Use when the user asks to "review our knowledge base for duplicates", "which KB articles are stale", "find conflicting articles", "which articles have no owner" or "run a hygiene check on these files". Do not use for review-date sweeps of a document library with owner reminders, use sharepoint-review-sweeper instead; for writing one article, use knowledge-article-drafter. Drafts for human review; never approves, authorises or signs off.

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Expert en edge computing v2 (CDN, edge functions, WebAssembly, IoT gateway, contexte DZ)

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リファクタリングを「更新されたチームの理解と、コードが表現する過去の理解の差分同期」と捉え、今回得た新知識（要求・ドメイン知識・制約）が naming と responsibility へ反映されているか、boundary が現在の understanding を表現しているか、diff の削除行やコメントに現れる過去の design history と constraint を失っていないかを diff-time で確認する。Knowledge Delta の signal は diff（追加/削除された hunk・コメント・ADR 参照）を一次情報とし、PR 本文が供給されるときは補助に用い、不確実なら question に留める。ドメイン用語の一貫性は ubiquitous-language-naming、集約/コンテキスト境界の設計判断は bounded-context-language、投機的抽象化・caller special-case は altitude-generalization、スコープ逸脱/前提破壊は fix-scope-integrity、振る舞い変更と構造変更の分離は behavior-structure-separation、完了主張の反証は refactor-claim-audit へ委譲する

Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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Agent fallback for CDelayedCall2_CServerSideClientBase_UpdateAcknowledgedFramecountCall_vtable (auto-generated, category: func). Locate CDelayedCall2_CServerSideClientBase_UpdateAcknowledgedFramecountCall_vtable in the CS2 engine module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CDelayedCall2_CServerSideClientBase_UpdateAcknowledgedFramecountCall_vtable, CDelayedCall2_CServerSideClientBase_UpdateAcknowledgedFramecountCall_vtable

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Feishuのナレッジベースをたどり、ページの所在確認や作成・移動・名前変更を支援します。リンクから対象を特定し、文書の閲覧・編集につなげます。

• wikiリンクの対象を確認したいとき
• ナレッジベースの階層を確認したいとき
• 指定した親の下にページを作りたいとき

Compile one or more OpenViking knowledge bases or document collections into topic-organized, evidence-grounded high-level knowledge, including cross-source findings, trends, changes, drivers, comparisons, implications, and uncertainties. Use with ov compile when the user asks to distill or synthesize a knowledge base, compare multiple collections, or derive higher-order insights such as changes across financial reports; do not use for document-by-document summaries.

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。