「substrate」の検索結果

Spin up a new loop (domain) in a file-based knowledge base — bootstrap the substrate if it's missing, gather the loop's charter, scaffold domains/<loop>/README.md, then do ONE real test run and record it in the loop's Timeline and LOG.md. Use when the user says "set up a new loop", "create a domain", "start a new beat/workstream", or names a recurring job they want the agent to own.

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。

Presentation/Core/Engine split control plane — the canonical entry point for any work decomposing the Yuzu server into three binaries (presentation · core · engine) per ADR-0031-presentation-core-engine-decomposition / ADR-0032 (the admission interlock) / ADR-0033 (access-control spine). Use when the user says "/split", "/pce-split", "/decomposition", asks to plan or implement a split workstream (WS-A*, WS-B*, WS-X*), asks "what's our gap to the presentation/core split" or "is it safe to extract presentation/the engine yet", asks to audit split readiness, or starts work touching the seam (handlers calling stores directly, the INV-31-4 contract test, the Drogon port, `server/presentation` or `server/engine`, the `uce` DB grants, component service identity, or the ADR-0032 admission/grant/audit substrate — `use_case_run_id`, the release log, the capability-declaration registry).

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。

Understand what SKMTC is, how its engine works, and the architectural invariants — for agents building or extending infrastructure *around* SKMTC rather than authoring generators or running the CLI. Covers the three-phase pipeline, the host/Worker boundary, cross-generator coordination, the manifest, the attribution / gen-maps (provenance) subsystem, the package graph, the dependency substrate, and the design decisions that make SKMTC behave unlike a typical codegen tool. Use this skill when the user asks "what is SKMTC", "how does the SKMTC engine work", "explain the SKMTC architecture", or is building platform infrastructure around SKMTC — a hosted generate API, a schema or generator registry, tracing or provenance tooling, a web app or SaaS that wraps the engine, or platform-level CI integration. This skill is the system mental model. It does NOT cover authoring generators (→ skmtc-generator), running CLI commands (→ skmtc-cli), or diagnosing broken runs (→ skmtc-debug).

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。

Query the witness-anchored ruLake cache — search, verify, explain, or refresh. Returns ranked results plus a decision_trace block with cost, latency, witness match, and substrates used.

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 hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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。

Assess drug-drug interactions — CYP metabolic interactions (substrate/inhibitor/inducer), transporter (P-gp, BCRP, OATP) effects, pharmacodynamic synergy/antagonism, clinical significance scoring, and management recommendations. Use for polypharmacy review, prescribing decision support, and safety analysis when adding or switching drugs.

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。

Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, validation, makewindows, cross-assembly liftover (liftOver/CrossMap), and BED<->VCF/BAM/FASTA conversion. Use when reading, creating, validating, sorting, lifting between genome builds, or converting interval files, preparing inputs for bedtools/tabix/bigBed, or debugging an off-by-one or empty-overlap result.

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。

Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, validation, makewindows, cross-assembly liftover (liftOver/CrossMap), and BED<->VCF/BAM/FASTA conversion. Use when reading, creating, validating, sorting, lifting between genome builds, or converting interval files, preparing inputs for bedtools/tabix/bigBed, or debugging an off-by-one or empty-overlap result.

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 hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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。

Track the full Toulmin epistemic ancestry of every claim produced by a multi-agent swarm. Each agent output is tagged with an argument chain (claim / data / warrant / relationship) derived from SwarmTracer spans and LineageEdge records, so operators can zoom into any sub-claim and see precisely which agents asserted it, what triggered each assertion, and how contradictions propagated or were synthesised away. The SwarmTracer pattern — wrapping the SwarmAgentExecutor to capture spans, record messages, and build a lineage graph — is the concrete runtime substrate. Argumentative lineage lifts that graph into the Toulmin vocabulary so reasoning provenance is both machine-queryable and human-interpretable.

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。