5,567 件 ・ 関連度順
概要と使いどころ
Use when choosing kernel spinlocks versus mutexes, using RCU, seqlocks, completions, or memory barriers, or debugging scheduling-while-atomic. Not for userspace atomics: use concurrency-debugging.
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 reducing header bloat and compilation cascades with Include What You Use, interpreting IWYU reports, mapping files, forward declarations, or CMake integration.
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 inspecting .debug_info or .debug_line sections with dwarfdump or readelf, working with split-DWARF .dwo files, setting up debuginfod, or checking how LTO and stripping affect debug info.
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 loading core files in GDB or LLDB, enabling core dump generation via ulimit or coredumpctl, mapping symbols with debuginfod, or extracting backtraces from production segfaults.
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 reading llc output, tracing IR through legalization and instruction selection, or scoping a new LLVM target. Not for IR-level passes: use llvm-ir-and-passes.
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 writing a custom bootloader, jumping to application code, relocating VTOR, or implementing DFU/USB firmware update on Cortex-M. Not for reset-to-main: use baremetal-startup.
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 adding telemetry, composing an observability surface, reviewing alerts, shipping a production feature, or diagnosing a production issue. Not for live failures, profiling, or launch runbooks.
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 making an architectural decision, changing a public API, shipping a feature, or recording a codebase term. Not for domain language: use domain-modeling. Not for docs: use docs-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 pinning down domain terminology, maintaining the domain model, or when a term conflicts or needs sharpening.
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 for a council, second opinions, or parallel investigation. Not for cross-examination of existing proposals: use cross-critique.
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 an implementation has more workarounds than structure and another patch will not pay. Not for in-place re-derivation: use breaking-driven. Not for one-artifact rewrites: use rewrite-clean-v0.
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 to set up the agent environment. Covers credentials, placeholders, tool access checks, a uv venv, a pack install, or a role-model mapping file. Not for unattended runs.
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。
Optimize a pydantic-ai agent's instructions, tool descriptions, output type, and signature inputs using the gepa CLI. Trigger when the user asks to "optimize this agent", "improve tool descriptions", iterate on prompts driven by eval failures, or otherwise improve a pydantic-ai agent against a dataset. Operates in the user's repo with full filesystem access — YOU are the reflection model; the gepa CLI handles minibatches, evaluation, and Pareto bookkeeping.
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Measure the active Agent model with pinned offline ModelTrace sampling and return an unsigned closed-set attribution record. Use before Agent-originated commits, Issue/PR creation or material updates, comments, reviews, and current collaboration writes in either execution mode. Do not treat a fingerprint, declared model label, or record reference as authenticated identity, permission, independent review, or acceptance.
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Design or implement the smallest complete executable evidence for a governed Proto UI behavior. Use for conformance cases, regression tests, cross-layer verification, test-entity mappings, failure-path coverage, or validation-plan design. Do not use tests to decide unsettled semantics.
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。
Inspect the pinned, active, and locally available Odylith versions.
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 pinned, active, and locally available Odylith versions need inspection.
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 Odylith Discipline pressure behavior, zero-credit hard laws, host/lane parity, or benchmark-proved learning-spine contracts need validation.
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.
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Compute a unified outer hull around a group of hex tiles that includes multiple disconnected sub-clusters separated by 1-tile gaps. Use when: (1) rendering nested hierarchy hulls (e.g. "package" hull wrapping multiple "sub-region" hex islands), (2) the naive boundary trace outlines each island separately instead of as one bag, (3) flood-fill from outside walks THROUGH the 1-hex gaps and marks them exterior, (4) heuristic "non-adjacent same-group neighbours" over-fills L-shape concavities, (5) axial-only opposite-pair filling misses non-axial bridges between islands. Solution: morphological close (dilate + erode) on the hex grid — fills exactly the interior 1-hex gaps without affecting exterior corners, then trace boundary on the closed shape.
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。
Diagnose React + Three.js (or any browser canvas) frame stutter by capturing a CDP CPU profile via Playwright instead of guessing from `[perf]` console logs. Use when: (1) frame time is bad but `[perf]` instrumentation doesn't pinpoint the offender, (2) you've spent more than ~30 minutes "fixing" suspects without measuring, (3) hot paths involve animation systems, allocation bursts, or invisible scene-graph traversal, (4) you need self-time per function with line numbers, not aggregate "render slow". The `console.warn` pattern is good for monitoring; CDP is needed for diagnosis. Covers script template, sourcemap setup so minified function names are decoded, and interactive variant where the user drives the interaction and signals stop via `touch /tmp/<flag>`.
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Speed up Rust development iteration when `cargo build --release` takes 10+ minutes even for one-line edits. Use when: (1) every release rebuild takes minutes despite tiny edits, (2) Cargo.toml has `lto = "fat"` and/or `codegen-units = 1` in `[profile.release]`, (3) you want a production- shaped build for testing without paying the LTO cost on every iteration, (4) you suspect cargo is re-linking the whole binary instead of doing incremental work, (5) developing rfdb/grafema-orchestrator/large Rust binaries where full LTO is enabled for shipped builds. Adds a `fast` profile that inherits release but disables LTO, raises codegen-units, enables incremental compilation — first build still slow because it populates a fresh `target/fast/` dep cache, but every subsequent rebuild drops from ~13 min to ~13 s (≈60×).
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。
Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.
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Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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。