「its」の検索結果

Synapse A2A agent communication — sending messages, spawning agents, delegating tasks, sharing memory, and coordinating file edits. Use this skill when: running synapse send/reply/broadcast/interrupt, spawning agents with synapse spawn or synapse team start, managing the task board with synapse tasks, sharing knowledge with synapse memory, locking files with synapse file-safety, checking agent status with synapse list/status, or orchestrating any multi-agent workflow.

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 provides systematic bug detection and discovery capabilities for VS Code extensions. Use when searching for hidden bugs, analyzing code for potential issues, investigating suspicious behavior, performing code audits, or proactively finding bugs before they manifest. Covers static analysis patterns, dynamic analysis techniques, code smell detection, and systematic codebase investigation.

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。

Identifies a sample's true file type independent of its extension: matching magic bytes and structural signatures, detecting container/archive formats, and flagging extension-content mismatches used to disguise malware. Activates for requests to identify a file type, check magic bytes, or detect a disguised/spoofed extension.

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。

Hunts for Linux persistence by inspecting cron, systemd units, shell rc files, SSH authorized_keys, and common autostart locations for attacker-added entries. Activates for requests to hunt Linux persistence, audit cron/systemd for backdoors, or find unauthorized autostart and authorized_keys entries.

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。

Analyzes Rust-compiled malware by detecting the Rust toolchain signature, demangling Rust v0/legacy symbol names, and identifying crate dependencies from embedded paths. Activates for requests to analyze Rust malware, demangle Rust symbols, or identify a Rust binary build and its crates.

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。

Analyzes bootkit and rootkit samples by identifying boot-process tampering (MBR/VBR/ UEFI), kernel-mode components, and stealth hooking techniques from static indicators. Activates for requests to analyze a bootkit or rootkit, examine MBR/UEFI tampering, or identify kernel-mode stealth components.

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 weekly goal and MITs for the week

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。

Decide whether a Pulp test earns its place — before writing it, when reviewing one, or when sweeping a subsystem for low-value, duplicated, or mis-routed tests. Applies a four-question authoring gate, a junk-pattern list, a retention bar, and an evidence template for any deletion, and routes kept tests to the right lane (required gate, pr-fast, slow, nightly). Handles "should I add a test for this", "is this test useful", "audit these tests", "reduce test time", "which tests can go", and "move toward functional tests".

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。

Judge whether a change made things better with demonstrable proxy measures tied to the mechanism it targets, not wall-clock time. Load before choosing acceptance measures for any before/after or A/B claim — CI and fleet speed-ups, build speed, DSP/audio quality, render fidelity. Each proxy names its data source, detection floor and sample size, and every zero is paired with a control on the same instrument.

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。

Audio Unit v2 adapter work for Pulp — picking the right AU component type (aufx/aumf/aumi/aumu) and its matching entry macro, wiring MIDI input and output (including the aumi MIDI-processor adapter), sharing the base-class-free adapter surface, and avoiding the DAW-side component cache that silently masks repackaging.

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。

Keep a plugin/demo repo's screenshots in sync with its UX. When editor / UI / design-token code changes in an opted-in repo (one carrying .pulp/screenshots.toml), re-capture and refresh every place each shot is consumed — README images, web-demo Open Graph (og.png) images, and gallery thumbnails — consistently across the WAM and WCLAP demo sites. Two capture routes: native headless (render_to_png / pulp-screenshot, Skia) for README / native-editor shots, and live-web headless Chrome (playwright-core) for WAM/WCLAP demo OG + gallery images. Backfills demos missing OG images. This is a PORTING / consistency tool — it re-shoots a repo's OWN UI, it never clones another product's art.

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。

Maintain Pulp's macOS Intel (x86_64) portability lint and CI tiering — the PULP_INTEL_CANARY configure gate, intel_canary_lint.py + its allowlist, and the Tier 0-3 workflows (build.yml canary step, intel-portability.yml, nightly-intel.yml, release-cli.yml universal gate). Use when touching cross-arch build wiring, arch-gated SIMD, or Intel CI.

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。

The acceptance bar for anything Pulp can install (a `pulp tool` registry entry, `pulp add` package, or any downloadable). Validate the FULL lifecycle — install AND uninstall, from OUTSIDE a Pulp checkout, with the installed-user's binary — before its README or docs ship. TRIGGER when adding/editing tools/packages/tool-registry.json, a new binary_download/python_pip/npm_package tool, a `pulp add` importer, or writing docs that tell users to install something.

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。

Prove a UI or generation feature actually works before asking a human to look at it. Covers A/B-ing an imported design against its source render, driving controls headlessly so a dead button cannot ship, proving the generator really spawns, and launching the real host (VCV Rack, a DAW). Use before any "take a look" on imported UI, plugin shells, or prompt-to-artifact pipelines.

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 thermal-fluid engineering systems, experiments, CFD, and correlations with explicit assumptions, validity limits, uncertainty, and mechanism-based interpretation. Use for heat transfer, fluid mechanics, phase change, HVAC, energy systems, turbomachinery, piping, or thermal management.

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 Chanjing text-to-digital-person APIs for AI portraits, talking videos, optional LoRA training, polling, and explicit downloads when requested.

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。

Explains DSP math concepts to developers who need the theory behind an algorithm. Use whenever the user asks how a signal-processing concept works, wants intuition behind a formula, or hits a math-shaped bug. Trigger on phrases like "how does a Fourier transform work", "explain z-transforms", "what is convolution", "why does my biquad ring", "what is windowing for", "what causes aliasing", or "how do I convert gain to dB". Also trigger when the user pastes DSP code and asks why it behaves a certain way.

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。

Audit an existing mathematical, logical, algorithmic, or technical proof from its stated conclusion backward as a bounded AND/OR graph of exact obligations. Use to reconstruct conclusion dependencies, determine whether a proof-supplied route establishes the conclusion, or expose hidden assumptions, unmet side conditions, cycles, overly strong steps, or silent route switches. For a source-mapped Lamport rendering use convert-lamport, and for forward hierarchy, construct, and scope conformance use forward-lamport before this skill. This does not decide full theorem-level entailment by searching for a different proof; do not turn the audit into a linear reversed proof, exhaustive proof search, or replacement proof.

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。

Convert an existing prose, flat, or loosely organized mathematical proof into a source-mapped Lamport-style hierarchy without repairing or completing its mathematics. Use when a submitted proof needs a traceable hierarchical rendering before a forward Lamport audit or for clearer presentation. Do not use to invent a proof, certify validity, or restructure an already hierarchical proof merely to audit it.

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。

Audit an existing Lamport-style hierarchical mathematical proof forward for validity and whether its hierarchy, scope, dependencies, side conditions, and proof constructs discharge the stated theorem. Use when the central question concerns a top-down proof with numbered levels, subproofs, ASSUME/PROVE, SUFFICES, PICK, CASE, DEFINE, Q.E.D., or prose equivalents. For an ordinary proof that first needs source-preserving hierarchy, use convert-lamport; for conclusion-first AND/OR obligation reconstruction, use reverse-lamport. Do not use to generate or replace a proof, or to audit Lean, Coq, Isabelle, or other proof-assistant source.

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。

Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.

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 whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).

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。

pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position.

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。

Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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。