1,420 件 ・ 関連度順
概要と使いどころ
Document-code change notification system: traceable doc-code binding via git tree hash, git-diff-driven reconciliation plans with confidence/risk metadata, and idempotent execution. Use when maintaining documentation freshness, detecting stale docs, or binding docs to source code.
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。
Animation, morphing, and shader best practices for Liquid Glass and SwiftUI motion on iOS 26+/macOS 26+ through iOS 27. Use when animating glass or building UI motion: glassEffectID, @Namespace morphing, GlassEffectContainer spacing, glassEffectUnion, glassEffectTransition (.matchedGeometry/.materialize), withAnimation, springs, UnitCurve, PhaseAnimator, KeyframeAnimator, @Animatable, matchedTransitionSource, navigationTransition zoom, scrollTransition, visualEffect, Metal shaders (colorEffect, distortionEffect, layerEffect, [[stitchable]]), accessibilityReduceMotion gating, Instruments/xctrace profiling of hangs and animation hitches. Also fires on symptoms: glass morph not animating, view pops in instead of morphing, glass fades instead of materializing, morph flickers with Reduce Motion, menu glass morphs as a rectangle, animation runs on insert but not removal, PhaseAnimator loops forever or fires once, shader shows clipped edges (maxSampleOffset), animation hitches or main-thread hangs while scrolling glass chrome. For static glass, availability gating, toolbars, UIKit/AppKit glass, or accessibility of the material itself use the liquid-glass skill first.
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。
Pixel-based motion and UI change analysis from frame sequences or screenshots using computer vision and visual comparison. Use when `.transition` or visual effects are not observable via MotionEyes logs, when you only have screenshots/video, when you need regression diffs across builds, or when you need to summarize on-screen changes without instrumentation. Do not use when MotionEyes traces are available and you need precise timing/values; use `motioneyes-animation-debug` instead.
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。
Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures. Use when the user asks to investigate a bug, find the root cause, trace a regression, understand why something broke, or wants a ranked diagnosis with the fastest proof path without making code edits.
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。
Systematically finds and fixes bugs using proven debugging techniques. Traces from symptoms to root cause, implements fixes, and prevents regression.
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。
Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.
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。
Navigate execution flow from an entry point, mapping call chains, external dependencies, and side effects.
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。
Debug Python code using tracebacks, pdb, and structured logging.
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 the RIPE Atlas REST API v2 to find probes, inspect measurements, create and monitor network measurements, and fetch results. Use when the user asks about Atlas probes, measurements, ping, traceroute, DNS, HTTP, TLS, NTP, or globally distributed reachability checks.
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 after the backend follows the model — derive E2E flow scripts from Lean golden traces, replay them against the Lean-backed dev screens and the real REST backend with Playwright, and compare observations. Also use to add a flow when a new command or screen appeared.
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。
Brand-narrative single source of truth (Busabase App-in-Skill) built on the TALE discipline — Trace / Architect / Land / Evaluate. The agent drafts positioning, the message house of value pillars, the story bank, evidence-backed proof points, and vocabulary/tone guardrails; the human curates which drafts become the CANONICAL narrative; a drift monitor flags off-brand usage across channels. Use when the user invokes $kelly-brand or /kelly-brand, mentions brand narrative, brand messaging, positioning, message house, value pillars, story bank, proof points, tone/vocabulary guardrails, narrative quality (NQS), brand drift, or wants a single source of truth for what the brand says.
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。
Busabase App-in-Skill dashboard visualizing a generated MOCK fleet of LLM agents running behind a shared AI gateway for a generic organization. Use when the user invokes $kelly-agent-observability or /kelly-agent-observability, wants to review agent health, call volume, latency, error rates, cost, or trace-level chain breaks across an agent fleet. This is a demo/reference dashboard over a generated mock fleet snapshot seeded into Busabase by a trusted script — never a live monitoring integration, never a real gateway or real agents.
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。
Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.
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。
Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs. Identifies wallet clusters, tracks fund movement through mixers and exchanges, and supports law enforcement attribution. Activates for requests involving ransomware payment tracing, bitcoin wallet analysis, cryptocurrency forensics, or blockchain intelligence gathering.
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。
Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC 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。
Break a plan, spec, or PRD into independently-grabbable issues on the project issue tracker using tracer-bullet vertical slices.
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。
Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery 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。
Focused Signals scout for PostHog projects using logs. Watches for volume bursts, severity-distribution shifts, service silence, fresh message patterns, and trace-correlated bursts via the logs ingestion pipeline. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
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。
Focused Signals scout for PostHog projects using error tracking. Watches `$exception` bursts, stuck loops, multi-fingerprint clusters, status regressions, and stack-trace activity-name patterns. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
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。
Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.
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。
Watch running terminal processes for crashes and stack traces. When an error appears, navigate to the failing file and line, diagnose, and fix it automatically.
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。
Add PostHog error tracking to capture and monitor exceptions. Use after implementing features or reviewing PRs to ensure errors are tracked with stack traces and source maps. Also handles initial PostHog SDK setup if not yet installed.
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 up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).
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。
Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.
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。