「race」の検索結果

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for browser cookies, localStorage, sessionStorage, IndexedDB, Cache Storage, service workers, offline caches, and client-side session persistence. Use when the user asks to inspect browser state, replay cached auth or session behavior, explain why a page behaves differently after load, or trace how stored client state changes requests, rendering, or access. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Reviews a reverse-skill case package for scope readiness, Evidence to Finding to Path traceability, work item coverage, timeline references, and optional artifact hash integrity before report handoff.

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。

Deep Interactive courses. Plans the course so the learner manipulates something on most pages — simulations, diagrams they explore, code they run, games, 3D scenes — with slides reserved for the opening frame and the closing consolidation. Use when the topic has a mechanism, a variable to vary, a process to trace, or a structure to inspect.

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。

Master Go concurrency with goroutines, channels, sync primitives, and context. Use when building concurrent Go applications, implementing worker pools, or debugging race conditions.

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。

Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.

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 maintaining a durable Markdown knowledge store that agents compile from sources, when every number or quote in a wiki page must trace back to an immutable source, or when compiled pages need cheap drift detection against the code they describe. Teaches the raw/wiki/archive layout, per-claim provenance links, and git fingerprints for zero-token staleness 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。

Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.

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。

Structured messaging protocols for agent team communication including message type selection, plan approval, shutdown procedures, and anti-patterns to avoid. Use this skill when establishing communication norms for a newly spawned team, when deciding whether to send a direct message or a broadcast, when a team-lead needs to review and approve an implementer's plan before work begins, when orchestrating a graceful team shutdown after all tasks are complete, or when debugging why teammates are not coordinating correctly at integration points.

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 failing Power Automate cloud flows using the FlowStudio MCP server. The Graph API only shows top-level status codes. This skill gives your agent action-level inputs and outputs to find the actual root cause. Load this skill when asked to: debug a flow, investigate a failed run, why is this flow failing, inspect action outputs, find the root cause of a flow error, fix a broken Power Automate flow, diagnose a timeout, trace a DynamicOperationRequestFailure, check connector auth errors, read error details from a run, or troubleshoot expression failures. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app

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。

Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.

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。

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

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。

Enforce output quality, evidence verification, and quality gates across security audits. Use this skill when you perform SAST, SCA, threat modeling, or security code reviews. Use this skill to verify code locations and taint traces for all findings. Use this skill to prevent the suppression of valid security findings. Use this skill to evaluate reports against quality thresholds (score ≥ 8/10) before delivery.

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。

Cross-session environment retrospective driven by OMC's own instrumentation — trace timelines, friction reports, logs, and plan notepads. Every finding lands on the surface that owns the fix (deterministic check, steering volume, tooling, or information access); nothing is written without user approval.

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。

Test-first at pre-agreed seams — one test, one implementation, tracer-bullet red/green; independent expected values; substitutions only at the seam's boundary class; launch Phase 4 reference.

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 the current OMC session or repo state using logs, traces, state, and focused reproduction

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。

Root-cause a reconciliation break to its source transaction or posting — follow the audit trail from the break row back to the originating entry on each side and state what differs and why. Use after gl-recon has classified a break.

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。

Unified LLM torch-profiler triage skill for `sglang`, `vllm`, `TensorRT-LLM`, and `TokenSpeed`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server when supported and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.

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 before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against references/review-rules.md, traces call paths for dead code). Reports findings grouped by severity, flagging what to fix before submitting (blocking issues + dead code) vs what to leave for the actual review. Report-only — does not edit files.

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, identifying wallet clusters and tracking fund movement through mixers and exchanges to support law enforcement attribution. Use when tracing ransomware bitcoin payments, performing cryptocurrency wallet forensics, or gathering blockchain threat intelligence on extortion payments.

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 (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing. Use when triaging a suspicious or reported email, investigating a phishing incident, or verifying whether a message's sender domain was spoofed.

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。

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time 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。

Use when working with cognee's session memory or improve() — storing conversation turns, agent traces and feedback with session_id, bridging sessions into the permanent graph, reading an ImproveResult, understanding why an improve stage was skipped, already_completed or lock_held, or tuning the IMPROVE_* settings.

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。

When the user corrects a factual error, root-cause it immediately. Don't just note the correction — trace the error to its source, fix the source, and prevent recurrence. Every factual error is either a data error (bad brain page, bad memory file, bad rendered SOUL/USER identity, bad facts row) or a hallucination (LLM confabulated from partial signals).

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。

Trace one idea's evolution through the brain: first mention, best articulation, related concepts, reversals, contradictions, abandoned branches, and the current live version. Use for single-idea conceptual lineage, not broad concept-map synthesis or structured entity metrics.

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。