「orr」の検索結果

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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MISP (Malware Information Sharing Platform) is an open-source threat intelligence platform for gathering, sharing, storing, and correlating Indicators of Compromise (IOCs) of targeted attacks, threat

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Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache investigation.

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Run a live investigation as an operator-driven query loop: the operator runs each query on the live system and pastes the result back, and you guide the investigation one labelled query at a time. Use alongside support-assist (which covers what to investigate) whenever someone is pasting query or command output back from a deployment, especially during an incident. Covers labelling, pacing, making each query decide the next step, version-grounding, and keeping queries cheap and correctly classified.

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 a support docs pack — dedup, length budgets, and no splintering — and land changes as a reviewed pull request. Use when a support thread, or a hand-off from Support assist, surfaces a new resolution, a correction to an existing one, or a deployment quirk worth recording. Not for ordinary code changes.

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。

Formally verify programs by writing Lean 4 proofs. Trigger this skill whenever the user wants to formally verify code, generate Lean 4 proofs, prove properties about algorithms or smart contracts, verify invariants, convert program logic into formal specifications, or anything involving Lean 4 and formal verification. Also trigger when the user mentions "qedgen", "lean proof", "formal proof", "verify my code", "prove correctness", "formal verification", or wants mathematical guarantees about their implementation.

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Track and correlate events across multiple timezones with automatic conversion. Use when analyzing distributed system incidents, coordinating cross-regional operations, or creating time-normalized reports from logs/events spanning PST/CST/EST/JST or other timezones. Handles daylight saving transitions 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。

Analyze network device logs to identify connectivity issues, latency problems, and outages. Correlate timestamps across timezones, detect anomaly patterns, and generate incident reports with root cause hypotheses. Use when troubleshooting network incidents from log 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。

Generate QR code images from text, URLs, or contact information using Python's qrcode library. Supports customization (size, margin, color, error correction) and batch generation.

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Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

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Use when evaluating a completed or in-progress project across scope, correctness, quality, testing, and learning outcomes using structured Praise-Critique-Grow feedback.

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Orchestrates multi-step tasks autonomously using a 3-tier agent hierarchy (Director/Manager/Worker). Use when user says "run this end-to-end", "handle everything", "execute all tasks", or needs parallel task execution with queuing, course correction, and session resume. Provides FIFO task queue, escalation policy, git worktree isolation, and context persistence across agent sessions.

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。

Generate a runnable exploit-validation command (Nuclei template, Metasploit module hint, AI-assisted Python script, or curl-based PoC) against a user-specified authorised target. Use when validating that a fix actually closed the vulnerability path, confirming a patch deployed correctly, or producing a copy-pasteable test command for QA. The skill never executes — the user 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。

A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity, then corrects the code according to the language spec, but through kids eyes, maintaining simplicity and readability.

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A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity. Output is a 1-2 line summary of the request as a whole: how is the file? Is the class nice? Is the function correct?

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 pi (coding-agent) session JSONL and ego-benchmark-harness run artifacts to explain slow or failed individual tasks, compare execution-path differences between agents or browser tools, and count tool calls and error types. Use this skill whenever users ask to analyze a pi session, explain why a task was slow or failed, compare two runs, investigate an error, locate the session corresponding to tasks.jsonl, inspect an agent execution path, count tool calls, analyze a run ID, or perform any forensic or retrospective analysis of JSONL under ~/.pi/agent/sessions/ or agent-home/.pi/agent/sessions/.

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Use when adding a test to an existing codebase for behavior that is already implemented. Ensures the test is meaningful by mutation-verifying it — breaking the production code to confirm the test fails for the expected reason. Triggers when the user says "add a test for existing behavior", "backfill test coverage", "the test passes on first run", or any scenario where a test is written without a corresponding new production change.

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Turn 'should never happen' into 'cannot happen' by defining owned inductive invariants and enforcing them at parse/construct/API/DB/lock/txn boundaries with a verification signal. Use when prompts mention invariants, impossible states, validation sprawl, cache/index drift, idempotency/versioning, retries/duplicates/out-of-order events, race/linearization bugs, loop correctness, or hardening another workflow (for example /tw-fix) with invariant checks first.

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Compile deep repository evidence into artifact-bound correctness doctrine — authority/law/proof maps, owned invariants, failure archaeology, and the strongest durable destination for each piece of knowledge, plus an optional minimal repository-specific skill portfolio. Use when the user wants both deep codebase understanding AND durable doctrine, knowledge routing, or repo-specific skill recommendations. Research discoverable facts before asking. Read-only. Not for quick onboarding, one isolated invariant, ordinary implementation, generic review, or direct skill creation.

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Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.

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。

Load for Senior CTO critique, architectural audit, risk review, sequencing correction, or refinement of an existing implementation-plan triad; edits the requested plan/context/tasks in place without creating a worktree or review file. Not for open-ended CTO advice, product implementation, or implementation workspace setup.

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Load for .NET observability changes involving structured logs/Loki, Prometheus metrics, OpenTelemetry traces, exception handling, correlation IDs, dashboards, alerts, or production error diagnosis; not for ordinary business validation errors.

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Load when asked to review a local diff/change set for correctness, regressions, blast radius, affected flows, and missing tests using the knowledge graph; use `review-pr` for intent/checklist/merge-gate evidence.

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。

Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"

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。