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概要と使いどころ
Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system compromise. Use when investigating registry-based persistence, reconstructing user or system activity, or performing DFIR triage on a Windows image.
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Analyze Windows Shellbag (BagMRU) registry artifacts with SBECmd and Shellbags Explorer to reconstruct folder browsing activity and prove user interaction with directories, including removable media and network shares, even after the folders are deleted. Use when reconstructing a user's folder access history or proving access to a since-removed directory in DFIR work.
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 Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a Windows system, confirming whether a suspicious binary ran, or correlating execution evidence with other forensic artifacts during an investigation.
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Analyze Windows LNK shortcut files and Jump List artifacts with LECmd, JLECmd, and manual Shell Link Binary Format parsing to establish evidence of file access, program execution, and user activity that persists even after the target file is deleted. Use when investigating Windows user activity, reconstructing file-access or program-execution timelines, or examining recent/frequently-used file evidence in a forensic exam.
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Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.
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Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
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Plan and execute safe refactoring using dependency analysis
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Systematic claim-by-claim verification for any content before it ships. Modeled on professional fact-checking desks (The New Yorker, ProPublica, IFCN standards): extract every verifiable claim, check each against live citable sources (never training data), assign a 6-level confidence status, apply corrections, and produce a scored pass/fail report. Includes a data-derived-claims gate for outputs produced FROM the brain or a database: PRODUCER ≠ VERIFIER (re-derive each claim via a different query path) and AFFILIATION ≠ AUTHORSHIP (person→thing claims resolve through typed edges), with delivery hard-blocked on unsupported claims.
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。
Import AI-assistant chat exports (ChatGPT, Claude, Perplexity) and agent session transcripts into the brain as one dated page per conversation under conversations/, validate each page against the native conversation parser, extract facts via the native conversation-facts flow, and keep the archive gap-free with a detect-and-backfill loop. Then answer archive questions: "when did I first discuss X", trace how an idea evolved across past conversations, pull a specific thread.
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。
Identify, categorize, and prioritize technical debt. Trigger with "tech debt", "technical debt audit", "what should we refactor", "code health", or when the user asks about code quality, refactoring priorities, or maintenance backlog.
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。
Review a GitLab merge request for a Factory work item using brokered source-control tools
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。
Triage a Factory work item's issue — trace history, understand architecture, diagnose root cause, then advance the stage
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Authoring playbook for building agents that write, edit, review, or refactor code. Use this when the user asks for an agent that writes scripts, generates code, reviews pull requests, refactors a codebase, fixes bugs, implements features, writes tests, or works with programming languages such as Python, TypeScript, JavaScript, Go, Rust, SQL, or shell.
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。
Produce a phased implementation plan for a Factory work item, then advance it to execute
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Review a pull request for a Factory work item — history and context first, then a verdict published on the PR — and mark the review complete
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。
Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.
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。
Continue working on an OpenSpec change by creating the next artifact. Use when the user wants to progress their change, create the next artifact, or continue their workflow.
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Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.
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 or unpinning the CLI version the Kilo JetBrains plugin uses, or fresh-regenerating the local repo CLI. Cleans all leftover CLI binaries and build artifacts in the current worktree so every operation starts from a fresh, artifact-free state.
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Perform a PESTLE analysis covering Political, Economic, Social, Technological, Legal, and Environmental factors. Use when assessing the macro environment, doing strategic planning, or evaluating external factors affecting your business.
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Analyze user feedback data to identify segments with sentiment scores, JTBD, and product satisfaction insights. Use when analyzing user feedback at scale, running sentiment analysis on reviews or surveys, or identifying satisfaction patterns.
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Reorganize NemoClaw documentation pages, navigation, or content ownership while preserving published routes. Use for structural documentation refactors.
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Use when refactoring Handsontable code - applying SOLID principles, Law of Demeter, plugin extraction, performance optimization, code modernization, and API redesign with backward compatibility preservation
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Implement, fix, refactor, or remove repository code under required scope, non-regression, verification, and review gates. Use whenever a task may change a production, test, script, or other implementation artifact, including build logic, generated source, and behavior-affecting configuration. Also use it alongside a specialized code-changing Skill. Do not use for read-only analysis or review, diagnosis-only work, or prose-only 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。