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概要と使いどころ
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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Conducts comprehensive network penetration tests against authorized target environments by performing host discovery, port scanning, service enumeration, vulnerability identification, and controlled exploitation to assess the security posture of network infrastructure. The tester follows PTES methodology from reconnaissance through post-exploitation and reporting. Activates for requests involving network pentest, infrastructure security assessment, internal network testing, or external perimeter testing.
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Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.
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Detect typosquatting, homograph phishing, and brand impersonation domains using dnstwist to generate domain permutations and identify registered lookalike domains targeting your organization.
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Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
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。
Mobile layout skill for antislop. Use for layouts that reflow on small screens: grids, overflow, tap targets. Load with the core.
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 malicious Linux ELF (Executable and Linkable Format) binaries including botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure. Covers static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples. Activates for requests involving Linux malware analysis, ELF binary investigation, Linux server compromise assessment, or container malware analysis.
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Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.
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Convert LaTeX papers between journal/conference templates for journal resubmission or template switching (e.g., Springer → MDPI, IEEE → Nature, IPOL → CVPR) through a privacy-preserving anonymized workflow. Use when the user provides a LaTeX .tex/project plus a target template directory or .tex file. Assume the input is not anonymized unless the user explicitly says it is already anonymized. Preserve privacy sentinels exactly; never open mapping.private.json except as a local script argument.
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Create or update project governing principles and development guidelines. Use at project start to establish code quality, testing standards, and architectural constraints that guide all development.
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Create or update feature specifications from natural language descriptions. Use when starting new features or refining requirements. Generates spec.md with user stories, functional requirements, and acceptance criteria following spec-driven development methodology.
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Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
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Check upgrade safety for a Tamanu upgrade that spans many versions: verify that every intermediate hotfix is included in the target version, and surface the new configuration/settings, data migrations, and FHIR rematerialisation impact the upgrade brings. Use when the user wants to check an upgrade from one Tamanu version to another (e.g. "check hotfixes from v2.31 to v2.47"), confirm no intermediate hotfix was dropped, or understand what config, migrations, and FHIR rework a multi-version jump requires. Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for cutting a release (see release-cutoff-checks) or deciding which tests to run (see scope-tamanu-release-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。
Start voice calls via the SolanaOS voice-call plugin.
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Build and release pipeline for the Pump SDK — tsup TypeScript builds, Cargo release profiles, semantic release with commitizen, npm publishing, linting, Makefile targets, Vercel deployment, and MCP server distribution.
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Full dev cycle. Planning tier for clarify and plan; worker tier after approval for autonomous execution, tiered lint, thread rotation, and feedback fixes. Start every change here.
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Visual expression system for AI personas with emotion-based face switching. Use at session start to display the expression UI, and switch expressions whenever the emotional state changes during conversation.
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テスト実行時の絞り込み運用ルール。プロジェクト全体テストではなく修正範囲に絞ったテスト実行を行う。`gradlew test`/`pytest`/`jest`/`go test`等のテストランナーを実行するとき、テスト実行コマンドを組み立てるときに必ず参照する。コミット差分や修正ファイルから対象テストを特定する手順を含む。
Android device offensive security, forensic extraction, and mobile app exploitation. Use this whenever the user needs to: break into or bypass the lock screen of an Android phone, extract data from a seized Android device for evidence, analyze or reverse-engineer an APK to find vulnerabilities, intercept communications on an Android device, exploit Android apps via deeplinks, steal crypto wallets or app data, set up rogue WiFi to target a phone, or plan any mobile device investigation or penetration test. Also trigger when the user mentions Android forensics, mobile pentesting, law enforcement phone extraction, device exploitation, WhatsApp or Telegram extraction from a phone, Android app reversing, CVE research for Android, or needing a chain of custody for mobile evidence. Even if the user describes the scenario casually or in another language, if they need to gain access to or pull data from an Android phone, use this skill.
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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.
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Find the canonical Rosetta Stone attribute that best matches a fuzzy description, semantic phrase, or required schema shape. Searches the catalog with pagination, describes the shortlist in one batched call, ranks candidates by name + shape match, and returns the canonical attribute ID plus close alternatives. Use when: "find the X attribute", "what's the graph-edge attribute ID", "look up the email Rosetta Stone attribute", "search the attribute catalog for Y", "which attribute has SOURCE_ID + TARGET_ID + IS_DIRECTED". (narrative-common)
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Verify a tested web agent's real behavior from its recorded Pi or Codex session and selected screenshots before scoring. The judge prompt provides absolute artifact paths; use targeted queries to compare actual outputs, errors, and the final answer with the recorded behavior, then return only the verdict JSON. Only for ego-bench judging—never to drive a browser or rerun the agent.
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Найти прошлое решение по теме в выбранных владельцем файлах.
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。
Сверить полученный результат с исходными данными и требованием владельца.
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。