Use when a redacted, trimmed agent transcript must be appended to a GitHub PR or issue body, with human approval and preview. Not for automated or model-initiated insertion.
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。
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概要と使いどころ
Use when a redacted, trimmed agent transcript must be appended to a GitHub PR or issue body, with human approval and preview. Not for automated or model-initiated insertion.
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 filing an approved task spec as a GitHub issue with a local archive and optional worktree implementer. Don't use for spec drafting without human interrogation and redaction gates.
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 the user explicitly asks for the Inngest REST API v2, raw HTTP, OpenAPI, API docs, API authentication, or an endpoint that the Inngest CLI does not expose. Covers api-docs.inngest.com, llms.txt, the OpenAPI v2 spec, Bearer authentication with API keys or signing keys, production and local base URLs, raw curl/fetch requests, request-shape discovery, pagination, secret redaction, and when to prefer the `inngest-api-cli` skill 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。
Generate production-ready business documents including freelance contracts, project proposals, SOWs, NDAs, and MSAs with jurisdiction-aware clauses. Covers US (Delaware), EU (GDPR), UK, and DACH (German law) legal frameworks. Includes contract templates, clause libraries, and DOCX conversion. Use when starting client engagements, writing proposals, drafting partnership agreements, or needing GDPR-compliant data processing addenda.
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。
Mosaic environment-sensitive areas (tenant name, user avatar, subscription name / ID, resource group, internal hostnames) on Azure Portal screenshots before publishing to blogs, Qiita, Medium, slides, or demos. Use when sanitizing screenshots, masking env info, or preparing portal captures for public sharing. Keywords: Azure Portal, screenshot, mask, mosaic, sanitize, tenant, subscription, public publishing, blur, redact.
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。
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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。
Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.
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。
Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.
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 and exploit misconfigured Linux Capabilities. This skill covers how attackers escalate privileges to root without relying on SUID binaries or kernel exploits by abusing excessive capabilities like cap_dac_read_search, cap_sys_ptrace, or cap_setuid assigned to ordinary 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。
Active Directory ACL abuse playbook. Use when exploiting misconfigured AD permissions including GenericAll, WriteDACL, DCSync rights, shadow credentials, LAPS reading, GPO abuse, and BloodHound-guided attack paths.
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 cultural adaptation of translated blog posts. Run after blog-translate completes. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "lokalisieren", "localiser", "adaptar".
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 choosing between Salesforce record-access mechanisms or reviewing the access model end to end. Trigger keywords: OWD, org-wide defaults, sharingModel, externalSharingModel, record access model, sharing architecture, which sharing mechanism, UserRecordAccess, __Share, RowCause, why can user see too much. NOT for sharing rules - use admin/sharing-rules. NOT for role hierarchy - use admin/role-hierarchy-design.
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。
Scan and redact PII and sensitive data (emails, phone numbers, SSNs, API keys, IP addresses, credentials, amounts, company/person names) from repository files. Includes a pre-commit hook to block commits containing PII. Use when asked to audit code for sensitive data, sanitize files before publishing, or install PII detection hooks. For application security hardening see security-engineering.
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。
Redacta y crea un issue de GitHub rellenando la plantilla adecuada de .github/ISSUE_TEMPLATE/ según el tipo (bug, funcionalidad o tarea). Úsalo cuando la persona pida abrir/crear un issue, reportar un bug o registrar una tarea/petición (p. ej. "abre un issue por este bug", "crea un issue para la nueva funcionalidad", "registra esta tarea"). No cierra ni comenta issues existentes.
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。
Coordinates staged or parallel jobs across live-verified coding-agent runtimes (Claude Code, Codex, Cursor, and other installed CLIs) and in-session toolkit subagents. Routes each job by capability and risk tier, isolates parallel writers in git worktrees, captures redacted output, resumes interrupted runs, and blocks completion until an independent arbiter verifies the results. Use when work should be split across multiple runtimes or subagents, run as staged or parallel jobs, and reviewed before handoff. NOT for a single-agent task or the in-session 7-phase flow (use mk:cook / mk:workflow-orchestrator).
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。
Normalizes ChIP-seq data using exogenous spike-in (ChIP-Rx with Drosophila chromatin per Orlando 2014 / Egan 2016; E. coli carryover for CUT&RUN/CUT&Tag). Distinguishes RRPM from Rx-Input scaling, integrates with DiffBind / DESeq2 / edgeR / csaw via sizeFactors and DiffBind library-size vectors, applies the Patel et al 2024 *Nat Biotechnol* failure-mode framework, and validates that normalization is applied at the read level (not peak counts). Use when global signal shifts are expected (HDACi, BETi, EZH2i, dosage, target knockdown), when ChIPseqSpikeInFree detects post-hoc shifts, or when validating internal-control regions before publication.
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。
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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。
Assess whether a hospitalized adult with diabetes on insulin pump therapy remains a candidate for continued inpatient pump use. Consider triggers such as the clinician asking "Is this patient appropriate to continue insulin pump in hospital?" and evaluate for impaired consciousness, inability to adjust pump settings, critical illness, DKA, or hyperosmolar hyperglycemic state.
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。
Decide whether commonly-excluded patients (eating disorders, elderly ≥75, upper GI disease) can actually receive a GLP-1 receptor agonist. Use when a clinician asks "can I give semaglutide to a patient with binge eating disorder / anorexia / bulimia", "is liraglutide safe in an 80-year-old", "GLP-1 with GERD or chronic gastritis", "GLP-1 in sarcopenic elderly", or anytime a patient is being ruled out for GLP-1 therapy based on a perceived contraindication. Grounded in Gorgojo-Martínez 2023 §6 (Myths or Reality).
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。
Performs a structured pre‑prescription evaluation of mental/psychological status, prior diabetes self‑care adherence, willingness/interest in CSII, and availability for follow‑up visits. Triggers include: 'Evaluating pump candidacy', 'Pre‑pump checklist: psych, adherence, motivation, follow-up'.
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。
Determines when adrenal venous sampling (AVS) is indicated for functional subtyping of primary aldosteronism (PA) when surgical treatment is feasible and desired by the patient. Triggered when a clinician confirms PA diagnosis and asks 'Do I need AVS for subtyping?' or evaluates surgical candidacy.
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。
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
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。
Expert-level climate engineering covering carbon capture and storage, direct air capture, solar geoengineering, enhanced weathering, and carbon accounting.
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 writing, debugging, or configuring an omp hook or extension — blocking or rewriting tool calls, redacting tool output, reshaping LLM context, registering tools/commands, or wiring modules through config.yml. Covers ExtensionAPI (current) vs HookAPI (legacy), the event catalog, discovery and load order, and disable controls. Not for Claude Code hooks (~/.claude/settings.json) or the shared harness safety guard (~/.agents/harness/hooks/).
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。