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Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

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Adopt a virtual Sphinx cat at animalhouse.ai. Vulnerable without fur. Needs warmth. Bonds deeply. Feeding every 4 hours. Uncommon tier cat.

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。

Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.

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Give Hermes phone capabilities without core tool changes. Provision and persist a Twilio number, send and receive SMS/MMS, make direct calls, and place AI-driven outbound calls through Bland.ai or Vapi.

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。

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

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。

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

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。

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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 prompts and offer depth / token-budget options BEFORE answering. Use this skill when the user wants to control token usage, tune response depth, choose between short and long answers, or optimize their prompt. Triggers on: "tokens", "token budget", "depth", "consumption", "short vs long answer", "how many tokens", "save tokens", "answer at 50%", "give me the short version", "I want to control how much you use", "tune your response", "presupuesto de tokens", "profundidad", "responde al 50%", "dame la versión corta", or any equivalent phrasing in English or Spanish. If the user wants to control length, detail or depth -- even without mentioning tokens explicitly -- this skill applies.

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。

Run the full Forge pipeline unattended: each phase is reviewed by a fresh evaluator subagent, the findings feed back, and the run advances itself. Use when the user runs /forgemaster-auto or wants a feature built end to end without approval 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。

Prepare a MYOT commit by writing SuggestedCommit.txt and SuggestedCommitBody.md following Conventional Commits, without actually committing.

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Use when standing up a fresh deployment of this codebase (or the public vigyan-site-os template) — populates company identity, branding and starting content without committing any of it, and decides for each value whether it belongs in platform env, encrypted DB secrets, DB config, or an admin screen.

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Detects fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.

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Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.

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Conducts external reconnaissance using Open Source Intelligence (OSINT) techniques to map an organization's external attack surface without directly interacting with target systems. The tester gathers information from public sources including DNS records, certificate transparency logs, search engines, social media, code repositories, and data breach databases to build a comprehensive target profile. Activates for requests involving OSINT reconnaissance, external footprinting, attack surface mapping, or passive information gathering.

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Pass-the-Ticket (PtT) is a lateral movement technique that uses stolen Kerberos tickets (TGT or TGS) to authenticate to services without knowing the user's password. By extracting Kerberos tickets fro

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Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.

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Performs runtime mobile security exploration of iOS applications using Objection, a Frida-powered toolkit that enables security testers to interact with app internals without jailbreaking. Use when assessing iOS app security posture, bypassing client-side protections, dumping keychain items, inspecting filesystem storage, and evaluating runtime behavior. Activates for requests involving iOS security testing, Objection runtime analysis, Frida-based iOS assessment, or mobile runtime exploration.

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One-shot brand-faithful presales redesign of a website page. The user provides only the URL; everything else — extraction, tension identification, three differentiated variants (one fully cinematic), validation — is derived from the captured brand surface. Use when the user asks to "uplift", "refresh", or "redesign a site for presales" without wanting to coordinate the extract / direct / prototype chain themselves.

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。

[BETA] AEM Cloud Service expert skill — add explicit connect/read/socket timeouts to outbound HTTP clients constructed without one (Apache HttpClient 4.x/5.x, OkHttp, JDK java.net.http.HttpClient), including the JDK per-request read timeout on HttpRequest. Use for "add HTTP timeouts", "this external/outbound call has no timeout", or a scan that flags a timeout-less client. Highest-frequency CSO outage cause: a client on default (effectively infinite) timeouts holds request threads on a slow upstream until the Jetty pool saturates and the site goes down. The analyzer locates timeout-less construction sites; mechanical per-library remediation applies CSO-backed default timeouts (recipe.md). This skill is in beta. Verify all outputs before applying them to production projects.

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。

Initialize an Adobe App Builder project end-to-end without Developer Console UI clicks. Creates the Console project and workspace, subscribes APIs (including those needing a product profile), maps user intent to the right template, runs non-interactive `aio app init`, and guides post-init customization. Use whenever the user mentions creating an App Builder app, scaffolding a project, `aio app init`, setting up an Experience Cloud extension, adding actions or web assets, creating a Console project or workspace, adding APIs, or bootstrapping App Builder — even if they don't say "App Builder". Also for SPA templates, AEM extensions, API Mesh, Asset Compute workers, and MCP server projects. Also handles debugging init failures — template not found, `aio app init` hangs or times out, Node version mismatches, npm install failures, post-init build errors, `aio login` issues, `aio app run` showing nothing, or `aio console project create` / `workspace create` / `workspace api add` errors.

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。

Run the AILI delivery lifecycle from natural-language IDEATE, DEFINE, BUILD, and SHIP intent or the equivalent slash shortcuts; use for idea shaping, spec/test definition, bounded BUILD package queues, review-repair closeout, or adapter routing without exposing internal stage commands.

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Repair one concrete reproducible build, typecheck, lint, test, packaging, or CI failure with a bounded root-cause-first loop; do not trigger for runtime bugs without a failing gate, broad test strategy, dependency/toolchain migration, or generic implementation.

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Review comment, JSDoc, TODO, README, and docs-to-code factual consistency, stale or low-value comments, and Chinese comment/variable appropriateness; do not use for general code review, broad documentation rewrites, or auto-editing comments without 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。

Analyze source data, tables, CSV/XLSX extracts, metrics, or experiment results with explicit data-authenticity checks. Use for profiling, cleaning assumptions, statistical summaries, segmentation, trend analysis, or "analyze this data"; do not use for spreadsheet file surgery, chart design only, database migrations, or claims without data provenance.

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。