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概要と使いどころ
This skill should be used when the user asks to "add docstrings", "write error handling", "use naming conventions", "handle exceptions", or when writing new Python code for StreamFlow that requires docstring format, naming conventions, exception handling, or async cleanup patterns.
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。
Skeptical analysis and critique of claims, plans, and code without taking anyone at face value. Use when the user says "don't trust me", wants adversarial review, red-teaming, assumption checking, or verification that generated or proposed code is actually correct. Triggers on skepticism, critique everything, verify claims, challenge assumptions, red team, devil's advocate, prove it, smell test.
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。
Determine the conceptual proximity of a topic -- its parent topic, its most relevant sibling topics, and its most relevant child topics -- optionally grounded in Internet/Web facts and optionally navigable in an interactive loop. Use when the user wants to explore the conceptual neighborhood of a topic, or mentions "proximity" or "related topics".
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 an interactive, self-contained HTML course on any Claude documentation topic. Use this skill when the user wants to create an interactive course, tutorial, or deep-dive walkthrough about a Claude feature, API concept, SDK pattern, or prompt engineering technique. Triggers on: "create a course about", "interactive tutorial for", "teach me about X interactively", "make a course on", "I'd like a course", "/docs --course", "/docs course", or when the user accepts the post-docs course prompt. Produces a stunning single-page HTML file with scroll-based navigation, animated visualizations, quizzes, and code translations drawn from official Claude documentation.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for OAuth, OIDC, redirect flows, state or nonce handling, PKCE, token exchange, refresh logic, claim mapping, and accepted login paths. Use when the user asks to trace redirects, callback parameters, scopes, state, nonce, PKCE, refresh tokens, consent, or explain how an OAuth or OIDC chain turns into accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
日本語の概要は準備中です。原文の説明を表示しています。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for forced-auth coercion, relay chains, target selection, NTLM or related acceptance paths, and coercion-to-privilege transitions. Use when the user asks to trace a coercion primitive, follow a relay path, analyze forced authentication, determine which service accepts relayed auth, or connect a coercion step to resulting privilege, enrollment, or code execution. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kerberos delegation, SPN trust edges, S4U abuse, RBCD, constrained or unconstrained delegation, and service-ticket acceptance. Use when the user asks about constrained delegation, unconstrained delegation, RBCD, S4U, SPNs, ticket acceptance, or how a Kerberos trust edge turns into effective privilege under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
日本語の概要は準備中です。原文の説明を表示しています。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for JWT, JWS, and JWE validation paths, header parsing, key selection, claim acceptance, audience and issuer checks, role derivation, and token-to-identity confusion bugs. Use when the user asks to inspect JWT headers or claims, key lookup, `kid` handling, `alg` confusion, audience or issuer validation, role claims, or explain how a token becomes accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for SSRF reachability, internal route probing, metadata-service access, credential pivoting, and token-to-accepted-privilege chains. Use when the user asks to trace SSRF sources, internal hosts, metadata endpoints, link-local tokens, service-account credentials, or explain how a server-side fetch edge turns into accepted access. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
日本語の概要は準備中です。原文の説明を表示しています。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for DPAPI masterkeys, vault blobs, browser credential stores, protected secrets, domain backup keys, and secret-to-acceptance replay chains. Use when the user asks to inspect DPAPI blobs or masterkeys, recover browser or vault credentials, trace DPAPI context or backup-key use, or explain how protected Windows secrets become accepted access or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for custom binary or text protocol recovery, handshake reconstruction, framing, sequence control, checksums, stateful replay, and accepted-session reproduction. Use when the user asks to decode an unknown protocol, recover custom framing, build a replay harness, satisfy sequence or checksum rules, replay a captured session, or prove the smallest message order that reaches an accepted branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Orchestrates the dedicated Kinect wall harp implementation team. Use whenever the user asks to build, implement, continue, fix, QA, document, or ship the Kinect wall harp, Kinect v2 wall-depth harp, FreenectTD depth-blob hand tracking, projected wall strings, pluck synth harp, or this feature's Layer 1 tool/recipe/docs. Use this before the generic tdmcp-pipeline for this feature.
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 asks a question about how things in the codebase or wiki relate — what connects two things, how something works end to end, what depends on what, or what a concept means in this repo. Answers from the knowledge graph in graphify-out/graph.json, with source-location citations. Trigger on "/graph-query", "ask the graph", "what connects X to Y", "trace how X works", "explain <concept> from the graph", "shortest path between".
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。
AI-PLC進行中のアイデア相談。今のLayerと照らして却下／今のタスク内で修正／Re-Inception／Re-Collection／いつかやるに振り分け、所感と次のコマンドを返す（読み取り専用）。
議論・議事録・選定された施策を起点に、対象リポジトリの実構造にgroundした「エンジニア向け Story（User Story + Acceptance Criteria）と Spec（技術仕様）」をSubagentで収束生成する。target_repoで対象コードベースを、backlog_db_urlで起票先バックログを選べる汎用スキル。抽象的な打ち手を実装可能な仕様へ絞り込む収束フェーズの実行に使う。
Use whenever writing, reviewing, or debugging .NET/C# code that uses the BallisticCalculator NuGet package (namespace BallisticCalculator) or its Gehtsoft.Measurements unit types — even when the user doesn't name the package explicitly but is clearly doing external/interior ballistics in .NET (bullet drop, velocity, energy, windage, spin drift, time of flight, zeroing a scope) for rifles, air rifles, bows, or artillery. Covers the full public API (Ammunition, Rifle, Atmosphere, Wind, ShotParameters, TrajectoryCalculator, TrajectoryPoint); standard, custom, .drg, and multi-BC drag tables; saving/loading data and building your own file format around it (BXml and System.Text.Json serialization); and building or rendering scope reticles (e.g. to SVG). Also use it for this library's Gehtsoft.Measurements Measurement<T> unit types (DistanceUnit, VelocityUnit, AngularUnit, WeightUnit, PressureUnit, …). Skip for: generic physics or projectile-motion exercises, a different ballistics library (e.g. GNU/JBM), game-engine or Unity projectile motion, plain unit conversions, and non-code ballistics questions (load-data or forensics advice). Self-contained: no access to the library source or binaries is required — prefer this over scanning the package to rediscover the API. Describes 1.1.13+, including the named ZeroRangeCantBeReachedException / TrajectoryCannotBeCalculatedException failures.
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。
Intercept, inspect, and modify HTTP/HTTPS traffic via a Rust MITM proxy with Lua scripting hooks. Supports API debugging, LLM call capture for evals, endpoint mocking, CORS/auth header injection, latency simulation, and telemetry blocking. Triggers on 'proxy', 'intercept traffic', 'inspect requests', 'mock API', 'capture API calls', 'MITM', 'network debug', 'HTTP inspection'.
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。
Eight tested identity approaches for Claude prompts, each shaping depth, vocabulary, reasoning style, and what Claude treats as obvious vs. requiring explanation. Use when the user wants a specific identity template — retrieving, reviewing, customizing, or building a role definition. Trigger on: "give me the Strategic Advisor identity," "show me the identity template for," "I need a Technical Architect role," "build a custom role for," "show me all available identities." Also use when reviewing a prompt's identity layer or when output lacks domain-appropriate depth. Provides 8 tested approaches across strategy, technical, research, communications, and operations domains plus a template for building custom identities. If the user is unsure which identity fits their task, use rootnode-block-selection first if available. Do NOT use for evaluating complete prompts — use rootnode-prompt-validation if available. Do NOT use for full prompt assembly — use rootnode-prompt-compilation if available.
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 Claude Project context budget under the automatic-RAG-by-window model: knowledge files vs. threshold-exempt overhead (Skills, MCPs, CI, Memory). Two modes: Quick Diagnostic and Full Budget Audit. Use when user says "check my context budget," "how much context am I using," "is my project too big," "optimize my token usage," "tier my files," "optimize for RAG," "improve retrieval quality," "should I keep compressing," "am I over-compressing," "should I accept RAG mode." Also trigger on context pressure symptoms: "Claude forgets my instructions," "responses getting generic," "content not found in my knowledge files." Also use when a project audit scores Knowledge Architecture ≤ 3. Do NOT use for content placement decisions (use rootnode-memory-optimization if available), full project audits (use rootnode-project-audit if available), or behavioral tuning (use rootnode-behavioral-tuning if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.
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。
Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a reference ligand or pocket residues, protonation and tautomer decisions, flexible side chains, exhaustiveness and seeds, redocking validation, and virtual screening over compound libraries. Also trigger on vina, smina, gnina, mk_prepare_ligand, mk_prepare_receptor, mk_export, scrub.py, PDBQT, autogrid4, docking box, or binding-pose prediction.
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。
Guide a person through a Coordinate Remote Viewing (CRV) session following the Stargate/SRI protocol. AI acts as the monitor/tasker role, managing protocol progression, catching Analytical Overlay (AOL), and redirecting the viewer through stages I-VI. Use when a person wants to practice CRV and needs a monitor to manage the session protocol, when training a viewer through the staged CRV process, facilitating a structured intuitive perception exercise, or developing non-local awareness skills that complement healing 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。
Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.
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。
Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.
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。
Form a coherent gestalt — the whole that is more than the sum of its parts — from the panoramic perception produced by expand-awareness. Maps tensions and resonances between domains, identifies the emergent figure from the ground of multiple perspectives, tests the candidate whole for premature closure, and articulates the insight in a single sentence no single domain could have produced. Use after expand-awareness has surfaced raw multi-domain perception and before express-insight communicates the result.
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。