「chain」の検索結果

Analyze Chrome, Chromium, Electron, React DevTools, or Perfetto-compatible JSON traces and audit user-reported profiling findings without loading large artifacts into context; prove trigger-to-render/layout chains, separate measured facts from source inference, find exact code choke points, classify forced layout and render fanout, implement semantically safe fixes, and verify behavior plus repository budgets. Use for trace files, reported profiling durations or call chains, dropped frames, long tasks, resize or scroll jank, render storms, layout thrashing, selector hot paths, interaction latency, or requests to locate exact source-level bottlenecks.

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。

Diagnose and repair CCB ask/job/message/reply/artifact/callback lineage. Use for missing replies, incomplete artifacts, pending callbacks, retry/resubmit/ack decisions, reply delivery problems, or work-chain resume advice.

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。

Submit one node result to the assigned Reviewer and finish only after a bounded pass/rework chain.

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。

Read the user's local TradingView desktop app through opencli: quotes, options chains with greeks and per-strike IV, options expiries, screener results across stocks, crypto, forex, futures, and bonds, gainers and losers, news headlines and full stories, alerts (active, fired, offline fires), watchlists including colored flag lists, symbol search, chart state, and chart screenshots. Use this skill whenever the user asks for any of these from TradingView ("TV") — for example an options chain, puts, or IV skew for a ticker, an oversold-stock screen, their red flag list, which alerts fired, or a screenshot of their chart. For plain quotes, TA readouts, scans, or futures without the desktop app, prefer tradingview-mcp. Read-only: it never places trades or edits watchlists, alerts, or layouts.

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。

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

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 a target's TLS configuration — negotiated protocol version, cipher suite, certificate chain, expiry, and downgrade vectors. Use when: SOC2 auditor flagged your endpoint for "weak TLS" but you don't know which control failed (TSC CC6.7 transmission integrity vs CC6.6 encryption) or which cipher is the problem. Threshold: any negotiated TLSv1.0 or TLSv1.1, OR a cipher with RC4 / 3DES / null / EXPORT, OR a cert with under 30 days to expiry, OR a chain that fails hostname verification. Trigger with: "audit tls", "check ssl config", "weak tls", "analyze tls".

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。

Design a multi-chain holdings workflow on Alchemy Portfolio APIs with explicit pagination, partial-failure, privacy, and reconciliation contracts. Use when building wallet portfolio views. Trigger with "Alchemy portfolio", "multi-chain token balances", or "wallet holdings dashboard".

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。

Organization-grade cloud and supply-chain attack-surface discovery: S3/GCS/Azure Blob bucket discovery via observed-name mining (CNAME/cert-SAN/Wayback) and bounded two-class permutation (6 prefixes x 15 suffixes on trusted tokens, bounded target-bound expansion on subdomain stems), existence (HEAD/GET) vs public-listing confirmation, object-key triage into 9 value tiers (database dumps, credentials, IaC state, kubeconfig, VCS dirs, config, archives, PII, logs), dangling-CNAME bucket-takeover detection, and the ownership-gated severity model that stops an unattributable public bucket from becoming a false CRITICAL; the fully offline AWS-account-ID recovery from a leaked AKIA/ASIA/AROA access key (base32 decode, runnable stdlib Python, canonical test vector, AWS-documentation-example-ID screening); dependency-confusion confirmation for npm/PyPI (internal-signal classifier -- private-registry binding vs org-namespace match -- paired with a read-only public-registry 404 check and the npm scope-claimability nuance the public search API misses); and passive cloud-native/container/Kubernetes/CI control-plane fingerprinting (Lambda URLs, API Gateway, Cloud Run, App Service, kubelet/etcd/K8s API/dashboard, Jenkins/GitLab/Argo CD) as an org-attribution and exposure surface. Passive/discovery only -- no exploitation, no credential submission, no active control-plane confirmation (a stage-6 validate_cloud active tier is described but out of scope). Use when enumerating a target's cloud storage footprint, recovering an AWS account ID from a leaked key, confirming a supply-chain dependency-confusion vector, or fingerprinting cloud-native/K8s/CI infrastructure for an authorized external recon engagement.

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。

FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR loss-magnitude estimate (IBM/Ponemon per-record cost bands, cross-source record dedup, threat-factor annualization), attack-path amplification (curated red-team chain catalog + generic graph-walk engine, with a kill-chain vs shared-fate honesty gate), and a board-ready one-pager deliverable (hero $ + letter grade + top-3 findings + top attack path + the ask). Extends osint-methodology's severity rubric and client deliverable templates with quantification. Passive analysis only -- operates on findings already collected, no target traffic, no API keys. Use when asked to score risk, quantify exposure, estimate breach cost, build a board report, translate technical findings to dollars, or explain why a grade or dollar figure came out the way it did.

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。

Terminal security analysis for shell environments. This skill should be used when checking commands for supply-chain attacks before execution, scanning repositories for hidden content or config poisoning, scoring URLs for homograph attacks, setting up AI tool protection, inspecting Python package artifacts, governing untrusted tasks or Web3 commands, auditing browser extensions, creating provenance receipts, downloading and executing scripts safely, investigating why tirith blocked a command, managing trusted patterns, running security audits, configuring MCP gateway proxies, or working with threat intelligence databases. Also use when the user mentions "tirith", "pipe-to-shell", "homograph", "ANSI injection", "zero-width", "punycode", "terminal security", "shell hook", "cloaking detection", "supply chain attack", "bidi override", "invisible unicode", or "config poisoning". Even if the user does not explicitly name tirith, use this skill when they ask about protecting shell environments, intercepting dangerous commands, or hardening AI agent tool execution.

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。

Supply-chain testing via package-manager dependency confusion: when internal package names resolve to attacker-controlled public registries, leading to malicious install and script execution. Use for npm/pip/gem/Maven/Composer/Docker manifest review and authorized red-team supply-chain exercises.

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。

LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory

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。

LangChain ReAct agent implementation with tool binding for reasoning and action loops

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。

Chain-of-thought and step-by-step reasoning prompts for complex problem solving

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。

LangChain tool creation and integration utilities for agent systems

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。

LangChain retriever implementation with various retrieval strategies for RAG 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。

Mainland-Chinese TV-series craft, documents and production chain (国产剧创作体系与制片链) — the China layer parallel to the Japanese and Korean/French skills. Distilled from Yao Kougen 姚扣根《电视剧写作概论》(2016; series taxonomy, four Chinese script formats, the 3-minute/15-minute/episode-end suspense law, 36 situations), Zhang Wei 张巍《电视剧改编教程》(2014; five source types, sell-point migration, six case studies), Zhang Mingzhi & Song Peiyi《电视剧出品人与制片人教程》(2016; running-time rules, two-stage censorship, scene-breakdown sheet, contracts), Zhao Binbin《影视剧片段改编教程》(2016; film to stage fragment), plus official Chinese renderings of US TV terms from the translated Douglas and Blum textbooks. Use when writing or judging a 国产剧, turning a novel or IP into a 30–50 episode drama, writing a 策划书 / 分集梗概 / 人物小传, formatting a Chinese teleplay, sizing episode count and word count, checking content red lines and the 立项/备案/审查 chain, reading a Chinese screenwriter contract, or mapping US series vocabulary onto Chinese usage.

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Screenplay format, page craft, the writing process and adaptation (剧本格式/写作流程/改编) — merged from Wendy Henson (spec format rules), Richard Walter (page craft, revision), Neill Hicks (selling vs shooting script, draft chain), Syd Field (master shot, card method, adaptation as original work), Robert McKee (step outline → treatment → screenplay), Julian Hoxter (format elements, adaptation four questions), Diamond & Weissman (one-page nine-step) and Eric Bork — plus an exact typographic grid, element conventions (V.O./O.S., MORE/CONT'D, dual dialogue), a machine-writable output contract (Fountain, with forced markers for Chinese), and Asian industry formats (Chinese 场号制 影视剧本格式, Japanese 柱・ト書き・セリフ / 決定稿). Use when formatting a spec script, choosing the output file format, deciding what goes in action lines, planning drafts and outlines, cutting pages, adapting a novel, play or true story, writing a Chinese TV/film or Japanese-style script page, or preparing a submission-ready draft.

Software supply chain security reference for OpenSSF Scorecard, SLSA, Sigstore, SBOM, and posture/backlog taxonomies.

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。

Trigger: PRs over 400 lines, stacked PRs, review slices. Split oversized changes into chained PRs that protect review focus.

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。

INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.

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Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.

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Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.

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。

INVOKE THIS SKILL when using the langgraph CLI to scaffold, develop, build, or deploy LangGraph applications. Covers langgraph new, dev, build, up, deploy, and langgraph.json configuration.

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。