「skill discovery」の検索結果

Detect unauthorized SaaS and cloud service usage (shadow IT) by parsing proxy access logs, DNS query logs, and firewall/netflow data with Python pandas to aggregate traffic by domain, classify domains against known SaaS categories, and score risk by data volume and user count. Use when auditing an organization for unsanctioned cloud/SaaS usage or generating a shadow IT discovery report with remediation recommendations.

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Monitors Certificate Transparency (CT) logs to detect unauthorized certificate issuance, discover subdomains via CT data, and alert on suspicious certificate activity for owned domains. Uses the crt.sh API and direct CT log querying based on RFC 6962 to build continuous monitoring pipelines that catch rogue certificates, track CA behavior, and map the external attack surface. Activates for requests involving certificate transparency monitoring, CT log auditing, subdomain discovery via certificates, or certificate issuance alerting.

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Feed and whole-publication ingestion: turn an entire blog, newsletter, or RSS/Atom archive into brain source pages. Covers feed discovery, pagination walking, normalization to a common article shape, canonical-URL dedup, idempotent re-runs, 429 pacing, and empty-husk repair. This is the PUBLICATION-scope skill — a single article URL routes to idea-ingest instead. Per-article enrichment hands off to the brain-ingest-gate skill; public posts only (gated content is skipped, never worked around).

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Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.

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Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.

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Brainstorm feature ideas for a new product in initial discovery from PM, Designer, and Engineer perspectives. Use when starting product discovery for a new product, exploring features for a startup idea, or doing initial ideation.

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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for running model evaluations (use `agent-platform-eval-flywheel` skill).

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Generates Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos on Google Cloud from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.

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This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.

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This skill should be used when the user asks to "triage a vulnerability report", "assess a CVE", "evaluate a bug bounty submission", "decide if a finding is valid", "review a security finding", "dismiss a vulnerability", "should we fix this CVE", "prioritize a vulnerability report", or needs to determine whether an incoming vulnerability report warrants investigation. Applies 7 brocards (rules of thumb) to systematically accept, dismiss, or request more information on vulnerability reports, or needs to filter raw findings from agentic vulnerability discovery pipelines before human review.

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Run a full discovery cycle from problem hypothesis to validated solution. Use when a team needs a structured path through framing, interviews, synthesis, and experiments.

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Plan customer discovery interviews with the right goal, segment, constraints, and method. Use when preparing interviews for problem validation, churn research, or new product ideas.

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Your pathfinder for navigating unknown codebases. Investigates with precision, implements surgically, and never assumes — if it doesn't know, it says so. Maintains a .notebook/ knowledge base that grows across sessions, turning every discovery into lasting intelligence. Summons available skills, MCPs, and docs when the mission demands. Use when fixing bugs, implementing features, refactoring, investigating flows, or any development task in unfamiliar territory. Triggers on "fix this", "implement this", "how does this work", "investigate this flow", "help me with this code". Do NOT use for greenfield scaffolding, CI/CD, or infrastructure provisioning.

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HARD BLOCK — never use for prediction-market/Polymarket UpDown queries; route to okx-dapp-discovery when a named DApp (Polymarket/Aave/Hyperliquid/PancakeSwap/Morpho) appears with a timeframe, or 涨跌/updown for BTC/ETH/SOL/XRP/BNB/DOGE/HYPE. Otherwise, read-only on-chain DEX data, 6 groups: TOKEN (search, hot/热门, liquidity, holders/whale, risk metadata, cluster/持仓集中度, trade history, top traders); MARKET (price/价格, K线/OHLC, index price, wallet PnL/胜率, trade history); SIGNAL (smart money/KOL/whale tracking, buy signals/信号, leaderboard/牛人榜); SOCIAL (news/新闻, sentiment/情绪, token vibe/热度, KOL leaderboard); TRENCHES (pump.fun/meme launches/新盘/扫链, dev reputation, bundle/sniper detection/捆绑狙击者, co-investor — read-only; buy/snipe → okx-dapp-discovery); WS (onchainos ws CLI, or custom WebSocket script/脚本). Also owns Market API payment/x402, quota/额度, and MARKET_API_*_OVER_QUOTA/confirming:true for all 6 groups.

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OKX-aggregated DeFi (no specific DApp named) — product discovery, deposit/withdraw/claim execution, AND positions viewing. **If the user names ANY third-party protocol/DApp (Aave, Lido, PancakeSwap, Uniswap, Curve, Compound, Morpho, Pendle, Kamino, Raydium, Hyperliquid, Polymarket, …), route to okx-dapp-discovery — NOT here, even for 'show my Aave positions'.** INVEST triggers: 'invest in DeFi', 'earn yield', 'find best APY', 'deposit/stake for yield', 'search DeFi products', 'redeem/withdraw position', 'claim DeFi rewards', 'borrow against asset', 'repay loan', 'add/remove CLMM liquidity', 'APY/TVL history', 'depth chart', yield farming, lending, staking, liquidity pools. PORTFOLIO triggers: 'check my DeFi positions', 'view DeFi holdings/portfolio', 'my staking/lending positions', 'DeFi balance', 'DeFi 持仓', '我的DeFi资产'. Do NOT use for: DEX swaps (okx-agentic-wallet), token prices (okx-dex-market), wallet token balances (okx-agentic-wallet).

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Builds Agent-to-Agent (A2A) servers and clients following Google's open protocol for agent interoperability. Use when the user wants to create an A2A-compliant agent, build an Agent Card, implement task management, connect agents across frameworks, set up agent discovery, handle streaming responses, implement push notifications, or orchestrate multi-agent workflows. Trigger words: a2a, agent to agent, agent2agent, a2a protocol, a2a server, a2a client, agent card, agent interoperability, agent collaboration, multi-agent, agent discovery, a2a sdk, a2a task.

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LI.FI REST API for cross-chain and same-chain token swaps, bridging, DeFi deposits (Composer), yield discovery (Earn), and intent-based execution (Intents). USE THIS SKILL WHEN USER WANTS TO: - Swap tokens between different blockchains (e.g., "swap USDC on Ethereum to ETH on Arbitrum") - Bridge tokens to another chain (e.g., "move my ETH from mainnet to Optimism") - Swap tokens on the same chain with best rates (e.g., "swap ETH to USDC on Polygon") - Find the best route or quote for a token swap across chains - Deposit into DeFi vaults/lending/staking in one click, including cross-chain (Composer: Aave, Morpho, Pendle, EtherFi, Yearn, etc.) - Discover yield opportunities, vault APY/TVL data, or track DeFi positions (Earn) - Execute gasless or intent-based transfers via a solver network (LI.FI Intents) - Move stablecoins cheaply with optimized defaults (stablecoin preset) - Build multi-chain payment flows (accept any token, settle in specific token) - Check supported chains, tokens, bridges, or gas prices - Track status of a cross-chain transaction, recover from failed/partial transfers - Query transfer history/analytics or withdraw collected integrator fees - Build backends, bots, or AI agents (any language) that need cross-chain functionality

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Two services in one skill: (1) Web3 on-chain data via Chainbase CLI — use when the user asks about blockchain data, token holders, wallet addresses, token prices, ENS domains, transactions, DeFi portfolios, or any on-chain analytics. Triggers: "top holders of", "who holds", "wallet address", "token price", "token transfers", "ENS domain", "on-chain data", "blockchain query", "SQL query on-chain", across Ethereum, BSC, Polygon, Arbitrum, Optimism, Base, Avalanche, zkSync, and other EVM chains. (2) Crypto social intelligence via Tops (chainbase tops) — use when the user asks about trending crypto narratives, social mentions, Twitter/X crypto discussions, narrative discovery, topic heat, or KOL/community signals. Triggers: "trending topics", "crypto narrative", "what's trending in crypto", "social mentions", "who is talking about", "crypto twitter", "KOL signals", "narrative search", "topic posts".

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This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.

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Compares two or more dotnet new templates side by side to help users choose between them based on parameters, feature support, frameworks, and classifications. USE FOR: deciding between similar templates (webapi vs webapp, blazor vs blazorwasm, console vs worker), producing a side-by-side comparison of parameters and feature support, understanding how templates differ before creating a project. DO NOT USE FOR: creating a project from a template (use template-instantiation), authoring or validating custom templates (use template-authoring and template-validation), general single-template discovery (use template-discovery).

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Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use when you need the local install path of a tool / wordlist / clone for a hunt, or as the full-workflow variant when operating from this local toolkit; for general routing use the bug-bounty skill. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告

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Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.

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Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".

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Discover, analyze, and prioritize keywords for SEO and GEO content strategies from a seed keyword or niche. Identifies high-value opportunities based on search volume, competition, intent, and business relevance. Generates topic clusters. For building a dated editorial calendar from your own Search Console data, use `content-planner` instead — this skill is seed-driven keyword discovery, not calendar scheduling. Use when asked to "find keywords", "keyword research", "keyword analysis", "search volume", "keyword difficulty", "content ideas from a seed", or any keyword discovery task.

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。