「chain」の検索結果

Think and work like an expert Astroparticle Physicist. Use when a task calls for Astroparticle Physicist judgment. Reasons from flux times cross section times acceptance, Poisson counting over structured backgrounds, and Cherenkov photoelectron budgets through SkyLLH unbinned likelihoods, Geant4/CORSIKA chains validated on through-going-muon and calibration samples, and Feldman-Cousins/CLs limits, while treating atmospheric-neutrino and downgoing-muon contamination, ER/NR leakage and the neutrino fog, and look-elsewhere trials as first-class failure modes.

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Think and work like an expert Actuarial Scientist. Use when a task calls for Actuarial Scientist judgment. Reasons from mortality tables (qx, period/cohort, select/ultimate) and Chain-Ladder/Mack reserving through GLM/GAM frequency–severity and Tweedie pricing, limited-fluctuation and Bühlhmann-Straub credibility, Solvency II SCR standard formula, and IFRS 17 CSM/RA while treating triangle truncation, overfitting, and tail risk as first-class failure modes.

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Authentication coercion (PetitPotam MS-EFSR, PrinterBug MS-RPRN, DFSCoerce MS-DFSNM) chained into NTLM relay (impacket ntlmrelayx) toward LDAP, AD CS web enrollment (ESC8), or SMB. Use when SMB signing is not enforced or LDAP channel binding is missing, and you want to force a privileged machine account to authenticate to your relay and turn that into RBCD, a DCSync-capable ACL grant, or a certificate. Includes the exact Coercer/ntlmrelayx commands, requirements, detection, and remediation.

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。

Competitive positioning, strategic group mapping, differentiation analysis, competitive advantage assessment, market positioning map, value chain positioning, brand positioning, cost leadership vs differentiation, niche strategy analysis, disruptive positioning

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。

Resolves rsIDs, navigates RsMergeArch/SNPHistory merge chains, and converts between rsID, SPDI, HGVS, and VCF representations using the dbSNP Build 156 JSON architecture. Use when normalizing variant identifiers, joining variant databases by cluster ID, or tracking deprecated rsIDs through historical merges.

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Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.

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。

Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Proteina-Complexa" as the BioClaw-facing skill label. For sequence design after backbone generation, use proteinmpnn or solublempnn. For QC thresholds, use protein-design-qc.

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All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.

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Fetch and analyze protein structures from RCSB PDB. Use this skill when: (1) Need to download a structure by PDB ID, (2) Search for similar structures, (3) Prepare target for binder design, (4) Extract specific chains or domains, (5) Get structure metadata. For sequence lookup, use uniprot. For binder design workflow, use binder-design.

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Internal — for Boundless team members only. Query the Boundless Indexer REST API for on-chain market data, staking, PoVW rewards, delegations, and market efficiency on prod/staging environments. Use when the user asks about proof requests, provers, requestors, staking data, PoVW rewards, delegations, market aggregates, leaderboards, or wants to fetch data from the indexer API on live networks. Also use when the user mentions "indexer" and wants to look up addresses, requests, or market statistics on deployed environments. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.

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Sequenced launcher that bootstraps QA on a project with no QA in place. Chains qa-project-context → test-strategy → test-planning in one guided run, then points you at automation. Use when: "set up QA on a new project," "QA from scratch," "no QA exists yet," "/qa-start." Not for: onboarding a QA engineer to an existing team with existing tests — use qa-project-bootstrap. Not for: "which skill do I use" with no clear match — use qa-do. Related: qa-project-context, test-strategy, test-planning, qa-project-bootstrap, qa-do.

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。

Test application security against OWASP Top 10 (2025) with automated CI tooling: OWASP ZAP (DAST), dependency/supply-chain scanning (OSV-Scanner, SBOM, provenance), Semgrep SAST, auth/session tests (JWT, OAuth, RBAC), and XSS/CSRF/SQLi/SSRF Playwright patterns. Use when: "security test," "OWASP," "vulnerability," "ZAP," "XSS," "SSRF," "dependency scan," "auth testing," "OWASP LLM Top 10." Scope is automated scanning + negative-path security tests in CI, not manual penetration testing. Not for: mapping security controls to regulations (SOC 2, HIPAA, PCI, GDPR) — use compliance-testing; pipeline stage wiring and deploy gating mechanics — use ci-cd-integration; purely functional API auth/input tests with no attacker model — see api-testing; testing your product's own LLM features or defending the agent itself (prompt-injection detector, indirect injection, jailbreak red-teaming) — use ai-system-testing. Related: ci-cd-integration, compliance-testing, api-testing, shift-left-testing, ai-system-testing.

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Validates gate criteria before PRD lifecycle advancement by delegating to the readiness scoring pipeline (scripts/readiness.py). Returns a graduated PASS / WARN / BLOCK verdict with top blockers and their causal chain. Triggers before advancing from v0.X to v0.Y or explicit `/ghm-gate-check`.

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Orchestrate go-to-market strategy by running positioning, offer construction, and channel allocation as a sequenced workflow during PRD v0.9 Go-to-Market. Triggers on requests to plan launch, define GTM strategy, or when user asks "how do we launch?", "go-to-market", "launch plan", "marketing strategy", "GTM", "launch this product". Chains three framework-grounded sub-skills (Dunford → Hormozi → ORB) and reconciles their outputs.

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张一鸣（字节跳动/TikTok创始人）的思维框架与表达方式。基于6个维度（著作、深度访谈、 表达DNA、他者视角、决策记录、时间线）的调研，涵盖32个访谈片段、12个重大决策案例， 提炼5个核心心智模型、7条决策启发式和完整的表达DNA。 用途：作为思维顾问，用张一鸣的视角分析产品、组织、全球化、人才和个人成长问题。 当用户提到「用张一鸣的视角」「张一鸣会怎么看」「一鸣的思路」「zhang yiming perspective」时使用。 即使用户只是说「帮我用张一鸣的角度想想」「如果是字节会怎么做」「切换到张一鸣」也应触发。 即使用户说「字节怎么看」「头条的逻辑」「一鸣怎么选择」「一鸣」也应触发。

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This skill should be used when the user says "/ultrawork", "ultrawork", or wants to run the full specify → execute pipeline automatically with a single command. Automated end-to-end workflow that chains specify and execute skills.

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。

E2B is a cloud platform that gives AI agents secure, isolated sandboxes — ephemeral Linux VMs with a filesystem, package managers, and internet access — to run AI-generated code safely. Use when a user wants an agent to execute Python or JavaScript, install packages, read/write files, or run a long process without risking the host machine, or wants to wire code execution into a LangChain, CrewAI, or custom agent loop.

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DSPy is a Python framework from Stanford NLP that replaces hand-written prompts with code: you declare LLM tasks as typed signatures, compose them into modules, and let optimizers (BootstrapFewShot, MIPROv2, GEPA) tune the prompts and few-shot examples against your metric. Use when the user asks about DSPy, signatures, ChainOfThought, ReAct, teleprompters or optimizers, or wants to stop hand-tuning prompts.

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。

ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.

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You are an expert in Biome, the Rust-based toolchain that replaces ESLint and Prettier with a single, fast tool. You help developers configure linting, formatting, and import sorting for JavaScript, TypeScript, JSX, JSON, and CSS — achieving 100x faster execution than ESLint+Prettier with zero configuration, unified diagnostics, and IDE integration.

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。

Animates 3D objects and characters in Blender with Python (bpy): keyframes, armatures and IK rigs, shape keys, F-Curves, the NLA editor and drivers. Use when the user wants to keyframe properties, create armatures and rigs, set up IK/FK chains, animate shape keys for facial animation, edit F-Curves, blend actions with NLA strips, add drivers, or script any animation workflow in Blender from the terminal.

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。

Arize is an AI observability platform, and Phoenix is its open-source tracing and evaluation tool for LLM applications. Use this skill when the user wants to trace OpenAI or LangChain calls, run a local Phoenix server, score RAG answers for faithfulness, debug slow or wrong LLM responses, or send OpenTelemetry traces to Arize for production monitoring.

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Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.

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3proxy is a small open-source proxy server that runs HTTP/HTTPS, SOCKS4/5, SNI and TCP/UDP port-mapping proxies from one config file. Use when a user asks to set up an HTTP or SOCKS5 proxy, add proxy users and passwords, write 3proxy access rules, chain or rotate upstream (parent) proxies, limit bandwidth, connections or monthly traffic per user, run 3proxy in Docker, or fix a 3proxy.cfg that will not start.

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。