Results for “hash”

Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.

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Swift Standard Library・Concurrency・Observation リファレンス。 プロトコル/ジェネリクス、async/await、actor、SerialExecutor、Sendable、Task、TaskGroup、AsyncSequence、AsyncStream、 Continuation (CheckedContinuation)、Observable、@Observable、Observations、ObservationRegistrar、 Span、InlineArray、RangeSet、Equatable、Hashable、Comparable、Identifiable、Optional、Codable、Result、generics、some/any。

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Scan, structure, validate, and submit your project to the Next Level Builders Directory (https://nextlevelbuilder.io) with 17 rich blocks, cryptographic content hashing, Polar payment handling, and automated moderation queueing.

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Choose and review cryptographic usage in your codebase: TLS, hashing, KMS, secret storage. Prefer standard libraries and vetted constructions.

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Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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Generate a single-file industrial-archive product landing page (Nordic museum aesthetic, hash-routed detail pages). Takes a natural-language description of one or more "exhibit-style" products (objects, tools, hardware, watches, kitchenware, lighting, audio, instruments, anything with a designed/forged/restrained quality); outputs ONE working .html file. Visitors see a black-framed cover that flips between exhibits, sidebar index, side meta column (material/origin/year/dimension), and click any exhibit → full detail page (giant italic title, spec table, hero image, lead + 3 prose paragraphs, gallery, prev/next). Runs in BOTH Claude Code (writes to ~/Desktop) and GLM / online chat (outputs HTML inline). Trigger: "做个工业风产品落地页", "做克制之物风格的展品页", "industrial-archive-gen ...", or when user drags this skill folder into chat asking for a product page in the Nordic-industrial archive style.

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Generate a single-file Chinese atelier-style portfolio website (horizontal-row list + hover-reveal thumb interaction, art lishu typography "Alimama DaoLiTi" via CDN, light/dark theme, hash-routed detail pages). Takes a natural-language description of the studio (name, director, discipline, works) and outputs ONE working .html file. Runs in BOTH Claude Code (writes to ~/Desktop) and GLM / online chat (outputs HTML inline). Trigger phrases include "做个中式工作室作品集", "做个隶书作品集", "lishu-portfolio-gen ...", "做个白屋风格的作品集", or when user drags this skill folder into chat asking for an atelier-style site.

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Deterministic external-evidence web validation workflow for Sanguo ETL/RAG. USE FOR: 外部證據網站檢查、來源健康度、allowlist 驗證、來源 seed 覆蓋、3kweb-check、external evidence source health、fetch/parse/hash first、LLM reviewer second。

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Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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Verify every visual-evidence artifact (screenshot/GIF/recording) attached to a PR carries a provenance manifest binding it to real daemon-backed truth — daemon port, run id, transcript head hash, agent node id, commit, and an honest real/fixture/mock source label — and that operator-control-panel PRs cover the required set of proof states. Use when gating a PR's proof artifacts before merge, auditing whether a "LIVE" visual claim is backed by a real daemon run versus a reused or fixture-backed mock, or defining the required state-coverage set for a control-panel change. NOT for the capture technique itself — headless Playwright, `screencapture -x -l`, non-interruptive capture (use port-daddy-agent-skill's visual-evidence doctrine), designing the receipt body schema an artifact attaches to (use agent-work-receipt-designer), or the broader dogfood/Potemkin product-quality bar (use multi-agent-authoring-product-bar).

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This skill should be used when the user asks to "最小再現環境を作って", "再現環境を構築して", "問題を切り分けたい", "パッケージの不具合か確認したい", "create a minimal reproduction", "isolate the issue", "build a repro environment", "reproduce this bug", "narrow down the cause", "is this a package bug or my code", "このPRの問題を再現して", "this commit broke something", or wants to determine whether a bug is caused by a package, the environment, or application code. Accepts PR URLs, commit hashes, or branch names as input.

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SHA-256コンテンツハッシュを使用して高コストなファイル処理結果をキャッシュ — パス非依存、自動無効化、サービスレイヤー分離。

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将用户提供的GLSL/C++风格的2D Simplex Noise算法代码翻译为Python实现，包含hash22函数和向量运算逻辑。

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Selects crypto algorithms and usage. Hashing, AES-GCM, RSA, ECDSA, PQC key derivation.

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Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.

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Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

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Perform DCSync attacks to replicate Active Directory credentials and establish domain persistence by extracting KRBTGT, Domain Admin, and service account hashes for Golden Ticket creation.

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Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache investigation.

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Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

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Implements secure authentication patterns including login/registration, session management, JWT tokens, password hashing, cookie settings, and CSRF protection. Provides auth routes, middleware, security configurations, and threat model documentation. Use when building "authentication", "login system", "JWT auth", or "session management".

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WPA/WPA2-PSK attack methodology — four-way handshake capture via targeted deauthentication, PMKID attacks (no client required), hcxdumptool / hcxpcapngtool conversion to hashcat hc22000 format, GPU-accelerated cracking with dictionary, mask, and rule-based attacks, vendor default-PSK generators (UPC, Sky, BT, etc.), 802.11r FT key cracking, opportunistic key cache analysis, and signal-level optimization. Use when the in-scope network is WPA/WPA2 Personal — the most common consumer/SMB encryption mode.

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WPA3 / SAE (Simultaneous Authentication of Equals) attack methodology — transition-mode (mixed WPA2/WPA3) downgrade, Dragonblood side-channel attacks (CVE-2019-9494, 9495, 13377, 13456), SAE auth flooding for AP CPU exhaustion, Hash-to-Element (H2E) timing analysis, group downgrade, and 6 GHz / Wi-Fi 6E spec implications (PMF mandatory, no transition mode allowed). Use when target advertises WPA3-SAE or WPA3-Personal/Enterprise, or operates in 6 GHz where WPA3 + PMF are required by spec.

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IDOR (Insecure Direct Object Reference) testing checklist: object ID enumeration, horizontal/vertical privilege escalation, GUID predictability, indirect references via hashes, chained IDOR, and API endpoint IDOR. Use for web app pentests and bug bounty IDOR discovery.

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Penetration test and red team report writing methodology. Covers executive summary structuring (risk-led narrative for non-technical readers), technical finding format (title, severity, affected scope, narrative, reproduction steps, impact, remediation, references), CVSS v3.1 / v4.0 scoring with vector justification, OWASP risk rating, evidence hygiene (redacting credentials, hashing client data, time-stamping every action), screenshot and PoC artifact management, finding chain narratives, scope/limitations/assumptions documentation, retest evidence and remediation tracking, deliverable formats (PDF, DOCX, HTML, JSON for SIEM ingestion), client-customer-deliverable separation, and common report mistakes (over-CVSSing, undermining the triager, missing the 'so what'). Use at the end of an engagement when authoring a deliverable, when restructuring a draft for executive readability, or when establishing a reusable report template for a consulting practice.

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