“verification”的搜索结果

Zero-knowledge proofs and privacy patterns on Stellar. Covers Groth16 verification in smart contracts via BLS12-381 host functions (CAP-0059, available), the BN254 + Poseidon proposals (CAP-0074/0075, status-sensitive), and concrete toolchain walkthroughs for Circom (on-chain verifiable today), Noir, and RISC Zero (attestation pattern until BN254 lands). Use when building privacy-preserving applications, ZK-verifier contracts, or wiring a proving toolchain to Stellar.

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Pre-PR verification for VenTu — tests, data validation, static build, and e2e for the touched surface. Use before opening any PR. For visual UI checks also load ventu-verify-ui.

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Ralph - persistent self-referential execution loop wrapping ultrawork with independent verifier verification

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Review changes for correctness, security, accessibility, and performance. Use for scoped quality reviews or verification plans; bug fixes use oma-debug.

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Automated CLI-based parallel agent execution — spawn subagents via Gemini CLI, coordinate through MCP Memory, monitor progress, and run verification

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Design AI content detection systems for authenticity verification. TRIGGERS - Use when user needs help with ai-content-detector related tasks.

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Write, review, or audit this repository's technical documentation, including README.md, CONTRIBUTING.md, root and scoped AGENTS.md files, docs pages, and documentation drift. Use for source-backed documentation authoring, behavior verification, and cross-surface consistency. Do not use for code review, deslop diff cleanup, or whole-page rewrites, splits, or reorganizations handled by docs-refactor.

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Run Unity Test Runner and report detailed results. Use for EditMode/PlayMode tests, change verification, or failure diagnosis.

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Keep GPT-5.6 and other coding-agent work proportional to the request by locking scope, choosing the smallest sufficient implementation, matching verification to risk, and stopping when success is proven. Use for implementations, bug fixes, refactors, reviews, or maintenance where scope creep, speculative abstractions, unrelated cleanup, repeated test runs, unnecessary dependencies, or polishing beyond the request are risks.

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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.

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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

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Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

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Run the refactor workflow — mandatory architectural justification, test verification, risk-based implementation, and scope enforcement.

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Genera tickets profesionales de soporte para Meta Business Suite / WhatsApp Business API optimizados para recibir prioridad URGENTE 24hr resolution. Úsala SIEMPRE que el usuario mencione crear un ticket a Meta, escalar un caso WABA, apelar un ban o restricción, reportar un false positive, abrir caso de soporte WhatsApp Business, problema de onboarding, rechazo de template, errores de Cloud API, OTP delivery, verification stuck, BM restricted, display name rejection, o cualquier issue de una WhatsApp Business Account. También activa cuando el usuario dice frases como "ayúdame a redactar un caso para Meta", "necesito reportar esto a soporte", "hay que escalar esto a WhatsApp", "ticket para Meta", o describe un problema de una cuenta de cliente de Vambe sin pedir ticket explícitamente pero claramente necesita uno. También activa ante preguntas del tipo "¿cómo apelar esto?" o "¿qué hago si Meta dice X?" relacionadas con WABA, BM o verificación.

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Azure Key Vault Keys SDK for .NET. Client library for managing cryptographic keys in Azure Key Vault and Managed HSM. Use for key creation, rotation, encryption, decryption, signing, and verification.

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Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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Review a task implementation against approved specs, task boundaries, and verification evidence. Use after an implementer finishes a task, after remediation, or before accepting a task as complete.

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Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

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Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.

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End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.

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Deploy your own Open-Inspect instance. Use when the user wants to set up, deploy, or onboard to Open-Inspect. Guides through repository setup, credential collection, Terraform deployment, and verification with user handoffs.

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Step through versioning, tagging, and verification

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Independent, traceable code review of one SDD Composy task in review_required against its approved PRD, stories, TechSpec, evidence, and project rules, with stable findings and severities. Use when a task passed QA and evidence — 'revisar a TASK-003', 'code review da task', 'review this task's diff'. Do NOT use for code review outside an SDD workspace (use the host code review), for QA (sdd-qa), or for verification (sdd-verify).

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