“signatures”的搜索结果

Automate Esignatures IO operations through Rube MCP using Composio tools. The skill guides an agent through finding available actions, checking the service connection, and supplying the required inputs for individual or bulk tasks.

中文简介正在准备中。当前显示英语简介。

• Finding available Esignatures IO actions
• Checking the connection before running tasks
• Automating repeated operations

Implement electronic signatures compliant with 21 CFR Part 11 Subpart C and EU Annex 11. Covers signature manifestation (signer, date/time, meaning), signature-to-record binding, biometric vs non-biometric controls, policy creation, and user certification requirements. Use when a computerized system requires legally binding electronic signatures for GxP records, when replacing wet-ink signatures in regulated workflows, when implementing batch release or document approval workflows, or when a regulatory gap reveals missing signature controls.

中文简介正在准备中。当前显示原文说明。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

中文简介正在准备中。当前显示原文说明。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

中文简介正在准备中。当前显示原文说明。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

中文简介正在准备中。当前显示原文说明。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

中文简介正在准备中。当前显示原文说明。

Ed25519 is a high-performance digital signature algorithm using the Edwards curve Curve25519. It provides 128-bit security with 64-byte signatures and 32-byte keys, offering significant advantages ove

中文简介正在准备中。当前显示原文说明。

通过Nutrient DWS API转换和加工PDF、Office文档及图像，提取文字与表格，识别扫描文字，并处理敏感信息遮盖、数字签名和表单填写。

• 将Office文档或HTML转为PDF
• 让扫描文档支持文字搜索
• 将PDF表格提取到Excel

Analyze authorized macOS applications and Mach-O binaries, the executable format used on macOS. Examine signatures, dependencies, Objective-C and Swift symbols, and runtime behavior to produce findings tied to specific addresses or symbols.

中文简介正在准备中。当前显示英语简介。

• Examining macOS application signatures and dependencies
• Investigating Objective-C and Swift program structure
• Tracing application behavior with lldb or Frida

Assess software dependencies, build pipelines, and container images for security risks. Create a software component inventory, check vulnerabilities and license conflicts, verify artifact signatures, and prioritize fixes based on whether vulnerable code can actually be reached.

中文简介正在准备中。当前显示英语简介。

• Creating and auditing a software component inventory
• Prioritizing dependency vulnerabilities by actual exposure
• Auditing CI/CD pipelines and secret handling

Trace how a web page generates request signatures and encrypted parameters by inspecting network requests and JavaScript execution. Collect runtime evidence, then use it to reproduce the behavior locally in Node.js.

中文简介正在准备中。当前显示英语简介。

• Tracing how request signatures are generated
• Finding the code that triggers a network request
• Capturing function inputs and outputs

Assess authorized single sign-on systems using SAML, OIDC, and OAuth2. Map the login flow, check signatures, redirects, and token validation, then document reproducible findings and their impact, including identity or tenant confusion.

中文简介正在准备中。当前显示英语简介。

• Review redirect and token checks in a single sign-on flow
• Assess SAML signatures and assertion handling
• Investigate tenant confusion and logout failures

Create rules for Suricata and Snort, systems that detect suspicious network traffic, to flag port scans and reconnaissance. Combine packet patterns with connection counts, tune alert volume, and summarize scanning sources, targets, and activity.

中文简介正在准备中。当前显示英语简介。

• Create rules to detect Nmap and Masscan activity
• Monitor scanning within an internal network
• Tune alerts for authorized vulnerability scanners

Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.

中文简介正在准备中。当前显示英语简介。

• Screening third-party data before training
• Checking downloaded model weights before deployment
• Investigating unexpected behavior on specific inputs

Investigate attacks that make legitimate Windows applications load malicious DLL files. This skill examines loading events, digital signatures, file hashes, and unusual locations, then checks process behavior to document suspicious cases and guide remediation.

中文简介正在准备中。当前显示英语简介。

• Investigating alerts about unsigned DLL files
• Hunting for persistent threats using legitimate applications
• Checking suspicious DLL loading paths

Investigate Entra ID reconnaissance by searching Graph API logs for tool signatures and unusual directory queries. Use KQL, a log query language, to correlate suspicious requests with sign-ins and turn useful searches into Sentinel detection rules.

中文简介正在准备中。当前显示英语简介。

• Investigating Graph API activity after suspected credential theft
• Checking whether offensive tool activity is visible in logs
• Creating and tuning scheduled Sentinel detection rules

Analyze Cobalt Strike communication profiles and Beacon payload configurations to extract request paths, headers, timing, and process injection settings. Turn these indicators into a JSON report and network detection rules for Suricata or Snort.

中文简介正在准备中。当前显示英语简介。

• Analyzing a captured Malleable C2 profile
• Extracting communication settings from a Beacon payload
• Creating Suricata or Snort detection rules

Search automatically generated code documentation to check function parameters, class definitions, API details, and comments before making changes. Find relevant documents by meaning, exact keywords, or document type, then retrieve their full contents.

中文简介正在准备中。当前显示英语简介。

• Check function parameters before implementation
• Find documentation about authentication
• List class and component documentation

Checks published OpenWork desktop downloads for mac-arm64, verifying developer signatures, Apple notarization, and update metadata. It checks versions, download references, and file hashes, then records evidence to support a release rollout decision.

中文简介正在准备中。当前显示英语简介。

• Checking signatures and notarization on published downloads
• Verifying update versions, asset references, and hashes
• Recording release verification evidence in a PR or issue

Examine writing for familiar AI patterns, statistical features, Pangram detection scores, and hidden character signatures. Compare drafts against known human writing to interpret the evidence, with explanations and limitations rather than a definitive authorship claim.

中文简介正在准备中。当前显示英语简介。

• Check a post for common AI writing patterns
• Compare AI detection scores across drafts
• Compare a revision with known human writing

Plan encryption, TLS connections, digital signatures, and key management using established cryptographic libraries. Get guidance on choosing algorithms, storing passwords, rotating keys, and avoiding implementation mistakes such as reused nonces or comparisons that reveal information through timing.

中文简介正在准备中。当前显示英语简介。

• Choosing encryption methods for stored data
• Reviewing TLS settings for encrypted connections
• Designing password storage and token signatures

Finds Lark contacts by name or email and retrieves open_id, an identifier used in later actions. It also looks up profile details, personal status, and signatures, and searches for bots or agents visible to the current user.

中文简介正在准备中。当前显示英语简介。

• Identifying a colleague before messaging or scheduling
• Looking up a profile from a known open_id
• Checking colleagues’ personal status and signatures

Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.

中文简介正在准备中。当前显示原文说明。

Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.

中文简介正在准备中。当前显示原文说明。