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cccskills

「tia」の検索結果

4,064 件 ・ 関連度順

概要と使いどころ

Analyze credential pooling operations and API reseller business models — economics, risks, detection patterns, and sustainability

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。

kevinnft/ai-agent-skills132026年8月1日 更新

Expert-level electrochemistry knowledge. Use when working with electrochemical cells, electrode potentials, Nernst equation, electrolysis, batteries, fuel cells, corrosion, electroplating, or electroanalytical methods. Also use when the user mentions 'galvanic cell', 'electrolytic cell', 'electrode potential', 'Nernst equation', 'overpotential', 'Butler-Volmer', 'battery', 'fuel cell', 'corrosion', 'electroplating', 'cyclic voltammetry', or 'impedance spectroscopy'.

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。

luokai0/ai-agent-skills-by-luo-kai122026年5月6日 更新

Initialize a Git repository with an initial commit

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。

opsmill/nornir-infrahub122026年10月14日 更新

Initialize a Git repository with an initial commit

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。

racecraft-lab/paddock-prototype122026年6月26日 更新

Replace OneCLI gateway with the built-in credential proxy. For users who want simple .env-based credential management without installing OneCLI. Reads API key or OAuth token from .env and injects into container API requests.

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。

nanocoai/nanoclaw-whatsapp102026年4月15日 更新

Replace OneCLI gateway with the built-in credential proxy. For users who want simple .env-based credential management without installing OneCLI. Reads API key or OAuth token from .env and injects into container API requests.

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。

nanocoai/nanoclaw-telegram102026年4月3日 更新

Detect LSASS credential dumping, SAM database extraction, and NTDS.dit theft using Sysmon Event ID 10, Windows Security logs, and SIEM correlation rules

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。

andycungkrinx91/konoha92026年10月9日 更新

Responds to phishing incidents by analyzing reported emails, extracting indicators, assessing credential compromise, quarantining malicious messages across the organization, and remediating affected accounts. Covers email header analysis, URL/attachment sandboxing, and mailbox-wide purge operations. Activates for requests involving phishing response, email incident, credential phishing, spear phishing investigation, or phishing 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。

andycungkrinx91/konoha92026年10月9日 更新

Extract DPAPI-protected secrets such as credentials and browser data offline and online.

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。

andycungkrinx91/konoha92026年10月9日 更新

Initialize a Git repository with an initial commit

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。

juvenal/openrender92026年9月30日 更新

Professional contract review skill for business agreements including NDAs, MSAs, SLAs, SOWs, and software license agreements. Provides systematic clause-by-clause analysis, risk assessment with quantified scoring, red flag detection, and negotiation guidance. Use this skill when reviewing vendor contracts, partnership agreements, service agreements, or any business contract requiring risk evaluation and negotiation preparation. Triggers include "review this contract", "analyze this NDA", "check this agreement for risks", "prepare for contract negotiation", or when evaluating terms and conditions.

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。

takusaotome/claude-skills-library92026年10月5日 更新

Use when the user asks to "check our positioning against what we can actually ship", "trace which differentiators we can defend", or "reconcile the positioning canvas with the claims ledger"; reconciles the reused positioning canvas against the shippable stage and the claims ledger to produce a differentiation truth set — every differentiating claim verifiable or marked '[needs source]' — that TALE-T1 is judged against. Not for building the canvas — use positioning-mapper; not for adjudicating claims — use offer-claims-registry; not for authoring the message house — use message-system-architect. 定位真相/差异化校准/可交付现实/主张核对

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。

unempyd/revenueos72026年9月14日 更新

FlutterSecureStorage (v10.x) for encrypted key-value storage of sensitive data using platform-native secure enclaves with biometric authentication support. Use this skill when storing OAuth tokens (access tokens, refresh tokens, ID tokens), API keys and secrets, user passwords or PINs, session tokens, encryption keys, private keys for cryptography, certificate data, biometric enrollment data, banking credentials, health data, or any sensitive information requiring platform-level encryption (iOS Keychain with Secure Enclave, Android Keystore with Hardware-backed keys). Supports Touch ID, Face ID, and Android biometric prompt integration. Handles biometric change invalidation (resetOnError), data migration from SharedPreferences/EncryptedSharedPreferences, Google Drive backup exclusion, automatic key rotation, configurable encryption algorithms (AES), and prevents data extraction even on rooted/jailbroken devices. Essential for authentication flows, secure credential management, or compliance requirements (PCI DSS, HIPAA, GDPR data protection).

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。

Poorgramer-Zack/dart-expert-skills72026年8月11日 更新

Azure Identity SDK for Rust authentication. Use for DeveloperToolsCredential, ManagedIdentityCredential, ClientSecretCredential, and token-based authentication.

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。

netbarros/psique62026年4月22日 更新

Azure Identity SDK for Rust authentication. Use for DeveloperToolsCredential, ManagedIdentityCredential, ClientSecretCredential, and token-based authentication.

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。

lucaspmarie-a11y/claude-skills-vault52026年6月7日 更新

Detect LSASS credential dumping, SAM database extraction, and NTDS.dit theft using Sysmon Event ID 10, Windows Security logs, and SIEM correlation rules

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。

micsapp/micstec-skills42026年3月20日 更新

Responds to phishing incidents by analyzing reported emails, extracting indicators, assessing credential compromise, quarantining malicious messages across the organization, and remediating affected accounts. Covers email header analysis, URL/attachment sandboxing, and mailbox-wide purge operations. Activates for requests involving phishing response, email incident, credential phishing, spear phishing investigation, or phishing 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。

micsapp/micstec-skills42026年3月20日 更新

Solve ordinary differential equations (initial and boundary value problems). Supports stiff/non-stiff systems, event detection, Hamiltonian/symplectic integration, parameter sweeps, and phase space analysis. Use for any ODE system in physics, engineering, or applied math.

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。

huang-sh/DeepScience42026年7月15日 更新

End-to-end 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-pipeline.

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。

huang-sh/DeepScience42026年7月15日 更新

Orchestrates an end-to-end MeRIP-seq / m6A-seq analysis from raw FASTQ to differential m6A peak calls and metagene plots, chaining fastp adapter trimming, STAR splice-aware alignment (NO deduplication for non-UMI MeRIP), deepTools replicate-concordance + IP-enrichment QC, PreSeq saturation curves, exomePeak2 (transcript-aware Poisson GLM) peak calling, optional MACS3 broad-peak cross-check, DRACH motif confirmation as a sanity check (NOT a per-peak filter), exomePeak2 differential calling via the four-BAM-vector interface (bam_ip + bam_input control; bam_treated_ip + bam_treated_input treatment), ChIPseeker annotation, and the canonical Guitar metagene with stop-codon enrichment as the biological QC anchor. Use when running a complete MeRIP analysis from raw reads, when chaining the constituent epitranscriptomics skills (merip-preprocessing -> m6a-peak-calling -> m6a-differential -> modification-visualization), or when wrapping the pipeline in Snakemake / Nextflow.

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。

huang-sh/DeepScience42026年7月15日 更新

Apply Adobe security best practices for OAuth credentials, secret rotation, I/O Events webhook signature verification, and least-privilege scoping. Use when securing API credentials, implementing webhook validation, or auditing Adobe security configuration. Trigger with phrases like "adobe security", "adobe secrets", "secure adobe", "adobe credential rotation", "adobe webhook signature".

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。

aibot88/sec_skill_store42026年5月27日 更新

This skill should be used when agents need to log into websites, retrieve passwords, or access credentials. CRITICAL - always use find_credential with the website URL, never guess item names.

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。

aibot88/sec_skill_store42026年5月27日 更新

Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.

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。

peacezha/TRACE32026年10月13日 更新

Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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。

peacezha/TRACE32026年10月13日 更新