「chaining」の検索結果

cat Skill: Derivational Pipe Chaining

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。

Design, review, and improve agent workflows & agent using SSOT, SRP, Fail Fast principles. Supports Prompt Chaining, Parallelization, Orchestrator-Workers patterns.

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。

Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.

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。

Discover, parse, and call external HTTP APIs at runtime using OpenAPI specs, tool templates, and iterative chaining. Adapted from UTCP (Universal Tool Calling Protocol) patterns for Node.js and Claude Code agents.

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。

Hunt full Account Takeover (ATO) by chaining lower-severity findings — IDOR-to-email-disclosure, password-reset-token-theft, OAuth-redirect-uri abuse, response-manipulation, cookie scope, CRLF in Set-Cookie, race-condition on signup. Use when the user wants to escalate small findings to ATO or thinks an ATO path exists.

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。

Use when writing Cortex-M NVIC ISRs, configuring priorities, handling HardFault, tail-chaining, or measuring interrupt latency. Not for the vector table itself: use baremetal-startup.

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。

Use when the user wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

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。

Deep OAuth 2.0 / OpenID Connect security assessment. Covers the full attack surface: redirect_uri validation bypass (path traversal, open redirect chains, subdomain confusion, URL parsing tricks, parameter pollution, response mode switching), missing/broken state parameter CSRF, PKCE downgrade and absent-challenge attacks, implicit grant token leakage (Referer, browser history, XSS fragment theft), authorization code injection, scope escalation, client confusion attacks, mutable-claims account takeover (iss+sub confusion, email-keyed identity merging), pre-account takeover via unverified registration, OpenID Connect dynamic client registration SSRF (logo_uri, jwks_uri, sector_identifier_uri, request_uri), nonce replay, ID token validation failures, Device Code phishing flow, mobile custom URI scheme hijacking, refresh token persistence, token introspection enumeration, consent screen clickjacking, host header injection, and cross-IdP mix-up attacks. Uses KOAuth, jwt_tool, nuclei oauth templates, and manual http(action="request", ...) payloads. Every technique includes actual request payloads and verification logic. Chains from /pentester or /api-security when OAuth endpoints are discovered. Chains into /web-exploit for open redirect, XSS, and SSRF chaining. Chains into /post-exploit when full account takeover is achieved. Chains into /credential-audit when access tokens or refresh tokens are recovered.

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。

Use when hunting Client-Side Path Traversal (CSPT) vulnerabilities where attacker- controlled input is unsafely concatenated into the path component of a JavaScript fetch() or XHR request. Trigger on: "CSPT", "client-side path traversal", "fetch path traversal", "XHR path injection", "fetch concatenation", "../ in fetch", "user input in fetch URL", "path component injection", fetch redirect chaining, CSPT to XSS, open redirect fetch, "JavaScript fetch user input", DOM fetch injection.

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。

Identify and exploit Cross-Site Request Forgery (CSRF) vulnerabilities by bypassing weak or flawed anti-CSRF token implementations, SameSite cookie attributes, and Origin/Referer headers. Use this skill when testing state-changing web application endpoints for session riding attacks. Covers token removal, token fixation, multipart manipulation, and chaining with XSS for complete bypass.

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。

Exploit Server-Side Request Forgery (SSRF) vulnerabilities to extract AWS IAM credentials from the Instance Metadata Service version 2 (IMDSv2). This skill details how to bypass the token requirement of IMDSv2 by chaining HTTP verbs (PUT then GET) if the SSRF vulnerability allows full control over the request headers and methods.

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 AI agent systems for tool abuse, unauthorized actions, privilege escalation through tool chaining, and safety bypass via agentic workflows. Use this skill when assessing autonomous AI agents that use tool-calling (function calling, plugins, actions) to interact with external systems. Covers multi-step attack chains, implicit trust exploitation, and capability boundary testing for AI agents.

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。

Audit deployment readiness across CI/CD, containers, monitoring, IaC, secrets, CDN, and DNS — score each area 0-100, identify critical gaps, and optionally auto-fix by chaining deploy sub-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。

Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.

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。

Declarative animation patterns using flutter_animate v4.5.x with composable effects (fade, scale, slide, shimmer, shake, blur), GLSL fragment shaders, and scroll-synchronized animations. Use this skill for adding UI animations and transitions, building staggered or sequenced effects, creating custom animation effects, fixing animation jank or performance issues, implementing shimmer loading states, or adding scroll-driven animations. Supports delays, curves, adaptive effects, and easy extension method chaining (.animate()).

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。

Designs event-driven architectures, webhook systems, API chaining flows, ETL pipelines, and integration patterns between services.

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。

Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.

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。

LLM Agent engineering: pattern selection (12-mode matrix), data simulation, convergence iteration. Covers intent routing, function calling, ReAct, MCP, prompt chaining, guardrails, evaluation.

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。

Master orchestrator for the 5-step productivity system. Runs the full audit-architect-analyst-refinery-compounder loop or individual steps. Use when: (1) "run the full loop", "start the system", "architect system", (2) "run step N", "run audit", "run architect", (3) "what step am I on?", "system status", "where am I in the loop?", (4) "continue", "resume", "next step". Manages state, sequencing, and skill chaining across all 5 steps.

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。

Master orchestrator for the 5-step productivity loop. Runs audit, architect, analyst, refinery, and compounder in sequence or individually. Use when you want to run the full loop, start the system, check system status, resume from where you left off, or run a specific step. Manages state, sequencing, and skill chaining. Supports full loop, single step, resume, and status-only modes.

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。

End-to-end shotgun metagenomics workflow from FASTQ to taxonomic and functional profiles, orchestrating controls/host depletion, Kraken2+Bracken classification, MetaPhlAn marker profiling, and HUMAnN functional profiling. Covers the controls-first ordering, why Kraken2 read counts are not abundances and MetaPhlAn cell fractions do not equal Bracken read fractions, and the consistent-pipeline framing. Use when profiling shotgun metagenomic samples end to end, or chaining classification, abundance, and function. For resistome see metagenomics/amr-detection; for strains see metagenomics/strain-tracking; for assembly see genome-assembly/metagenome-assembly.

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。

Orchestrates the end-to-end germline short-variant pipeline from FASTQ to a filtered, normalized, benchmarked VCF, chaining QC/trim, BWA-MEM2 alignment, duplicate marking, optional BQSR, calling (bcftools/GATK HaplotypeCaller/DeepVariant/DRAGEN), normalization, site+genotype filtering, annotation, and hap.py/vcfeval benchmarking. Use when deciding the pipeline-wide reference-genome commitment (GRCh38 analysis set vs T2T, ALT/decoy handling), sequencing the steps in the defensible order (normalize BEFORE annotate, filter site- then genotype-level), choosing the calling engine and single-sample vs cohort joint-calling, picking a filtering strategy by cohort size, or benchmarking stratified within GIAB confident regions. Hands off mechanism to the variant-calling and read-alignment component skills; not a re-teach of any single step.

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。

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。

Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.

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。