「its」の検索結果

Draft the QA scope and plain-language Given/When/Then test scenarios for a Jira ticket, built from the full ticket, the decisions made in its comments, and the actual code change read commit by commit (open PR, the ticket's merged PR, the branch against its base, or a named commit range). Saves a Markdown draft under docs/, iterates with the user, then posts it to the ticket as a new Jira comment only after explicit approval. Use on "QA scope for PROJ-123", "what should QA test", "write test scenarios for QA", "hand this ticket to QA", "prep the QA handoff", "gherkin scenarios for this ticket", or when a branch is ready for manual testing — even when the user only names the ticket or says "this branch".

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。

Turn the git commits for a Jira ticket into a short, business-friendly implementation report and save it as local markdown. Use when the user wants to report finished work on a Jira ticket: a Jira report, update, or comment, a summary of their commits or changes for the ticket, or a note telling PM/QA what shipped.

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。

Resolve CodeRabbit review feedback on a GitHub pull request. Collects every open CodeRabbit finding (inline threads, plus the nitpick and outside-diff findings packed into review bodies), judges and fixes each one against the real code with subagents, unifies fixes that share a pattern, commits locally, asks before pushing, then shows every reply draft linked to its finding and posts only on approval. Runs only on top-tier models. Use on "resolve the CodeRabbit feedback", "address coderabbit comments", "answer the rabbit", "fix what CodeRabbit flagged on

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Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

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AI Agent skill marketplace integration for A2A Market. Enables agents to buy skills, sell skills, and earn money autonomously. Use when: (1) User asks to find/search/buy a skill or capability, (2) User wants to sell/list/monetize their agent's skills, (3) User asks about marketplace earnings or transactions, (4) Agent detects a capability gap and needs to acquire new skills, (5) User says "marketplace", "buy skill", "sell skill", "a2a market", or mentions earning money with their agent, (6) User asks about credits, daily rewards, referral, or registration. Supports x402 USDC payments on Base L2 and Credits payment system.

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Drafts a Certificate of Withdrawal for a foreign corporation surrendering its authority to transact business in a US state. Use when a foreign corporation is withdrawing its certificate of authority, ceasing in-state operations, or completing dissolution in a non-home jurisdiction.

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。

Stay ahead of EPA enforcement at cement plants. Free CementOps Compliance Suite skill. NESHAP Subpart LLL limits, CEMS QA/QC, Title V permits, alternative fuels emissions, exceedance response, and NOV defense procedures.

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Extracts technical API constraints from OpenAPI/Swagger specs and developer docs into a contract-ready API Access & Constraints Schedule with source traceability, risk flags, and change-control language. Use when drafting legal exhibits or schedules covering API access scope, rate limits, authentication, data fields, or deprecation terms for MSAs, SOWs, or order forms.

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。

Delegate implementation work to a dedicated implementer subagent, then review its diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants a separate model to write code, mentions handing off / delegating implementation, asks to work through a plan or spec unit-by-unit with a subagent doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer self-reports success, must not run the full test suite by default, must not touch git, and its report is a claim rather than evidence. Cursor port of implementation-loop. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the cursor-engineering-mode skill (it ships in this same plugin).

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。

Internal utility for the skills library itself — opt-in, local-first, privacy-respecting usage telemetry and update checks. Provides version checking, usage logging, and usage reporting for the skills in this repo. Use only when asked to view skill usage stats, configure telemetry opt-in/out, wire a skill to log its own runs, or check for skill updates.

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。

Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

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Use when writing, reviewing, or deploying Solidity smart contracts — token contracts, signature-gated claim systems, merkle-gated staking vaults, allocation modules, role-based proxies, or factory patterns. Triggers on edits to `*.sol` files, `hardhat.config.*`, `foundry.toml`, deploy scripts, or mentions of "smart contract", "Solidity", "Web3", "EVM", "Hardhat", "Foundry", "ERC20/721/1155", "merkle", "staking", or "on-chain". For security audits and adversarial review see security-engineering.

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。

Assess and manage Azure service quotas and limits to prevent deployment and scaling failures. Use this skill when users ask about quota errors, capacity planning, regional availability, or resource limits.

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。

Expert in Git workflows, conventions, and patterns for modern development teams and AI agents. Activate on: branching strategy, commit conventions, git hooks, pre-commit, husky, lint-staged, trunk-based development, git worktree, monorepo git, PR review workflow, conventional commits, git workflow. NOT for: CI/CD pipelines (use github-actions-pipeline-builder), repository hosting (use devops-automator), multi-agent orchestration (use multi-agent-coordination).

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。

Map the most influential Reddit venues for a brand's AI visibility using Xpoz. Finds which subreddits and threads AI engines actually cite when answering buyer questions, where those questions get asked, and which live threads are worth answering. Use when asked to "get cited by AI through Reddit", "which subreddits matter for my brand", "Reddit influence map", "where should we engage on Reddit", or "why does ChatGPT cite Reddit for my category".

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。

Makes the agent reconsider a task it just handed back to the human and find a way to do it itself, checking memory, CLI tools, skills, MCP servers, and documentation in turn. Use when the user invokes `/you` or `$you`, or replies that the agent should do something itself rather than asking them.

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Implements one numbered sub-task from a task list at .ai/[feature]/tasks.md, running linters and tests, without staging, committing, or marking the task complete. Use when asked to carry out a specific sub-task by number, or as the inner step of a larger loop that handles commits separately. For the full loop including commits, use task-orchestration.

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Structures git branches and worktrees: keeps unrelated changes on separate branches, isolates each in its own worktree created so the repository's setup runs, records dependencies with git-machete, and recombines them into a temporary mixdown branch for local testing. Use before creating any branch or worktree in a codebase that works on more than one branch, including branching off the default branch to start a change; when starting work in a fork, clone, or third-party checkout; when choosing what a new branch should be based on; when a branch is growing a second unrelated concern; when deciding whether to stack branches or keep them independent; when preparing a change for a pull request or patch submission; when handed an existing worktree or branch to implement and commit in a fork; or when a local build needs several in-progress branches combined. Not needed where the repository clearly commits everything to the trunk, or holds notes or personal data rather than code.

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Rebases every local branch of a fork onto a moved upstream, flags the ones upstream has superseded, and rebuilds the mixdown branch that combines them. Use after fetching a fork's upstream and finding it has advanced far, been force-pushed, or rewritten its history; when several stacked or sibling branches all need bringing forward at once; when a branch was cut from a mixdown and now carries merge commits it should not have; or when asked to rebase all branches, re-mix, or bring a whole worktree set up to date.

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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).

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。

Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.

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Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

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Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

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Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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。