「exit」の検索結果

Use SonarJS-derived rules in .NET repositories that ship JavaScript or TypeScript frontends and need deeper bug-risk, code-smell, or cognitive-complexity checks than a minimal ESLint baseline. Use when the repo wants `eslint-plugin-sonarjs` locally or already runs SonarQube or SonarCloud in CI.

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。

Builds probability-weighted valuation models with multiple exit scenarios, timing assumptions, and risk-adjusted returns. Use when building growth equity valuations, modeling scenario-weighted outcomes, or analyzing risk-adjusted returns.

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。

Builds fund economic models with sensitivity across deployment pace, exit multiples, and fee/carry structures for LP and GP returns. Use when modeling fund economics, projecting LP net returns, or analyzing fee-adjusted performance.

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。

Constructs venture return models with entry valuation, follow-on reserve, multiple scenario exits, and portfolio-level fund math. Use when modeling VC returns, calculating fund economics, or projecting portfolio outcomes.

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。

Constructs LBO models with sources/uses, debt schedules, operating projections, and returns analysis across entry/exit scenarios. Use when modeling leveraged buyouts, calculating sponsor returns, or analyzing leverage capacity.

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。

Evaluates cross-border transaction complexities including tax treaties, currency, regulatory approvals, and cultural factors. Use when structuring international deals, assessing cross-border risk, or navigating multi-jurisdiction closings.

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。

6-phase multi-agent pipeline with approval gates. Orchestrates Plan→Design→Implement→Review→Test→Deploy phases with explicit entry/exit criteria, phase-level agent assignment, and human-in-the-loop approval checkpoints.

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。

Living catalogue of 7 known hazard entries (SE-01 through SE-07) specific to agent-studio: Windows backslash paths, prototype pollution, hook exit codes, async swallowing, ReDoS in glob-to-regex, DST arithmetic, array mutation during iteration. Each entry: symptom, root cause, fix, test assertion.

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。

Static code analysis and complexity metrics

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。

Simplify cluttered UI by removing unnecessary elements, choices, features, and structural complexity. Use when the problem is too many controls, options, features, or competing elements—not when the structure is sound and the real issue is only visual intensity or lack of personality.

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 after EZ_math_model coding has produced src/*.py and before packaging when code quality, reuse, vectorization, naming, or unnecessary complexity should be reviewed without changing behavior.

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。

Check whether the knowledge base shelf-index is stale relative to the library files. Reports drifted entries without rebuilding. Designed to be invoked from environment validation or pre-push hooks as an opt-in check. Default mode is warning; strict mode fails with non-zero exit when any drift is detected.

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。

Systematically gather comprehensive travel itinerary requirements through structured discovery questions, MCP-powered research (Perplexity, Exa), and expert detail gathering. Use when users request travel planning, itinerary creation, or trip assistance requiring deep research and personalized requirements gathering. Outputs detailed requirements specification with day-by-day itineraries.

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。

Source pre-2016 cinema with gothic/occult sensibility, literary texture, and historical grandeur — build watchlists, curate Criterion/MUBI picks. Uses Perplexity for film discourse, Exa for web search, Firecrawl for scraping. Triggers on film recommendations, watchlist, gothic cinema, occult film, arthouse, pre-2016 movies.

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。

Audits websites for AI search visibility (GEO/SEO) and generates deployable artifacts (llms.txt, schema JSON-LD, robots.txt). 7-dimension scorecard tailored by site type and size, grounded in Princeton KDD 2024, Toronto 2025, and ICLR 2026 research. This skill should be used when optimizing for ChatGPT, Perplexity, Google AI Overviews, or other AI search engines. Also triggered by queries about AI search optimization, generative engine optimization, llms.txt, AI crawler configuration, schema markup for AI, citation optimization, or Google AI Mode.

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。

Plan and record evidence-backed LLM and AI-search citation checks across approved assistant/search surfaces with timestamped queries, locales, modes, cited URLs, snippets, screenshots or transcripts, and caveats. Use this skill when the user asks whether ChatGPT, Perplexity, Claude, Gemini, Copilot, or another assistant cites a target site, but do not claim citation without direct evidence.

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 an engineering plan needs pressure-testing for architecture, complexity, data, risk, and operability.

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 get found in Google or AI engines (ChatGPT, Perplexity, AI Overviews) — keyword and content strategy, technical or content SEO audits, schema markup, link building, topical clustering, E-E-A-T, AI citation (AEO), or deciding whether SEO is even the right channel for their startup. Calibrated for early-stage, low-authority sites. Not for paid acquisition or channel-mix/CAC analysis (use channel-expert), or writing the page copy itself (use copywriter-skill).

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。

Evolve an existing skill by refining its content in-place or creating an advanced variant. Covers assessing the current skill, gathering evolution requirements, choosing scope (refinement vs. variant), applying changes, updating version metadata, and synchronizing the registry and cross-references. Use when a skill's procedure steps are outdated, user feedback reveals gaps, a skill needs a complexity upgrade, an advanced variant is needed alongside the original, or related skills are added and cross-references are stale.

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。

Statically enforce a privacy/redaction boundary on an artifact tree before publication, the way an AST checker enforces a code contract. Builds a two-tier gate — a shape-based deny-list (regex over identifier shapes) plus a structure-aware tier that parses JSON/HTML/SVG/Markdown/code and asserts sensitive content cannot appear in meaningful positions — with a strict exit-code contract, label-only output that never echoes the patterns themselves, and false-positive narrowing instead of suppressions. Use when gating a public mirror, a disclosure draft, a wire-capture export, or any artifact that must pass a leak check before it ships, and when a plain line-grep is too coarse to trust.

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。

Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medchem query language (MATCHRULE, HASALERT, HASPROP, HASGROUP) for filtering a library at scale. Also trigger on medchem, `import medchem as mc`, RuleFilters, NIBRFilters, CommonAlertsFilters, NamedCatalogs, QueryFilter, PAINS filtering, or structural alerts.

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 studying Intel VT-x or AMD-V internals: VMCS and VMCB, EPT and NPT, VM exit handling, APIC virtualization, or building a minimal type-1 hypervisor. Not for running VMs: use qemu-kvm.

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 tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.

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。

Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.

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。