「elf」の検索結果

Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly. Use this to run an analysis, review one before acting on it, or work out why two people looking at the same data reached opposite conclusions.

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 the help center, in-product guidance, and knowledge base that let customers resolve problems without contacting anyone — content, findability, maintenance, and deflection measurement. Use this to build or fix a help center, reduce support volume, write documentation for customers, improve findability, or decide what deserves a help article versus a product fix.

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。

Handles customer situations that have exceeded normal support — severity assessment, incident communication, executive escalation, and recovering a relationship after a failure. Use this when a customer issue is escalating or has gone to leadership, during a customer-affecting outage, when a major account is at risk, when a relationship needs repairing after a failure, or to design the escalation path itself.

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。

Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.

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。

Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.

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。

Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'

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。

Baidu FaMou algorithm skills for efficient algorithm self-evolution. Provides experiment management and visualization capabilities to help optimize complex algorithms. Use when user needs algorithm optimization or experiment management.

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。

Create professional presentations using the Pyramid Principle methodology. Supports PPTX generation, Marp/Reveal.js Markdown slides, chart creation, speaker notes, and self-evaluation rubrics. Minimal intake form to rapid output workflow.

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。

Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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。

Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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。

Generates complete tumor immune-infiltration-guided bulk-transcriptome diagnostic biomarker and machine-learning research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, immune infiltration estimation, immune-linked module discovery, consensus feature selection, diagnostic modeling, nomogram construction, clinical association, and optional prognostic extension or validation. Covers five study patterns (immune-cell-first diagnostic workflow, immune-module-to-biomarker workflow, consensus-ML biomarker workflow, diagnostic-plus-prognostic hybrid workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, reference literature pack, and self-critical risk review.

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。

Verifies whether a scientific or biomedical claim is actually supported by the cited original papers rather than by citation drift, overstatement, selective citation, or correlation-to-causation inflation. Use this skill whenever a user wants to check whether a repeated statement, slide claim, manuscript sentence, review assertion, or “people often say” scientific conclusion is truly supported by the underlying primary literature. Always separate the claim itself, the cited paper(s), what the paper actually showed, what it did not show, and whether later retellings drifted beyond the original evidence. Never fabricate references, findings, study features, or citation chains.

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。

Guides Metronome usage-based billing integration decisions — event ingestion (single and batch, idempotency, billable metrics), contract design (rate cards, overrides, dimensional pricing, products), invoicing lifecycle (grace periods, finalization, Stripe sync), credit and commit management (prepaid, postpaid, thresholds, auto-recharge), and Stripe integration (arrears invoicing, tax providers, line item limits). Use when building, modifying, or reviewing any Metronome integration — including ingesting usage events, creating customers, contracts, or rate cards, designing billable metric group keys, managing credits and commits, configuring invoicing, syncing invoices with Stripe Billing, billing AI applications for token usage, setting up self-serve (PLG) pricing, reviewing customer health, commit burn, portfolios, or renewals, or migrating from Stripe Billing Meters to Metronome.

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。

Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'

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。

Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.

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。

Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.

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。

SkillsBench task authoring — walk a contributor from idea to submission-ready task following CONTRIBUTING.md and the task-implementation rubric. Use when the user wants to create a new SkillsBench task, scaffold a task from an existing workflow (notebook, Excel workbook, document, dataset), convert a prompt or a benchmark item into a SkillsBench task, write skills for a task, or prepare a SkillsBench PR. Pairs with `task-review` (run that as a self-check before submitting).

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。

Local self-check of instructions and mask outputs (format/range/consistency) without using GT.

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。

Reference for multi-hop / triangulated currency conversion on batch rate tapes. When a rate row carries an explicit "via" currency, the row's rate is only one leg of the conversion, and the via currency may ITSELF be quoted through another via — so the effective rate is the product of every leg, resolved by walking the chain until a direct quote is reached. Useful when an FX feed lists some pairs directly to the reporting currency and others as triangulated (or doubly-triangulated) quotes through vehicle currencies.

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。

Autonomously engage with complex coding tasks — multi-step planning, tool use, self-correction, and solution verification without human intervention.

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。

Context priming before work (bd prime) and self-reflection after completion to extract patterns, gotchas, and decisions into the knowledge base.

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。

Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.

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 tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed 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。

Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification

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。